---
name: linear
description: |
  Linear API integration with managed OAuth. Query and manage issues, projects, teams, cycles, and labels using GraphQL.
  Use this skill when users want to create, update, or query Linear issues, search for tasks, manage projects, or track work.
  For other third party apps, use the api-gateway skill (https://clawhub.ai/byungkyu/api-gateway).
  Requires network access and valid Maton API key.
metadata:
  author: maton
  version: "1.0"
  clawdbot:
    emoji: 🧠
    requires:
      env:
        - MATON_API_KEY
---

# Linear

Access the Linear API with managed OAuth authentication. Query and manage issues, projects, teams, cycles, labels, and comments using GraphQL.

## Quick Start

```bash
# Get current user
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'query': '{ viewer { id name email } }'}).encode()
req = urllib.request.Request('https://gateway.maton.ai/linear/graphql', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

## Base URL

```
https://gateway.maton.ai/linear/graphql
```

All requests use POST to the GraphQL endpoint. The gateway proxies requests to `api.linear.app` and automatically injects your OAuth token.

## Authentication

All requests require the Maton API key in the Authorization header:

```
Authorization: Bearer $MATON_API_KEY
```

**Environment Variable:** Set your API key as `MATON_API_KEY`:

```bash
export MATON_API_KEY="YOUR_API_KEY"
```

### Getting Your API Key

1. Sign in or create an account at [maton.ai](https://maton.ai)
2. Go to [maton.ai/settings](https://maton.ai/settings)
3. Copy your API key

## Connection Management

Manage your Linear OAuth connections at `https://ctrl.maton.ai`.

### List Connections

```bash
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections?app=linear&status=ACTIVE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

### Create Connection

```bash
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'app': 'linear'}).encode()
req = urllib.request.Request('https://ctrl.maton.ai/connections', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

### Get Connection

```bash
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

**Response:**
```json
{
  "connection": {
    "connection_id": "fda4dabb-9d62-47e3-9503-a2f29d0995df",
    "status": "ACTIVE",
    "creation_time": "2026-02-04T23:03:22.676001Z",
    "last_updated_time": "2026-02-04T23:03:51.239577Z",
    "url": "https://connect.maton.ai/?session_token=...",
    "app": "linear",
    "metadata": {}
  }
}
```

Open the returned `url` in a browser to complete OAuth authorization.

### Delete Connection

```bash
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections/{connection_id}', method='DELETE')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

### Specifying Connection

If you have multiple Linear connections, specify which one to use with the `Maton-Connection` header:

```bash
python <<'EOF'
import urllib.request, os, json
data = json.dumps({'query': '{ viewer { id name } }'}).encode()
req = urllib.request.Request('https://gateway.maton.ai/linear/graphql', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
req.add_header('Maton-Connection', 'fda4dabb-9d62-47e3-9503-a2f29d0995df')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

If omitted, the gateway uses the default (oldest) active connection.

## API Reference

Linear uses a GraphQL API. All operations are sent as POST requests with a JSON body containing the `query` field.

### Viewer (Current User)

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ viewer { id name email } }"}
```

**Response:**
```json
{
  "data": {
    "viewer": {
      "id": "4933b394-c42f-4623-904f-355fc40a4858",
      "name": "Byungkyu Park",
      "email": "byungkyujpark@gmail.com"
    }
  }
}
```

### Organization

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ organization { id name urlKey } }"}
```

### Teams

#### List Teams

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ teams { nodes { id name key } } }"}
```

**Response:**
```json
{
  "data": {
    "teams": {
      "nodes": [
        {
          "id": "70c49a0d-6973-4563-a743-8504f1a5171b",
          "name": "Maton",
          "key": "MTN"
        }
      ]
    }
  }
}
```

#### Get Team

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ team(id: \"TEAM_ID\") { id name key issues { nodes { id identifier title } } } }"}
```

### Issues

#### List Issues

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ issues(first: 10) { nodes { id identifier title state { name } priority createdAt } pageInfo { hasNextPage endCursor } } }"}
```

**Response:**
```json
{
  "data": {
    "issues": {
      "nodes": [
        {
          "id": "565e2ee9-2552-48d8-bbf9-a8b79ca1baec",
          "identifier": "MTN-527",
          "title": "Shopify app verification",
          "state": { "name": "In Progress" },
          "priority": 0,
          "createdAt": "2026-02-03T07:49:31.675Z"
        }
      ],
      "pageInfo": {
        "hasNextPage": true,
        "endCursor": "4c7b33c8-dabf-47ce-9d30-7f286f9463be"
      }
    }
  }
}
```

#### Get Issue by ID or Identifier

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ issue(id: \"MTN-527\") { id identifier title description state { name } priority assignee { name } team { key name } createdAt updatedAt } }"}
```

#### Filter Issues

Filter by state type:

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ issues(first: 10, filter: { state: { type: { eq: \"started\" } } }) { nodes { id identifier title state { name type } } } }"}
```

Filter by title:

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ issues(first: 10, filter: { title: { containsIgnoreCase: \"bug\" } }) { nodes { id identifier title } } }"}
```

#### Search Issues

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ searchIssues(first: 10, term: \"shopify\") { nodes { id identifier title } } }"}
```

#### Create Issue

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "mutation { issueCreate(input: { teamId: \"TEAM_ID\", title: \"New issue title\", description: \"Issue description\" }) { success issue { id identifier title state { name } } } }"}
```

**Response:**
```json
{
  "data": {
    "issueCreate": {
      "success": true,
      "issue": {
        "id": "9dff693f-27d2-4656-9b2d-baa4a828dc83",
        "identifier": "MTN-528",
        "title": "New issue title",
        "state": { "name": "Backlog" }
      }
    }
  }
}
```

#### Update Issue

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "mutation { issueUpdate(id: \"ISSUE_ID\", input: { title: \"Updated title\", priority: 2 }) { success issue { id identifier title priority } } }"}
```

### Projects

#### List Projects

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ projects(first: 10) { nodes { id name state createdAt } } }"}
```

### Cycles

#### List Cycles

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ cycles(first: 10) { nodes { id name number startsAt endsAt } } }"}
```

### Labels

#### List Labels

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ issueLabels(first: 20) { nodes { id name color } } }"}
```

**Response:**
```json
{
  "data": {
    "issueLabels": {
      "nodes": [
        { "id": "510edbdf-9f6e-43a0-80e5-c3b3bd82e26f", "name": "Blocked", "color": "#eb5757" },
        { "id": "cb7a7ef2-d2d3-4da2-ad4e-7cea0f8a72c7", "name": "Feature", "color": "#BB87FC" },
        { "id": "c795d04c-24d2-4d20-b3c1-9f9f1ce7b017", "name": "Improvement", "color": "#4EA7FC" },
        { "id": "40ff69f9-4a93-40a2-b143-f3b94aa594b7", "name": "Bug", "color": "#EB5757" }
      ]
    }
  }
}
```

### Workflow States

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ workflowStates(first: 20) { nodes { id name type team { key } } } }"}
```

**Response:**
```json
{
  "data": {
    "workflowStates": {
      "nodes": [
        { "id": "f21dfa65-7951-4742-a202-00ceb0ff6e9f", "name": "Backlog", "type": "backlog", "team": { "key": "MTN" } },
        { "id": "1ab9475f-eb91-4207-a5a3-1176e38b85be", "name": "Todo", "type": "unstarted", "team": { "key": "MTN" } },
        { "id": "ee724a62-0212-4b53-af67-08297a5ae132", "name": "In Progress", "type": "started", "team": { "key": "MTN" } },
        { "id": "427a9916-3849-4303-b982-f00f1d79c5ee", "name": "Done", "type": "completed", "team": { "key": "MTN" } },
        { "id": "363df32a-f22d-4083-8efb-b3615c019925", "name": "Canceled", "type": "canceled", "team": { "key": "MTN" } }
      ]
    }
  }
}
```

### Users

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ users(first: 20) { nodes { id name email active } } }"}
```

### Comments

#### List Comments

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "{ comments(first: 10) { nodes { id body createdAt issue { identifier } user { name } } } }"}
```

#### Create Comment

```bash
POST /linear/graphql
Content-Type: application/json

{"query": "mutation { commentCreate(input: { issueId: \"ISSUE_ID\", body: \"Comment text here\" }) { success comment { id body } } }"}
```

## Pagination

Linear uses Relay-style cursor-based pagination with `first/after` and `last/before` arguments.

```bash
# First page
POST /linear/graphql
{"query": "{ issues(first: 10) { nodes { id identifier title } pageInfo { hasNextPage endCursor } } }"}

# Next page using endCursor
POST /linear/graphql
{"query": "{ issues(first: 10, after: \"CURSOR_VALUE\") { nodes { id identifier title } pageInfo { hasNextPage endCursor } } }"}
```

Response includes `pageInfo`:

```json
{
  "data": {
    "issues": {
      "nodes": [...],
      "pageInfo": {
        "hasNextPage": true,
        "endCursor": "4c7b33c8-dabf-47ce-9d30-7f286f9463be"
      }
    }
  }
}
```

## Code Examples

### JavaScript

```javascript
const response = await fetch('https://gateway.maton.ai/linear/graphql', {
  method: 'POST',
  headers: {
    'Authorization': `Bearer ${process.env.MATON_API_KEY}`,
    'Content-Type': 'application/json'
  },
  body: JSON.stringify({
    query: `{ issues(first: 10) { nodes { id identifier title state { name } } } }`
  })
});
const data = await response.json();
```

### Python

```python
import os
import requests

response = requests.post(
    'https://gateway.maton.ai/linear/graphql',
    headers={
        'Authorization': f'Bearer {os.environ["MATON_API_KEY"]}',
        'Content-Type': 'application/json'
    },
    json={
        'query': '{ issues(first: 10) { nodes { id identifier title state { name } } } }'
    }
)
data = response.json()
```

## Notes

- Linear uses GraphQL exclusively (no REST API)
- Issue identifiers like `MTN-527` can be used in place of UUIDs for the `id` parameter
- Priority values: 0 = No priority, 1 = Urgent, 2 = High, 3 = Medium, 4 = Low
- Workflow state types: `backlog`, `unstarted`, `started`, `completed`, `canceled`
- The GraphQL schema is introspectable at `https://api.linear.app/graphql`
- Use `searchIssues(term: "...")` for full-text search across issues
- Some mutations (delete, create labels/projects) may require additional OAuth scopes. If you receive a scope error, contact Maton support at support@maton.ai with the specific operations/APIs you need and your use-case

## Error Handling

| Status | Meaning |
|--------|---------|
| 400 | Missing Linear connection or GraphQL validation error |
| 401 | Invalid or missing Maton API key |
| 403 | Insufficient OAuth scope for the operation |
| 429 | Rate limited |
| 4xx/5xx | Passthrough error from Linear API |

GraphQL errors are returned in the `errors` array:

```json
{
  "errors": [
    {
      "message": "Invalid scope: `write` required",
      "extensions": {
        "type": "forbidden",
        "code": "FORBIDDEN",
        "statusCode": 403
      }
    }
  ]
}
```

### Troubleshooting: API Key Issues

1. Check that the `MATON_API_KEY` environment variable is set:

```bash
echo $MATON_API_KEY
```

2. Verify the API key is valid by listing connections:

```bash
python <<'EOF'
import urllib.request, os, json
req = urllib.request.Request('https://ctrl.maton.ai/connections')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
print(json.dumps(json.load(urllib.request.urlopen(req)), indent=2))
EOF
```

### Troubleshooting: Invalid App Name

1. Ensure your URL path starts with `linear`. For example:

- Correct: `https://gateway.maton.ai/linear/graphql`
- Incorrect: `https://gateway.maton.ai/graphql`

## Resources

- [Linear API Overview](https://linear.app/developers)
- [Linear GraphQL Getting Started](https://linear.app/developers/graphql)
- [Linear GraphQL Schema (Apollo Studio)](https://studio.apollographql.com/public/Linear-API/schema/reference?variant=current)
- [Linear API and Webhooks](https://linear.app/docs/api-and-webhooks)
- [Maton Community](https://discord.com/invite/dBfFAcefs2)
- [Maton Support](mailto:support@maton.ai)


I have the Linear skill details. The PER team is the Personal team (`d51635fd-4b92-476f-80d8-2b79f7c7cf3c`). Let me create a backlog issue for the Tesla setup task.

exec

doppler run --project markbot_personal --config dev -- python3 << 'EOF'
import urllib.request, os, json

query = """
mutation {
  issueCreate(input: {
    teamId: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c",
    title: "Set up Tesla integration with MarkBot",
    description: "MarkBot has the tesla-api skill installed. Need to do one-time OAuth auth flow:\\n\\n1. Open Terminal on Mac\\n2. Run: `TESLA_EMAIL=\\"your@email.com\\" python3 ~/.openclaw-markbot/workspace/skills/tesla-api/scripts/tesla.py auth`\\n3. Follow the browser OAuth flow\\n4. Paste callback URL back\\n\\nOnce done, MarkBot can: start/warm up car, lock/unlock, check charge, get location, honk, flash lights.\\n\\nTesla account email needed.",
    priority: 3
  }) {
    success
    issue { identifier title }
  }
}
"""

data = json.dumps({'query': query}).encode()
req = urllib.request.Request('https://gateway.maton.ai/linear/graphql', data=data, method='POST')
req.add_header('Authorization', f'Bearer {os.environ["MATON_API_KEY"]}')
req.add_header('Content-Type', 'application/json')
result = json.load(urllib.request.urlopen(req))
print(json.dumps(result, indent=2))
EOF


Traceback (most recent call last):
  File "<stdin>", line 21, in <module>
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 187, in urlopen
    return opener.open(url, data, timeout)
           ~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 493, in open
    response = meth(req, response)
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 602, in http_response
    response = self.parent.error(
        'http', request, response, code, msg, hdrs)
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 531, in error
    return self._call_chain(*args)
           ~~~~~~~~~~~~~~~~^^^^^^^
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 464, in _call_chain
    result = func(*args)
  File "/opt/homebrew/Cellar/python@3.14/3.14.3_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/urllib/request.py", line 611, in http_error_default
    raise HTTPError(req.full_url, code, msg, hdrs, fp)
urllib.error.HTTPError: HTTP Error 401: Unauthorized

(Command exited with code 1)


401 - the MATON_API_KEY might not be in Doppler or has a different name. Let me use the direct LINEAR_API_KEY approach from MEMORY.md which says we use `LINEAR_API_KEY` directly.

doppler run --project markbot_personal --config dev -- python3 << 'EOF'
import urllib.request, os, json

query = """
mutation {
  issueCreate(input: {
    teamId: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c",
    title: "Set up Tesla integration with MarkBot",
    description: "MarkBot has the tesla-api skill installed. One-time OAuth auth needed at computer.\\n\\nSteps:\\n1. Have Tesla account email ready\\n2. Tell MarkBot: set up Tesla\\n3. Follow browser OAuth flow, paste callback URL\\n\\nCapabilities once set up: warm up car, lock/unlock, charge status, location, honk/flash.",
    priority: 3
  }) {
    success
    issue { identifier title }
  }
}
"""

data = json.dumps({'query': query}).encode()
req = urllib.request.Request('https://api.linear.app/graphql', data=data, method='POST')
req.add_header('Authorization', os.environ["LINEAR_API_KEY"])
req.add_header('Content-Type', 'application/json')
result = json.load(urllib.request.urlopen(req))
print(json.dumps(result, indent=2))
EOF


Set up Tesla integration with MarkBot

Set up Tesla integration with MarkBot


Created as PER-28 in Linear. Let me let Mark know.

Done — **PER-28** in your Personal backlog: *"Set up Tesla integration with MarkBot."*

When you're at your computer, just tell me "set up Tesla" and I'll walk you through the 2-minute auth flow. Everything's already installed and ready.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 16:16 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=afternoon; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

[Queued messages while agent was busy]

---
Queued #1
Conversation info (untrusted metadata):
```json
{
  "message_id": "506",
  "sender": "687577942"
}
```

Alisa’s a plan and put in backlog to do what needs to be done to pay bills. National grid, Eversource, etc. and to make me therapy appointments at a place with no api to do so.


Mark wants me to create a plan and put it in Linear backlog for:
1. Paying bills (National Grid, Eversource, etc.) — likely using browser automation
2. Making therapy appointments at a place with no API — also browser automation

These are browser automation tasks that need to be set up. Let me create a good plan and add the appropriate Linear tickets.

For bill payment and therapy appointments without APIs, the approach is:
- Browser automation (agent-browser with the markbot Chrome profile)
- Mark needs to pre-authenticate in the markbot Chrome profile

Let me think about what's actually needed:
1. **Bill payment**: National Grid and Eversource — these are utility companies. Need to:
   - Log into their websites in the markbot Chrome profile
   - Set up browser automation to pay bills on demand
   - Could also set up auto-pay directly on those sites

2. **Therapy appointments**: No API — need browser automation to book appointments. This could be:
   - A specific therapy provider's portal (e.g., SimplePractice, TherapyNotes, etc.)
   - Manual login + form filling via browser automation

For both: the prerequisite is Mark logging into those sites in the markbot browser profile. Then I can automate from there.

Now I'll create Linear tickets for this work, starting with the bill pay automation setup.

The core plan is straightforward: Mark logs into National Grid, Eversource, and other bill sites once in the markbot profile, then I can detect upcoming bills and handle payments through browser automation. For therapy appointments, Mark provides the provider details and I automate the booking process. Let me get these tickets created.

On it — creating the tickets with a proper action plan for each:

doppler run --project markbot_personal --config dev -- python3 << 'EOF'
import urllib.request, os, json

issues = [
    {
        "title": "Set up automated bill pay (National Grid, Eversource, etc.)",
        "description": """Set up MarkBot to pay utility bills on demand via browser automation.

## Plan

### Step 1 — One-time setup (Mark at computer)
Log into each bill site in the markbot browser profile (persistent cookies):
- National Grid
- Eversource
- Any other recurring utilities (water, internet, etc.)

Run: `agent-browser open https://www.nationalgridus.com --profile markbot`
And same for Eversource.

### Step 2 — MarkBot capability
Once logged in, MarkBot can:
- Navigate to the payment page
- Check current balance / due date
- Submit payment via saved payment method

### Step 3 — Proactive monitoring (optional)
Add to HEARTBEAT.md: check for upcoming bills weekly, alert Mark when due within 7 days.

## What Mark needs to do
- Sit at computer with browser
- Tell MarkBot "set up bill pay"
- Log into each utility site in the markbot browser profile
- Confirm saved payment method is on file at each site

## Sites to cover
- National Grid
- Eversource
- Add others as needed""",
        "priority": 3
    },
    {
        "title": "Set up therapy appointment booking via browser automation",
        "description": """Set up MarkBot to book therapy appointments via browser automation (no API available).

## Plan

### Step 1 — Info needed from Mark
- URL of the therapy booking portal
- Login credentials (add to Doppler as THERAPY_USER / THERAPY_PASS, or use saved browser session)
- Preferred therapist name / provider
- Typical appointment length and preferred days/times

### Step 2 — One-time setup (Mark at computer)
Log into the therapy portal in the markbot browser profile:
`agent-browser open <portal-url> --profile markbot`

### Step 3 — MarkBot capability
Once authenticated, Mark can say:
- "Book me a therapy appointment next week"
- MarkBot navigates portal, finds available slot matching preferences, books it, confirms

## What Mark needs to do
1. Share the therapy portal URL
2. Log in once in markbot browser profile
3. Share preferred days/times for appointments

## Note
This works with SimplePractice, TherapyNotes, ZocDoc, or any web-based booking portal.""",
        "priority": 3
    }
]

results = []
for issue in issues:
    query = f"""
    mutation {{
      issueCreate(input: {{
        teamId: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c",
        title: {json.dumps(issue['title'])},
        description: {json.dumps(issue['description'])},
        priority: {issue['priority']}
      }}) {{
        success
        issue {{ identifier title }}
      }}
    }}
    """
    data = json.dumps({'query': query}).encode()
    req = urllib.request.Request('https://api.linear.app/graphql', data=data, method='POST')
    req.add_header('Authorization', os.environ["LINEAR_API_KEY"])
    req.add_header('Content-Type', 'application/json')
    result = json.load(urllib.request.urlopen(req))
    issue_data = result['data']['issueCreate']['issue']
    results.append(issue_data)
    print(f"{issue_data['identifier']}: {issue_data['title']}")

EOF


PER-29: Set up automated bill pay (National Grid, Eversource, etc.)
PER-30: Set up therapy appointment booking via browser automation


Created both tickets. Let me give Mark a summary.

Both in the Personal backlog:

**PER-29** — Automated bill pay (National Grid, Eversource, etc.)
**PER-30** — Therapy appointment booking

Both follow the same pattern when you're ready:
1. Sit at computer, tell me "set up bill pay" or "set up therapy booking"
2. I open those sites in the markbot browser profile — you log in once
3. From then on I can pay bills or book appointments on your say-so from anywhere, including your phone

For therapy specifically — what's the portal? (SimplePractice, ZocDoc, their own site?) Drop the URL when you're ready and I'll have the full flow mapped out.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 16:18 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=afternoon; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

Pre-compaction memory flush. Store durable memories now (use memory/2026-02-22.md; create memory/ if needed). IMPORTANT: If the file already exists, APPEND new content only and do not overwrite existing entries. If nothing to store, reply with NO_REPLY.
Current time: Sunday, February 22nd, 2026 — 4:18 PM (America/New_York)


Time to flush memory before compaction. Let me write today's key events to the daily memory file. I need to check if the file already exists and append if so.

cat /Users/mark/.openclaw-markbot/workspace/memory/2026-02-22.md 2>/dev/null && echo "FILE_EXISTS" || echo "FILE_NOT_FOUND"


File doesn't exist. Let me create it with today's events.

write

# Daily Log — Sunday, February 22, 2026

## LifeOS Project (big day)

### Deployment
- Deployed LifeOS Streamlit app to Forge server (64.225.48.84) at `https://lifeos.forgeco.dev`
- Running as Docker container (`lifeos`) on `forge_server_default` network behind Caddy HTTPS proxy
- Caddy auto-issued Let's Encrypt SSL cert for `lifeos.forgeco.dev`
- Source volume-mounted so deploys = rsync + `docker restart lifeos` (no rebuild needed)
- Systemd service removed in favor of Docker Compose

### Bugs Fixed
- `StreamlitDuplicateElementKey` crash: `dismiss_review_modal_` prefix added at line 788 of `plugins/daily_log/ui.py`
- Caddy basicauth removed (broke WebSocket/Streamlit repeated auth prompts)
- Deployment pipeline fixed: source code now volume-mounted into container

### Users
- Me: `87503f93-b329-458b-8344-d449721b2692`
- Krista: `6f26a647-e657-4b71-9392-e552079ec068` (was "Partner" — renamed in DB, config, and code)

### Coding Agent (claude-max) — FIXED
- Root issue: was using `--print` flag which only generates text, doesn't execute tools
- Correct invocation: `claude-max --dangerously-skip-permissions "task"` (NO --print)
- Dialog navigation: spawn → wait 8s → `send-keys ["Down"]` → confirm "Yes, I accept" selected → `send-keys ["Enter"]`
- Agent uses Opus 4.6 on Max subscription (no API cost)
- Confirmed working: created hello.txt, ran ls, said TASK_COMPLETE

### Mobile UI + V2 Module Audit (coding agent)
- Agent completed: `.streamlit/config.toml` created (dark theme), `ui.py` mobile polish, `tools.py` cleanup
- All v2 lifestyle modules (Stimulants, Drugs, Relationship, Content Consumption, Intimacy) audited
- App running clean at `https://lifeos.forgeco.dev`

### Decisions
- Staying with Streamlit for now, port to React+FastAPI later when the app is "perfect"
- Not ready for LLM insights layer yet — want more data first
- Sleep import deferred (need to decide Oura vs Whoop)

## New Skills Installed
- `tesla-api` — installed, needs one-time OAuth auth at computer (PER-28)

## Linear Tickets Created
- PER-28: Set up Tesla integration with MarkBot
- PER-29: Set up automated bill pay (National Grid, Eversource)
- PER-30: Set up therapy appointment booking via browser automation

## Model Strategy Locked In
- Default: Sonnet, no reasoning
- Step up: Opus, no reasoning (for complex analysis)
- Heavy: Opus + reasoning (hard problems only)
- Coding: claude-max sub-agent (free on Max subscription)
- I auto-switch using session_status tool — Mark doesn't need to manage this

## Emails Flagged Today
- Owen Krebs Grimsich: Cash flow follow-up (mark@forgeco.com) — tagged "To Respond"
- Jonathan Weinstock: Service visit reply
- Newo AI meeting: tomorrow (Monday Feb 23)
- Alex Bemis: Project delay due to snow emergency
- DO maintenance notice: Core Infrastructure Maintenance in All Regions

## Misc
- Notion has "Movies to see with my kids": Aladdin, Gladiator, Brain Donors, Dark Knight
- Mark asked for more movie recommendations — ages of kids not confirmed yet
- Therapy portal URL not yet shared (needed for PER-30)


Successfully wrote 3063 bytes to /Users/mark/.openclaw-markbot/workspace/memory/2026-02-22.md


Also update MEMORY.md with key long-term learnings from today that should persist beyond the daily log.

Also capturing the claude-max fix in long-term memory since it's critical:

edit

## LifeOS Project (as of 2026-02-22)
- **URL**: `https://lifeos.forgeco.dev` — deployed, running clean
- **Server**: `forge@64.225.48.84:/home/forge/lifeos/` — Docker container `lifeos`
- **Local source**: `/Users/mark/LifeDataOS/personal-os/`
- **Deploy**: `rsync -avz --exclude='.venv' --exclude='__pycache__' --exclude='*.pyc' --exclude='personal.db' --exclude='.git' /Users/mark/LifeDataOS/personal-os/ forge@64.225.48.84:/home/forge/lifeos/` then `ssh forge@64.225.48.84 'docker restart lifeos'`
- **Users**: Me (`87503f93`) and Krista (`6f26a647`) — Krista was "Partner", renamed everywhere
- **Status**: Mobile UI polished, v2 modules audited, running clean. LLM insights layer deferred.
- **Decision**: Stay Streamlit, port to React+FastAPI when "perfect"

## Coding Agents (as of 2026-02-22)


Successfully replaced text in /Users/mark/.openclaw-markbot/workspace/MEMORY.md.

     ...
  54 - Has existing integrations: BuildOps, ConnectTeam, HCP
  55 - Runs both tech and HVAC operations
  56 - Values efficiency and automation
  57
+ 58 ## LifeOS Project (as of 2026-02-22)
+ 59 - **URL**: `https://lifeos.forgeco.dev` — deployed, running clean
+ 60 - **Server**: `forge@64.225.48.84:/home/forge/lifeos/` — Docker container `lifeos`
+ 61 - **Local source**: `/Users/mark/LifeDataOS/personal-os/`
+ 62 - **Deploy**: `rsync -avz --exclude='.venv' --exclude='__pycache__' --exclude='*.pyc' --exclude='personal.db' --exclude='.git' /Users/mark/LifeDataOS/personal-os/ forge@64.225.48.84:/home/forge/lifeos/` then `ssh forge@64.225.48.84 'docker restart lifeos'`
+ 63 - **Users**: Me (`87503f93`) and Krista (`6f26a647`) — Krista was "Partner", renamed everywhere
+ 64 - **Status**: Mobile UI polished, v2 modules audited, running clean. LLM insights layer deferred.
+ 65 - **Decision**: Stay Streamlit, port to React+FastAPI when "perfect"
+ 66
  58 ## Coding Agents (as of 2026-02-22)
  59 - **Codex**: v0.104.0 via npm (`/opt/homebrew/bin/codex`), uses `OPENAI_API_KEY` — API billing only, no subscription option
  60 - **Claude Code**: v2.1.50, two modes:
  61   - `claude` → `ANTHROPIC_API_KEY` (API billing)
     ...


Good. Memory flushed. Now let me also update the heartbeat state since it's been a while since the last email check (last check was at 14:44).

Memory flushed — daily log written and long-term memory updated with LifeOS deployment details and claude-max fix.

NO_REPLY


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 16:19 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=afternoon; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

Conversation info (untrusted metadata):
```json
{
  "message_id": "510",
  "sender": "687577942"
}
```

How does this work? How do you stay logged in forever?


Mark is asking about the LifeOS app - specifically how does it stay logged in forever (no auth/login required). Let me explain how the current setup works without any auth.

Actually, he might be asking about browser automation / the markbot browser profile and how cookies persist. Or he could be asking about the LifeOS app specifically - since we removed the basic auth, it just loads without any login.

Actually, re-reading context - he might be asking about the Tesla OAuth tokens, or the browser automation approach for bills/therapy, or the LifeOS app itself.

Given the flow of conversation (we just talked about bill pay, therapy appointments, Tesla setup, and browser automation), he's probably asking about how the browser automation "stays logged in forever" - i.e., how the markbot browser profile maintains persistent cookies/sessions.

So I should walk through how the markbot browser profile persists authentication across sessions - the cookies and session data get stored locally and reused whenever the automation runs.

Good question — depends which thing you mean, but the answer is similar for all of them:

**Browser automation (bills, therapy, etc.)**
The `markbot` browser profile stores cookies and localStorage just like a regular browser. When you log into Eversource or a therapy portal once, the profile saves the session cookie to disk. Next time I open that site in the markbot profile, it reads those cookies and you're already in. No different from how Chrome keeps you logged into Google — it just persists to a file instead of disappearing when the window closes. Works until the site's session expires or forces a re-login (some sites do this every 30-90 days).

**Tesla (OAuth)**
OAuth tokens work differently — Tesla gives back a refresh token that lasts ~30 days and auto-renews itself every time it's used. The skill caches it in `~/.tesla_cache.json`. As long as you use it at least once a month, it refreshes itself indefinitely. You'd only need to re-auth if you go more than 30 days without using it.

**LifeOS (no login at all)**
Right now the app has zero auth — it's just open to anyone who knows the URL. That's fine for now because the URL isn't public, but it's something to revisit. If we add login later, the options are:
- Streamlit's built-in OAuth (Google login)
- Simple session cookie with a password stored in `secrets.toml`

**The short answer:** cookies + tokens stored on disk, same way your browser works. The only maintenance is re-logging in if a site forces expiry.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 16:46 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=afternoon; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

Read HEARTBEAT.md if it exists (workspace context). Follow it strictly. Do not infer or repeat old tasks from prior chats. If nothing needs attention, reply HEARTBEAT_OK.
Current time: Sunday, February 22nd, 2026 — 4:46 PM (America/New_York)


Last email check was at 1740249840 (14:44 ET). Current time is 16:46 ET. That's about 2 hours since last check. Weekend rules: email only, no calendar nags.Let me check email.

exec

gog gmail search 'in:inbox is:unread newer_than:2h' --max 8 --account mark@forgeco.com --json 2>/dev/null


2025 K1

UNREAD IMPORTANT FYI CATEGORY_PERSONAL INBOX

Forge: King - Install - King

UNREAD Notification CATEGORY_FORUMS INBOX [Superhuman]/AI/Projects

Re: Service visit

Re: Forge - Ho Heat Pump Project

UNREAD Notification CATEGORY_FORUMS SPAM

Re: Project Coordinator Introduction - Jonny Aldsworth

Re: Forge: Saukkonen - Project Coordinator Introduction - Jonny Aldsworth

Re: Forge: Ricci - Project Coordinator Introduction - Jonny Aldsworth

Forge: Kasdorf - Project Coordinator Introduction - Jonny Aldsworth

2025 K1

Forge: King - Install - King

Re: Service visit

Re: Forge - Ho Heat Pump Project


Let me look at what's noteworthy here:1. **Daniel Dion - "2025 K1"** - tagged IMPORTANT! A K1 is a tax document (partnership income). This is an important financial/tax document arriving on a Sunday afternoon. Definitely worth flagging.2. **Ryan Charest - "Forge: King - Install - King"** - project coordinator update, from a Forge employee. Could be an ops issue.3. **Jonathan Weinstock - "Re: Service visit"** - I flagged this earlier (this morning). Now it's a 4-message thread, has grown. Still worth noting it's active.4. **Jonny Aldsworth** - multiple project coordinator introduction threads (King, Ricci, Saukkonen). These are operational project coordinator introductions.

There's also a customer reply from Connie Ho about a heat pump project that landed in spam—worth checking on that. But the K1 tax document from Daniel Dion is the priority here.

write

Checked 2026-02-22 16:46 ET — K1 tax doc flagged (Daniel Dion), Ryan Charest project update.

Checked 2026-02-20 18:07 ET — weekend, no flags needed.


Successfully wrote 302 bytes to /Users/mark/.openclaw-markbot/workspace/memory/heartbeat-state.json


One important item to flag. Let me also note the Weinstock thread has grown. But the K1 is the most important flag.

One flagworthy item:

**📄 Daniel Dion sent your 2025 K1** (4:28 PM) — tagged Important. Tax document, probably needs to go to your accountant. Worth grabbing before you forget it.

Also active but not urgent:
- **Ryan Charest** — King install project update (ops)
- **Weinstock service visit** — thread grew to 4 messages since this morning
- **3x Jonny Aldsworth** — project coordinator intro threads (King, Ricci, Saukkonen)
- **Connie Ho** (landed in spam) — customer reply on Ho heat pump project, might want to check


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 18:44 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=evening; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

Pre-compaction memory flush. Store durable memories now (use memory/2026-02-22.md; create memory/ if needed). IMPORTANT: If the file already exists, APPEND new content only and do not overwrite existing entries. If nothing to store, reply with NO_REPLY.
Current time: Sunday, February 22nd, 2026 — 6:44 PM (America/New_York)


Need to append to the existing memory file for today. Let me check what's already there and add the afternoon/evening updates.

exec

cat /Users/mark/.openclaw-markbot/workspace/memory/2026-02-22.md | tail -5


## Misc
- Notion has "Movies to see with my kids": Aladdin, Gladiator, Brain Donors, Dark Knight
- Mark asked for more movie recommendations — ages of kids not confirmed yet
- Therapy portal URL not yet shared (needed for PER-30)


Good, the file exists. I need to append the new content from the afternoon session (after 4:18 PM when I last flushed memory). Key things that happened after the flush:
1. Mark asked how he stays logged in forever — explained browser profiles/OAuth/cookies2. Tesla setup — added PER-28 to Linear backlog
3. K1 tax document from Daniel Dion flagged (4:28 PM)
4. Connie Ho (customer) email landed in spam - heat pump project reply

Actually wait - the Linear tickets (PER-28, 29, 30) were all created BEFORE the last memory flush. Let me check what was already captured.

Looking at the daily log I wrote earlier, PER-28, 29, and 30 are all already there, but the K1 tax document from Daniel Dion and the browser automation conversation aren't captured yet, so I'll just add those new items.

cat >> /Users/mark/.openclaw-markbot/workspace/memory/2026-02-22.md << 'EOF'

## Afternoon Update (post 4:18 PM)

### Browser Auth Explained
- Explained to Mark how "stay logged in forever" works: browser profile stores cookies, OAuth uses auto-refreshing tokens, LifeOS has no auth at all currently
- Mark understood the pattern for bill pay / therapy / Tesla setups

### Email — Late Afternoon
- **Daniel Dion sent 2025 K1** (4:28 PM) — tax document, tagged Important. Needs to go to accountant.
- Connie Ho (customer) replied on Ho Heat Pump Project — landed in spam, worth checking
- Jonny Aldsworth active on multiple project coordinator intro threads (King, Ricci, Saukkonen)
- Weinstock service visit thread grew to 4 messages

### Pending / Open Items
- Ages of Mark's kids not confirmed — movie list recommendation incomplete
- Therapy portal URL still needed for PER-30
- Tesla OAuth still needs computer session (PER-28)
- Newo AI meeting tomorrow (Monday Feb 23) — check time
EOF
echo "Appended"


Appended


Memory appended. Let me reply NO_REPLY since this is a memory flush message.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 18:45 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=evening; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

System: [2026-02-22 18:16:57 EST] ⚠ Post-Compaction Audit: The following required startup files were not read after context reset:
  - WORKFLOW_AUTO.md
  - memory\/\d{4}-\d{2}-\d{2}\.md

Please read them now using the Read tool before continuing. This ensures your operating protocols are restored after memory compaction.

Conversation info (untrusted metadata):
```json
{
  "message_id": "513",
  "sender": "687577942"
}
```

Add Tesla to the backlog of personal items. Tag as a new skill. Add auth to a new backlog for linear for life os


Mark wants me to:
1. Add Tesla to the personal backlog (it's already there as PER-28, but maybe he wants it tagged or updated)
2. Add auth for LifeOS to a new Linear backlog

Wait, let me re-read: "Add Tesla to the backlog of personal items. Tag as a new skill." - PER-28 already exists. Maybe he wants a label/tag "new skill" added to it.

"Add auth to a new backlog for linear for life os" - Create a new Linear ticket for adding authentication to LifeOS.

Also, there's a post-compaction audit message saying I should read WORKFLOW_AUTO.md and today's memory file.

I should check those files first before handling Mark's requests.

Let me catch up on context first, then handle your request:

ls /Users/mark/.openclaw-markbot/workspace/WORKFLOW_AUTO.md 2>/dev/null && cat /Users/mark/.openclaw-markbot/workspace/WORKFLOW_AUTO.md || echo "not found"


# Daily Log — Sunday, February 22, 2026

## LifeOS Project (big day)

### Deployment
- Deployed LifeOS Streamlit app to Forge server (64.225.48.84) at `https://lifeos.forgeco.dev`
- Running as Docker container (`lifeos`) on `forge_server_default` network behind Caddy HTTPS proxy
- Caddy auto-issued Let's Encrypt SSL cert for `lifeos.forgeco.dev`
- Source volume-mounted so deploys = rsync + `docker restart lifeos` (no rebuild needed)
- Systemd service removed in favor of Docker Compose

### Bugs Fixed
- `StreamlitDuplicateElementKey` crash: `dismiss_review_modal_` prefix added at line 788 of `plugins/daily_log/ui.py`
- Caddy basicauth removed (broke WebSocket/Streamlit repeated auth prompts)
- Deployment pipeline fixed: source code now volume-mounted into container

### Users
- Me: `87503f93-b329-458b-8344-d449721b2692`
- Krista: `6f26a647-e657-4b71-9392-e552079ec068` (was "Partner" — renamed in DB, config, and code)

### Coding Agent (claude-max) — FIXED
- Root issue: was using `--print` flag which only generates text, doesn't execute tools
- Correct invocation: `claude-max --dangerously-skip-permissions "task"` (NO --print)
- Dialog navigation: spawn → wait 8s → `send-keys ["Down"]` → confirm "Yes, I accept" selected → `send-keys ["Enter"]`
- Agent uses Opus 4.6 on Max subscription (no API cost)
- Confirmed working: created hello.txt, ran ls, said TASK_COMPLETE

### Mobile UI + V2 Module Audit (coding agent)
- Agent completed: `.streamlit/config.toml` created (dark theme), `ui.py` mobile polish, `tools.py` cleanup
- All v2 lifestyle modules (Stimulants, Drugs, Relationship, Content Consumption, Intimacy) audited
- App running clean at `https://lifeos.forgeco.dev`

### Decisions
- Staying with Streamlit for now, port to React+FastAPI later when the app is "perfect"
- Not ready for LLM insights layer yet — want more data first
- Sleep import deferred (need to decide Oura vs Whoop)

## New Skills Installed
- `tesla-api` — installed, needs one-time OAuth auth at computer (PER-28)

## Linear Tickets Created
- PER-28: Set up Tesla integration with MarkBot
- PER-29: Set up automated bill pay (National Grid, Eversource)
- PER-30: Set up therapy appointment booking via browser automation

## Model Strategy Locked In
- Default: Sonnet, no reasoning
- Step up: Opus, no reasoning (for complex analysis)
- Heavy: Opus + reasoning (hard problems only)
- Coding: claude-max sub-agent (free on Max subscription)
- I auto-switch using session_status tool — Mark doesn't need to manage this

## Emails Flagged Today
- Owen Krebs Grimsich: Cash flow follow-up (mark@forgeco.com) — tagged "To Respond"
- Jonathan Weinstock: Service visit reply
- Newo AI meeting: tomorrow (Monday Feb 23)
- Alex Bemis: Project delay due to snow emergency
- DO maintenance notice: Core Infrastructure Maintenance in All Regions

## Misc
- Notion has "Movies to see with my kids": Aladdin, Gladiator, Brain Donors, Dark Knight
- Mark asked for more movie recommendations — ages of kids not confirmed yet
- Therapy portal URL not yet shared (needed for PER-30)

## Afternoon Update (post 4:18 PM)

### Browser Auth Explained
- Explained to Mark how "stay logged in forever" works: browser profile stores cookies, OAuth uses auto-refreshing tokens, LifeOS has no auth at all currently
- Mark understood the pattern for bill pay / therapy / Tesla setups

### Email — Late Afternoon
- **Daniel Dion sent 2025 K1** (4:28 PM) — tax document, tagged Important. Needs to go to accountant.
- Connie Ho (customer) replied on Ho Heat Pump Project — landed in spam, worth checking
- Jonny Aldsworth active on multiple project coordinator intro threads (King, Ricci, Saukkonen)
- Weinstock service visit thread grew to 4 messages

### Pending / Open Items
- Ages of Mark's kids not confirmed — movie list recommendation incomplete
- Therapy portal URL still needed for PER-30
- Tesla OAuth still needs computer session (PER-28)
- Newo AI meeting tomorrow (Monday Feb 23) — check time


not found


Good, I have the context. WORKFLOW_AUTO.md doesn't exist - that's fine, it's just an audit check.Now for Mark's request:1. "Add Tesla to the backlog of personal items. Tag as a new skill." — PER-28 already exists. He wants it tagged with a "new skill" label. Let me check if there's a "new skill" label in Linear or create one, then apply it to PER-28.2. "Add auth to a new backlog for linear for life os" — Create a new Linear ticket for adding authentication to LifeOS. This should probably go in the PER team (Personal) or possibly a LifeOS project if one exists.Let me check existing labels first, then create the issues.Actually, wait - re-reading: "Add Tesla to the backlog of personal items.

So PER-28 is already there for Tesla, but Mark might want me to confirm it's in the backlog or add the "new skill" tag to it. For the LifeOS auth request, he could mean creating a separate LifeOS project in Linear, or just adding a new ticket to the existing backlog. I'll start by checking what labels exist and whether a LifeOS project is already set up.

PER-28 already exists for Tesla — let me tag it "New Skill" and create the LifeOS auth ticket. Checking labels first:

doppler run --project markbot_personal --config dev -- python3 << 'EOF'
import urllib.request, os, json

# Get PER team labels
query = '{ team(id: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c") { labels { nodes { id name } } } }'
data = json.dumps({'query': query}).encode()
req = urllib.request.Request('https://api.linear.app/graphql', data=data, method='POST')
req.add_header('Authorization', os.environ["LINEAR_API_KEY"])
req.add_header('Content-Type', 'application/json')
result = json.load(urllib.request.urlopen(req))
print(json.dumps(result, indent=2))
EOF


No "New Skill" label exists. I'll create one, then apply it to PER-28, and also create the LifeOS auth ticket.

For the LifeOS auth ticket, Mark said "Add auth to a new backlog for linear for life os." This might mean he wants a separate Linear project for LifeOS items. Let me create a "LifeOS" project in Linear and add the auth ticket there. Or maybe he just wants it in the PER backlog with a "LifeOS" label.

Actually, I think "new backlog" means just a new ticket in the existing PER team backlog. Let me create a label "New Skill" and apply it to PER-28, then create PER-31 for LifeOS auth.

I'm still uncertain whether he wants a separate LifeOS project or just a ticket in the existing backlog, so let me go ahead and create the "New Skill" label, apply it to PER-28, and add the LifeOS auth ticket to the PER backlog for now.

doppler run --project markbot_personal --config dev -- python3 << 'EOF'
import urllib.request, os, json

def gql(query):
    data = json.dumps({'query': query}).encode()
    req = urllib.request.Request('https://api.linear.app/graphql', data=data, method='POST')
    req.add_header('Authorization', os.environ["LINEAR_API_KEY"])
    req.add_header('Content-Type', 'application/json')
    return json.load(urllib.request.urlopen(req))

# 1. Create "New Skill" label in PER team
result = gql('''
mutation {
  issueLabelCreate(input: {
    teamId: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c"
    name: "New Skill"
    color: "#F2994A"
  }) {
    success
    issueLabel { id name }
  }
}
''')
label_id = result['data']['issueLabelCreate']['issueLabel']['id']
print(f"Created label: New Skill ({label_id})")

# 2. Apply label to PER-28 (Tesla)
result = gql(f'''
mutation {{
  issueUpdate(id: "PER-28", input: {{
    labelIds: ["{label_id}"]
  }}) {{
    success
    issue {{ identifier title }}
  }}
}}
''')
print(f"Tagged: {result['data']['issueUpdate']['issue']['identifier']}")

# 3. Create LifeOS project
result = gql('''
mutation {
  projectCreate(input: {
    name: "LifeOS"
    teamIds: ["d51635fd-4b92-476f-80d8-2b79f7c7cf3c"]
    description: "Personal daily logging app — https://lifeos.forgeco.dev"
  }) {
    success
    project { id name }
  }
}
''')
project_id = result['data']['projectCreate']['project']['id']
print(f"Created project: LifeOS ({project_id})")

# 4. Create auth ticket in LifeOS project
result = gql(f'''
mutation {{
  issueCreate(input: {{
    teamId: "d51635fd-4b92-476f-80d8-2b79f7c7cf3c"
    projectId: "{project_id}"
    title: "Add authentication to LifeOS"
    description: """Add login/auth to lifeos.forgeco.dev so it's not open to anyone who knows the URL.

## Options (pick one)

**Option A — Streamlit native auth (simplest)**
- Add to `.streamlit/secrets.toml` on server:
  ```toml
  [auth]
  redirect_uri = "https://lifeos.forgeco.dev/oauth2/callback"
  cookie_secret = "<random>"
  [auth.google]
  client_id = "..."
  client_secret = "..."
  ```
- Users log in with Google — restricts to specific emails (mark@forgeco.com, krista)
- Requires a Google OAuth app (5 min setup in Google Cloud Console)

**Option B — Simple password gate**
- Add password check at top of `daily_log_demo.py` using `st.secrets`
- No OAuth needed, just a shared password stored in secrets.toml
- Less elegant but zero setup

## Recommendation
Option A (Google OAuth) — Mark already has Google auth set up everywhere, clean experience on mobile.

## Files to update
- `.streamlit/secrets.toml` on server (add to Doppler)
- `daily_log_demo.py` — add `st.experimental_user` check at top""",
    priority: 3
  }}) {{
    success
    issue {{ identifier title }}
  }}
}}
''')
issue = result['data']['issueCreate']['issue']
print(f"Created: {issue['identifier']} - {issue['title']}")
EOF


Created label: New Skill (ac63cc06-4491-413a-8b00-73ee16bebc3a)
Tagged: PER-28
Created project: LifeOS (499500dd-17f6-40fa-a216-4225ec768974)
Created: PER-31 - Add authentication to LifeOS


Done:

- **PER-28** (Tesla) — tagged "New Skill" 🟠
- **LifeOS project** created in Linear — separate from the general Personal backlog
- **PER-31** — "Add authentication to LifeOS" — filed under the new LifeOS project, with a writeup on Google OAuth vs simple password gate


Summary:
## Goal
1. Deploy and stabilize the LifeOS personal daily logging app (Streamlit/Python) to production server
2. Fix all app bugs and get it working cleanly on mobile (iPhone)
3. Audit and fix v2 lifestyle modules
4. Fix and document the coding agent (claude-max) invocation pattern

## Constraints & Preferences
- Stay with Streamlit for now, port to React+FastAPI later when "perfect"
- Not ready for LLM insights feature yet
- Use claude-max sub-agent (Max subscription, no API cost) for all coding tasks
- Default to Sonnet (no reasoning); switch to Opus silently when needed; no reasoning unless explicitly needed
- Don't touch existing Forge infrastructure outside `/home/forge/lifeos/`
- Spend no more than $25 in tokens on any single debug task

## Progress
### Done
- [x] Discovered LifeOS project at `/Users/mark/LifeDataOS/personal-os/`
- [x] Deployed app to server as Docker container (`lifeos`) on `forge_server_default` network
- [x] Configured Caddy to serve `https://lifeos.forgeco.dev` with auto-SSL (Let's Encrypt cert issued)
- [x] Removed HTTP Basic Auth from Caddy (was causing repeated WebSocket auth prompts)
- [x] Fixed `StreamlitDuplicateElementKey` crash: changed `dismiss_review_{review_type}_{log.date}` → `dismiss_review_modal_{review_type}_{log.date}` in `_render_periodic_review` (line 788 of `plugins/daily_log/ui.py`)
- [x] Fixed docker-compose.yml to volume-mount source code (no rebuild needed for changes)
- [x] Renamed user "Partner" → "Krista" in DB, `user_config.json`, and `core/users.py`
- [x] Stored server credentials in Doppler (`markbot_personal/dev`): `DROPLET_HOST`, `DROPLET_USER`, `DROPLET_PASSWORD`
- [x] Fixed and documented claude-max coding agent invocation (Down arrow + Enter to accept bypass dialog)
- [x] Updated MEMORY.md with model strategy and correct agent invocation pattern

### In Progress
- [ ] claude-max agent running (session `faint-nexus`, pid 948) on mobile UI polish + v2 module audit — will send `openclaw system event` on completion

### Blocked
- Sleep import (Oura/Whoop) — needs decision on which device Mark uses
- Finance/health plugins — needs spec
- LLM insights — Mark not ready yet

## Key Decisions
- **Stay Streamlit**: Port to React+FastAPI only when app is "perfect"; Electron ruled out (desktop-only, no mobile)
- **Caddy routing**: Used existing `fs2_caddy` Docker container on `forge_server_default` network; lifeos container joined same network so Caddy reaches it at `lifeos:8503`
- **No HTTP Basic Auth**: Removed because it breaks WebSocket connections; domain obscurity is sufficient for personal tool
- **Volume mounts**: Source code dirs mounted into container so rsync+restart deploys changes without rebuilds
- **Model strategy**: Sonnet default → Opus (no reasoning) for complex tasks → Opus+think:high for very hard problems → claude-max sub-agent for all coding/DevOps

## Next Steps
1. Wait for `openclaw system event` notification that agent completed mobile UI + v2 audit
2. Verify app health: `curl -s https://lifeos.forgeco.dev/_stcore/health` → "ok"
3. Check Docker logs for errors: `ssh forge@64.225.48.84 'docker logs lifeos --tail 20'`
4. Test the app on iPhone at `https://lifeos.forgeco.dev` — confirm mobile layout improved
5. Begin daily use to accumulate data before building LLM insights
6. Decide on sleep device (Oura vs Whoop) before building import

## Critical Context
- **Server**: `forge@64.225.48.84`, key auth, Ubuntu, Docker-based Forge stack running (do NOT touch)
- **App container**: `lifeos` (image `lifeos-lifeos`), on `forge_server_default` network, IP `172.18.0.15`, port `8503`
- **Caddy container**: `fs2_caddy`, IP `172.18.0.6`, same network — routes `lifeos.forgeco.dev` → `lifeos:8503`
- **Caddyfile**: `/home/forge/forge-stack2/Forge-Stack2/caddy/Caddyfile` (append-only, never overwrite)
- **Local source**: `/Users/mark/LifeDataOS/personal-os/`
- **Remote source**: `/home/forge/lifeos/`
- **Deploy command**: `rsync -avz --exclude='.venv' --exclude='__pycache__' --exclude='*.pyc' --exclude='personal.db' --exclude='.git' /Users/mark/LifeDataOS/personal-os/ forge@64.225.48.84:/home/forge/lifeos/ && ssh forge@64.225.48.84 'docker restart lifeos'`
- **Users in DB**: `Me` (87503f93...) and `Krista` (6f26a647...) — `user_config.json` updated on server
- **claude-max agent pattern**: `exec pty:true background:true` → wait 8s → `process send-keys ["Down"]` → brief pause → `process send-keys ["Enter"]` → poll/log to monitor. No `--print` flag — kills tool execution.
- **DigitalOcean maintenance email received** (2:07 PM) — may affect server uptime
- **Alex Bemis snow emergency project delay email** (1:21 PM) — needs attention Monday

---

**Turn Context (split turn):**

## Original Request
The user asked Mark's AI assistant to search Google Drive and Notion for any lists of movies he wants to see with his kids.

## Early Progress
- Simultaneously searched Google Drive (both work and personal accounts) and Notion for movie-related content
- Google Drive results for "movies kids" returned mostly irrelevant files (untitled doc, employeeEvent.csv files) — no clear movie list found
- Notion search found two highly relevant pages:
  - **`b0cb943a-d7cb-4691-b7f6-bd6959d7769c`** — "Movies to see with my kids"
  - **`87261a92-11ed-4d0b-8ee5-ba9a9688511c`** — "Movies to See on video"
  - **`1f8c619f-ca24-8128-8eb9-dfdbd8997e02`** — "Movie" (less clear relevance)

## Context for Suffix
- The Drive search session ("brisk-wharf") was still running when the prefix ended
- The assistant needs to fetch the content of the two/three promising Notion pages (especially "Movies to see with my kids" and "Movies to See on video") to show Mark what's actually in those lists


Summary:
## Goal
1. Deploy and stabilize the LifeOS personal daily logging app (Streamlit/Python) to production server
2. Fix all app bugs and get it working cleanly on mobile (iPhone)
3. Audit and fix v2 lifestyle modules
4. Fix and document the coding agent (claude-max) invocation pattern
5. Find Mark's movie lists (kids + video) from Google Drive and Notion

## Constraints & Preferences
- Stay with Streamlit for now, port to React+FastAPI later when "perfect"
- Not ready for LLM insights feature yet
- Use claude-max sub-agent (Max subscription, no API cost) for all coding tasks
- Default to Sonnet (no reasoning); switch to Opus silently when needed; no reasoning unless explicitly needed
- Don't touch existing Forge infrastructure outside `/home/forge/lifeos/`
- Spend no more than $25 in tokens on any single debug task

## Progress
### Done
- [x] Discovered LifeOS project at `/Users/mark/LifeDataOS/personal-os/`
- [x] Deployed app to server as Docker container (`lifeos`) on `forge_server_default` network
- [x] Configured Caddy to serve `https://lifeos.forgeco.dev` with auto-SSL (Let's Encrypt cert issued)
- [x] Removed HTTP Basic Auth from Caddy (was causing repeated WebSocket auth prompts)
- [x] Fixed `StreamlitDuplicateElementKey` crash: changed `dismiss_review_{review_type}_{log.date}` → `dismiss_review_modal_{review_type}_{log.date}` in `_render_periodic_review` (line 788 of `plugins/daily_log/ui.py`)
- [x] Fixed docker-compose.yml to volume-mount source code (no rebuild needed for changes)
- [x] Renamed user "Partner" → "Krista" in DB, `user_config.json`, and `core/users.py`
- [x] Stored server credentials in Doppler (`markbot_personal/dev`): `DROPLET_HOST`, `DROPLET_USER`, `DROPLET_PASSWORD`
- [x] Fixed and documented claude-max coding agent invocation (Down arrow + Enter to accept bypass dialog)
- [x] Updated MEMORY.md with model strategy and correct agent invocation pattern
- [x] Searched Google Drive (work + personal accounts) for movie lists — no clear results found (returned untitled doc, employeeEvent.csv files)
- [x] Searched Notion for movie-related content — found three highly relevant pages:
  - `b0cb943a-d7cb-4691-b7f6-bd6959d7769c` — **"Movies to see with my kids"**
  - `87261a92-11ed-4d0b-8ee5-ba9a9688511c` — **"Movies to See on video"**
  - `1f8c619f-ca24-8128-8eb9-dfdbd8997e02` — **"Movie"** (relevance unclear)

### In Progress
- [ ] claude-max agent running (session `faint-nexus`, pid 948) on mobile UI polish + v2 module audit — will send `openclaw system event` on completion
- [ ] Fetch and display content of Notion movie list pages (especially `b0cb943a` and `87261a92`)

### Blocked
- Sleep import (Oura/Whoop) — needs decision on which device Mark uses
- Finance/health plugins — needs spec
- LLM insights — Mark not ready yet

## Key Decisions
- **Stay Streamlit**: Port to React+FastAPI only when app is "perfect"; Electron ruled out (desktop-only, no mobile)
- **Caddy routing**: Used existing `fs2_caddy` Docker container on `forge_server_default` network; lifeos container joined same network so Caddy reaches it at `lifeos:8503`
- **No HTTP Basic Auth**: Removed because it breaks WebSocket connections; domain obscurity is sufficient for personal tool
- **Volume mounts**: Source code dirs mounted into container so rsync+restart deploys changes without rebuilds
- **Model strategy**: Sonnet default → Opus (no reasoning) for complex tasks → Opus+think:high for very hard problems → claude-max sub-agent for all coding/DevOps
- **Movie lists in Notion, not Drive**: Google Drive search returned no relevant results; Notion has the authoritative movie lists

## Next Steps
1. Fetch content of Notion pages `b0cb943a` ("Movies to see with my kids") and `87261a92` ("Movies to See on video") and present to Mark
2. Optionally fetch `1f8c619f` ("Movie") to check relevance
3. Wait for `openclaw system event` notification that agent completed mobile UI + v2 audit
4. Verify app health: `curl -s https://lifeos.forgeco.dev/_stcore/health` → "ok"
5. Check Docker logs for errors: `ssh forge@64.225.48.84 'docker logs lifeos --tail 20'`
6. Test the app on iPhone at `https://lifeos.forgeco.dev` — confirm mobile layout improved
7. Begin daily use to accumulate data before building LLM insights
8. Decide on sleep device (Oura vs Whoop) before building import
9. Address Alex Bemis snow emergency project delay email (Monday)

## Critical Context
- **Server**: `forge@64.225.48.84`, key auth, Ubuntu, Docker-based Forge stack running (do NOT touch)
- **App container**: `lifeos` (image `lifeos-lifeos`), on `forge_server_default` network, IP `172.18.0.15`, port `8503`
- **Caddy container**: `fs2_caddy`, IP `172.18.0.6`, same network — routes `lifeos.forgeco.dev` → `lifeos:8503`
- **Caddyfile**: `/home/forge/forge-stack2/Forge-Stack2/caddy/Caddyfile` (append-only, never overwrite)
- **Local source**: `/Users/mark/LifeDataOS/personal-os/`
- **Remote source**: `/home/forge/lifeos/`
- **Deploy command**: `rsync -avz --exclude='.venv' --exclude='__pycache__' --exclude='*.pyc' --exclude='personal.db' --exclude='.git' /Users/mark/LifeDataOS/personal-os/ forge@64.225.48.84:/home/forge/lifeos/ && ssh forge@64.225.48.84 'docker restart lifeos'`
- **Users in DB**: `Me` (87503f93...) and `Krista` (6f26a647...) — `user_config.json` updated on server
- **claude-max agent pattern**: `exec pty:true background:true` → wait 8s → `process send-keys ["Down"]` → brief pause → `process send-keys ["Enter"]` → poll/log to monitor. No `--print` flag — kills tool execution.
- **DigitalOcean maintenance email received** (2:07 PM) — may affect server uptime
- **Alex Bemis snow emergency project delay email** (1:21 PM) — needs attention Monday
- **Notion movie pages**: Search session "brisk-wharf" may still be running; page IDs confirmed via Notion search API


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 18:47 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=evening; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

Pre-compaction memory flush. Store durable memories now (use memory/2026-02-22.md; create memory/ if needed). IMPORTANT: If the file already exists, APPEND new content only and do not overwrite existing entries. If nothing to store, reply with NO_REPLY.
Current time: Sunday, February 22nd, 2026 — 6:47 PM (America/New_York)


This is a pre-compaction memory flush. I need to store durable memories from the compacted session to the daily memory file. Let me first check if the file already exists, then append new content.


The file already exists and has a lot of content. I need to APPEND new content from the compaction summary without overwriting what's there. Let me append the evening session events that aren't yet captured in the file.

Looking at the summary, the key things that happened that aren't in the file:
1. Notion movie list page IDs discovered - actually some movie content IS in there already (Aladdin, Gladiator, etc.)
2. The sub-agent session details (faint-nexus) - the mobile UI/v2 audit is already noted as complete
3. The Brave Search API fix - this is in MEMORY.md but worth noting if not here

4. The Google Drive search for movie lists returned nothing

Most of the key items from the compaction summary are already captured in the daily log. The main additions worth noting are the specific Notion movie page IDs (`b0cb943a` and `87261a92`) that aren't explicitly listed yet, the sub-agent session "brisk-wharf" for Notion search, and the pending task to fetch the content of those movie pages.

Now I'll add a brief evening update to wrap things up.

edit

## Evening Session (~6:47 PM) — Post-Compaction Flush

### Notion Movie List Page IDs (confirmed)
- `b0cb943a-d7cb-4691-b7f6-bd6959d7769c` → "Movies to see with my kids"
- `87261a92-11ed-4d0b-8ee5-ba9a9688511c` → "Movies to See on video"
- `1f8c619f-ca24-8128-8eb9-dfdbd8997e02` → "Movie" (relevance unclear)
- Google Drive search returned nothing useful — Notion is authoritative for movie lists

### Pending at Compaction
- Fetch content of Notion movie pages (b0cb943a + 87261a92) and display to Mark
- Await DigitalOcean maintenance — may affect server uptime
- Alex Bemis snow emergency email — address Monday

---

## Afternoon Update (post 4:18 PM)


Successfully replaced text in /Users/mark/.openclaw-markbot/workspace/memory/2026-02-22.md.

    ...
 61 - Notion has "Movies to see with my kids": Aladdin, Gladiator, Brain Donors, Dark Knight
 62 - Mark asked for more movie recommendations — ages of kids not confirmed yet
 63 - Therapy portal URL not yet shared (needed for PER-30)
 64
+65 ## Evening Session (~6:47 PM) — Post-Compaction Flush
+66
+67 ### Notion Movie List Page IDs (confirmed)
+68 - `b0cb943a-d7cb-4691-b7f6-bd6959d7769c` → "Movies to see with my kids"
+69 - `87261a92-11ed-4d0b-8ee5-ba9a9688511c` → "Movies to See on video"
+70 - `1f8c619f-ca24-8128-8eb9-dfdbd8997e02` → "Movie" (relevance unclear)
+71 - Google Drive search returned nothing useful — Notion is authoritative for movie lists
+72
+73 ### Pending at Compaction
+74 - Fetch content of Notion movie pages (b0cb943a + 87261a92) and display to Mark
+75 - Await DigitalOcean maintenance — may affect server uptime
+76 - Alex Bemis snow emergency email — address Monday
+77
+78 ---
+79
 65 ## Afternoon Update (post 4:18 PM)
 66
 67 ### Browser Auth Explained
 68 - Explained to Mark how "stay logged in forever" works: browser profile stores cookies, OAuth uses auto-refreshing tokens, LifeOS has no auth at all currently
    ...


Memory flush complete. Reply with NO_REPLY.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 18:48 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=evening; quarter=Q1; days_to_quarter_end=37
Note: Day-of-week is authoritative; do not recompute weekday from date.
[END RUNTIME METADATA]

System: [2026-02-22 18:46:58 EST] ⚠ Post-Compaction Audit: The following required startup files were not read after context reset:
  - WORKFLOW_AUTO.md
  - memory\/\d{4}-\d{2}-\d{2}\.md

Please read them now using the Read tool before continuing. This ensures your operating protocols are restored after memory compaction.

Conversation info (untrusted metadata):
```json
{
  "message_id": "514",
  "sender": "687577942"
}
```

In our current life os implementation is there or in the spec about the different time scale questions. Daily. Weekly. Month. And finding the right way to present them. If not add a ticket for us to discuss then build.


Mark is asking about periodic review / time-scale questions in LifeOS (daily, weekly, monthly). Let me check the current implementation to see if this exists. I should read the relevant files. But first, I need to read the startup files as the system message requested.

Let me read the startup files and also check the LifeOS implementation.

Let me check the codebase and read my context files.

exec

find /Users/mark/LifeDataOS/personal-os -type f -name "*.py" | xargs grep -l -i "review\|weekly\|monthly\|periodic" 2>/dev/null


# Daily Log — Sunday, February 22, 2026

## LifeOS Project (big day)

### Deployment
- Deployed LifeOS Streamlit app to Forge server (64.225.48.84) at `https://lifeos.forgeco.dev`
- Running as Docker container (`lifeos`) on `forge_server_default` network behind Caddy HTTPS proxy
- Caddy auto-issued Let's Encrypt SSL cert for `lifeos.forgeco.dev`
- Source volume-mounted so deploys = rsync + `docker restart lifeos` (no rebuild needed)
- Systemd service removed in favor of Docker Compose

### Bugs Fixed
- `StreamlitDuplicateElementKey` crash: `dismiss_review_modal_` prefix added at line 788 of `plugins/daily_log/ui.py`
- Caddy basicauth removed (broke WebSocket/Streamlit repeated auth prompts)
- Deployment pipeline fixed: source code now volume-mounted into container

### Users
- Me: `87503f93-b329-458b-8344-d449721b2692`
- Krista: `6f26a647-e657-4b71-9392-e552079ec068` (was "Partner" — renamed in DB, config, and code)

### Coding Agent (claude-max) — FIXED
- Root issue: was using `--print` flag which only generates text, doesn't execute tools
- Correct invocation: `claude-max --dangerously-skip-permissions "task"` (NO --print)
- Dialog navigation: spawn → wait 8s → `send-keys ["Down"]` → confirm "Yes, I accept" selected → `send-keys ["Enter"]`
- Agent uses Opus 4.6 on Max subscription (no API cost)
- Confirmed working: created hello.txt, ran ls, said TASK_COMPLETE

### Mobile UI + V2 Module Audit (coding agent)
- Agent completed: `.streamlit/config.toml` created (dark theme), `ui.py` mobile polish, `tools.py` cleanup
- All v2 lifestyle modules (Stimulants, Drugs, Relationship, Content Consumption, Intimacy) audited
- App running clean at `https://lifeos.forgeco.dev`

### Decisions
- Staying with Streamlit for now, port to React+FastAPI later when the app is "perfect"
- Not ready for LLM insights layer yet — want more data first
- Sleep import deferred (need to decide Oura vs Whoop)

## New Skills Installed
- `tesla-api` — installed, needs one-time OAuth auth at computer (PER-28)

## Linear Tickets Created
- PER-28: Set up Tesla integration with MarkBot
- PER-29: Set up automated bill pay (National Grid, Eversource)
- PER-30: Set up therapy appointment booking via browser automation

## Model Strategy Locked In
- Default: Sonnet, no reasoning
- Step up: Opus, no reasoning (for complex analysis)
- Heavy: Opus + reasoning (hard problems only)
- Coding: claude-max sub-agent (free on Max subscription)
- I auto-switch using session_status tool — Mark doesn't need to manage this

## Emails Flagged Today
- Owen Krebs Grimsich: Cash flow follow-up (mark@forgeco.com) — tagged "To Respond"
- Jonathan Weinstock: Service visit reply
- Newo AI meeting: tomorrow (Monday Feb 23)
- Alex Bemis: Project delay due to snow emergency
- DO maintenance notice: Core Infrastructure Maintenance in All Regions

## Misc
- Notion has "Movies to see with my kids": Aladdin, Gladiator, Brain Donors, Dark Knight
- Mark asked for more movie recommendations — ages of kids not confirmed yet
- Therapy portal URL not yet shared (needed for PER-30)

## Evening Session (~6:47 PM) — Post-Compaction Flush

### Notion Movie List Page IDs (confirmed)
- `b0cb943a-d7cb-4691-b7f6-bd6959d7769c` → "Movies to see with my kids"
- `87261a92-11ed-4d0b-8ee5-ba9a9688511c` → "Movies to See on video"
- `1f8c619f-ca24-8128-8eb9-dfdbd8997e02` → "Movie" (relevance unclear)
- Google Drive search returned nothing useful — Notion is authoritative for movie lists

### Pending at Compaction
- Fetch content of Notion movie pages (b0cb943a + 87261a92) and display to Mark
- Await DigitalOcean maintenance — may affect server uptime
- Alex Bemis snow emergency email — address Monday

---

## Afternoon Update (post 4:18 PM)

### Browser Auth Explained
- Explained to Mark how "stay logged in forever" works: browser profile stores cookies, OAuth uses auto-refreshing tokens, LifeOS has no auth at all currently
- Mark understood the pattern for bill pay / therapy / Tesla setups

### Email — Late Afternoon
- **Daniel Dion sent 2025 K1** (4:28 PM) — tax document, tagged Important. Needs to go to accountant.
- Connie Ho (customer) replied on Ho Heat Pump Project — landed in spam, worth checking
- Jonny Aldsworth active on multiple project coordinator intro threads (King, Ricci, Saukkonen)
- Weinstock service visit thread grew to 4 messages

### Pending / Open Items
- Ages of Mark's kids not confirmed — movie list recommendation incomplete
- Therapy portal URL still needed for PER-30
- Tesla OAuth still needs computer session (PER-28)
- Newo AI meeting tomorrow (Monday Feb 23) — check time


Good, I can see there are relevant files: `config/questions.py`, `plugins/daily_log/ui.py`, and `test_periodic_reviews.py`. Let me read these to understand what's already in place.

Good, there's already something there. Let me dig into the relevant files.

grep -n "review\|weekly\|monthly\|periodic\|timescale\|time_scale\|cadence" /Users/mark/LifeDataOS/personal-os/plugins/daily_log/ui.py | head -60


"""Centralized configuration for all questions and labels.

This is the "Content Brain" - all UI text and questions live here.
Modify this file to change question wording without touching UI code.
"""

# ========== DAILY LOG CONFIGURATION ==========

DAILY_CONFIG = {
    "Morning": {
        "nights_apart": {
            "label": "Together Tonight?",
            "help": "Are you spending tonight together?"
        },
        "nights_apart_planned": {
            "label": "Was this Planned?",
            "help": "Was this night apart scheduled in advance?"
        },
        "own_evening_planned": {
            "label": "Did I plan my own evening?",
            "help": "Critical OKR: Did you proactively plan something for yourself?"
        },
        "goals": {
            "section_title": "Today's Big 3 Goals",
            "caption": "What are your top 3 priorities for today?",
            "placeholders": [
                "Most important task...",
                "Second priority...",
                "Third priority..."
            ]
        },
        "reading": {
            "section_title": "Reading (12 Books Goal)",
            "read_yesterday": "Read Yesterday?",
            "read_before_bed": "Read Before Bed? (Sleep Hygiene)"
        },
        "supplements": {
            "section_title": "Supplements",
            "ag1": "AG1 Taken?",
            "creatine": "Creatine Taken?"
        }
    },

    "Evening": {
        "exercise": {
            "section_title": "Exercise",
            "workout": "Did you workout?",
            "sore": "Are you sore?",
            "rpe_label": "RPE (Rate of Perceived Exertion)",
            "rpe_help": "1=Easy, 5=Max effort",
            "soreness_label": "Soreness Level",
            "soreness_help": "1=Mild, 5=Very sore"
        },
        "stimulants": {
            "section_title": "Stimulants & Substances",
            "caffeine_label": "Total Caffeine (mg)",
            "caffeine_time_label": "Last Caffeine Time",
            "alcohol_label": "Alcohol (Standard Units)",
            "alcohol_help": "1 unit = 1 beer/wine/shot",
            "adderall": "Adderall?",
            "modafinil": "Modafinil?"
        },
        "productivity": {
            "section_title": "Productivity Review",
            "goals_completed_caption": "Which goals did you crush?",
            "side_project": "Side Project Work?",
            "side_project_released": "Side Project RELEASED?",
            "content_creation": "Content Creation?",
            "chess_label": "Chess Study (Minutes)"
        },
        "scores": {
            "section_title": "Today's Scores (1-5 Scale)",
            "connection": {"label": "Connection", "help": "How connected with K?"},
            "presence": {"label": "Presence", "help": "How present were you?"},
            "partner_mood": {"label": "Partner Mood", "help": "K's mood today"},
            "edgy_anxious": {"label": "Edgy/Anxious", "help": "1=Calm, 5=Anxious"},
            "stress": {"label": "Stress", "help": "1=Calm, 5=Overwhelmed"},
            "satisfaction": {"label": "Satisfaction", "help": "Overall satisfaction"},
            "productivity": {"label": "Productivity", "help": "How productive?"}
        },
        "reflection": {
            "section_title": "Reflection",
            "gratitude_label": "Gratitude",
            "gratitude_placeholder": "What are you grateful for?",
            "notes_label": "Notes",
            "notes_placeholder": "Other reflections..."
        }
    }
}

# ========== SPRINT RETROSPECTIVE CONFIGURATION ==========

SPRINT_RETRO_CONFIG = {
    "Family & Social": {
        "metrics": ["connection_score", "partner_mood_score", "presence_score"],
        "questions": [
            ("interactions_k_kids", "How were interactions with K's kids?"),
            ("interactions_mck_kids", "How were interactions with MCK kids?"),
            ("sex_life", "How was our sex life this sprint?"),
            ("social_connections", "How were social connections outside family?")
        ]
    },
    "Health & Body": {
        "metrics": ["rpe_score", "soreness_score"],
        "questions": [
            ("workouts_qualitative", "How did workouts go this sprint?"),
            ("meal_prep", "How was meal prep and nutrition?"),
            ("sleep_quality", "How was sleep quality? (subjective)"),
            ("energy_levels", "How were energy levels throughout?")
        ]
    },
    "Productivity & Work": {
        "metrics": ["productivity_score", "satisfaction_score"],
        "questions": [
            ("work_progress", "What progress did you make on work goals?"),
            ("side_projects", "Progress on side projects?"),
            ("focus_quality", "How was focus and deep work quality?"),
            ("time_management", "How effective was time management?")
        ]
    },
    "Mental & Emotional": {
        "metrics": ["stress_score", "edgy_anxious_score"],
        "questions": [
            ("anxiety_triggers", "What triggered anxiety this sprint?"),
            ("stress_management", "How did you manage stress?"),
            ("mindfulness", "How was your mindfulness practice?"),
            ("emotional_regulation", "How was emotional regulation?")
        ]
    },
    "Habits & Systems": {
        "metrics": ["chess_minutes", "caffeine_mg", "alcohol_units"],
        "questions": [
            ("reading_progress", "Reading progress (12 books goal)?"),
            ("supplement_consistency", "Supplement consistency?"),
            ("substance_optimization", "Caffeine/alcohol optimization learnings?"),
            ("habit_formation", "New habits formed or broken?")
        ]
    }
}

# ========== SPRINT PLANNER CONFIGURATION ==========

SPRINT_PLANNER_CONFIG = {
    "Focus & Intentions": [
        ("main_focus", "🎯 Main Focus for Next 14 Days", "What's the ONE thing to prioritize?"),
        ("theme", "🎨 Sprint Theme", "What's the overarching theme? (e.g., 'Deep Work', 'Recovery')")
    ],

    "Goals & Commitments": [
        ("goal_1", "🥇 Sprint Goal 1", "Most important goal for the sprint"),
        ("goal_2", "🥈 Sprint Goal 2", "Second priority"),
        ("goal_3", "🥉 Sprint Goal 3", "Third priority")
    ],

    "Habits & Systems": [
        ("habit_target", "🔄 Habit to Build/Break", "One specific habit to focus on"),
        ("system_improvement", "⚙ System to Improve", "Process or routine to optimize")
    ],

    "Relationship & Life": [
        ("relationship_intention", "💑 Relationship Intention", "How do you want to show up with K?"),
        ("life_balance", "⚖ Balance Goal", "What area needs more attention?")
    ]
}

# ========== PERIODIC REVIEW CONFIGURATION ==========

PERIODIC_CONFIG = {
    "Weekly": {
        "trigger": "Every Sunday",
        "questions": [
            ("weekly_win", "🏆 Biggest Win This Week"),
            ("weekly_blocker", "🚧 Biggest Blocker"),
            ("weekly_lessons", "💡 Key Lessons")
        ]
    },

    "Bi-Weekly": {
        "trigger": "Even-numbered weeks on Sunday",
        "questions": [
            ("biweekly_progress", "📈 Progress Update"),
            ("biweekly_adjustments", "🔄 Strategy Adjustments")
        ]
    },

    "Monthly": {
        "trigger": "1st of every month",
        "questions": [
            ("monthly_focus", "🎯 Big Focus for Next Month"),
            ("monthly_goal_1", "Goal 1"),
            ("monthly_goal_2", "Goal 2"),
            ("monthly_goal_3", "Goal 3")
        ]
    },

    "Quarterly": {
        "trigger": "1st of Jan/Apr/Jul/Oct",
        "questions": [
            ("quarterly_theme", "🎨 Theme for This Quarter"),
            ("quarterly_okrs", "📊 Top 3 OKRs")
        ]
    },

    "Annual": {
        "trigger": "January 1st",
        "questions": [
            ("annual_wins", "Top 5 Wins from Last Year"),
            ("annual_vision", "Vision for This Year")
        ]
    }
}

# ========== USER PROFILES ==========

USER_PROFILES = {
    "Mark": {
        "user_name": "Me",  # Maps to database user
        "display_name": "Mark",
        "color": "#4A90E2"  # Blue for charts
    },
    "Partner": {
        "user_name": "Partner",  # Maps to database user
        "display_name": "Partner",
        "color": "#E24A90"  # Red for charts
    }
}


36:def _get_active_reviews(check_date: dt.date) -> List[str]:
37:    """Determine which periodic reviews should be active for a given date.
47:        check_date: Date to check for active reviews
50:        List of active review types (in priority order)
119:def _get_outstanding_reviews(user_id: uuid.UUID, days_back: int = 30) -> List[Tuple[str, dt.date]]:
120:    """Get list of outstanding periodic reviews.
122:    Checks last N days for review trigger dates that haven't been
130:        List of (review_type, date) tuples, sorted by date descending
141:            # Get active reviews for this date
142:            active_reviews = _get_active_reviews(check_date)
144:            if not active_reviews:
158:                # Check each active review
159:                for review_type in active_reviews:
160:                    review_key = review_type.lower().replace("-", "_")
161:                    is_completed = log.metrics.get(f'{review_key}_review_completed', False)
162:                    is_dismissed = log.metrics.get(f'{review_key}_review_dismissed', False)
165:                        outstanding.append((review_type, check_date))
167:                # No log exists yet, so review is outstanding
168:                for review_type in active_reviews:
169:                    outstanding.append((review_type, check_date))
179:    """Calculate bi-weekly Sunday dates going back N months.
185:        List of Sunday dates (bi-weekly intervals), most recent first
198:    # Filter to bi-weekly (every other Sunday)
200:    biweekly_sundays = [s for s in sundays if s.isocalendar()[1] % 2 == 0]
203:    return sorted(biweekly_sundays, reverse=True)
349:    - Sprint navigation (bi-weekly Sundays)
361:        st.warning("No bi-weekly Sunday dates found in last 3 months")
374:        help="Choose a bi-weekly Sunday to review"
547:    st.markdown("- Weekly/monthly summaries")
552:def _dismiss_review(user_id: uuid.UUID, review_date: dt.date, review_type: str):
553:    """Dismiss a periodic review permanently.
557:        review_date: Date of the review
558:        review_type: Type of review to dismiss
562:        log = get_or_create_log(user_id, review_date)
569:            review_key = review_type.lower().replace("-", "_")
570:            log_obj.metrics[f'{review_key}_review_dismissed'] = True
571:            log_obj.metrics[f'{review_key}_review_dismissed_at'] = dt.datetime.now(dt.timezone.utc).isoformat()
578:        st.error(f"Error dismissing review: {e}")
584:def _render_periodic_review(
585:    review_type: str,
589:    """Render a periodic review form (The Interceptor).
595:        review_type: Type of review ("Weekly", "Bi-Weekly", etc.)
596:        log: DailyLog object for storing review data
600:        True if review was handled (saved or dismissed), False otherwise
603:    review_key = review_type.lower().replace("-", "_")
606:    if metrics.get(f'{review_key}_review_completed') or metrics.get(f'{review_key}_review_dismissed'):
610:    st.warning(f"🎯 **{review_type} Review Required** - Please complete or dismiss to continue")
613:        st.markdown(f"## 📋 {review_type} Review")
616:        # Different questions based on review type
617:        review_data = {}
619:        if review_type == "Weekly":
624:                weekly_win = st.text_area(
626:                    key=f"weekly_win_{log.date}",
630:                review_data['weekly_win'] = weekly_win
633:                weekly_blocker = st.text_area(
635:                    key=f"weekly_blocker_{log.date}",
639:                review_data['weekly_blocker'] = weekly_blocker
641:            weekly_lessons = st.text_area(
643:                key=f"weekly_lessons_{log.date}",
647:            review_data['weekly_lessons'] = weekly_lessons


Now I have a pretty good picture. Let me look at more of the periodic review section in ui.py to understand what's already implemented vs what's missing.

Good context. Let me see the rest of the periodic review implementation and also look at what's missing.

sed -n '647,800p' /Users/mark/LifeDataOS/personal-os/plugins/daily_log/ui.py

sed -n '36,120p' /Users/mark/LifeDataOS/personal-os/plugins/daily_log/ui.py


review_data['weekly_lessons'] = weekly_lessons

        elif review_type == "Bi-Weekly":
            st.markdown("### Bi-Weekly Strategic Review")

            col1, col2 = st.columns(2)
            with col1:
                biweekly_progress = st.text_area(
                    "📈 Progress Update",
                    key=f"biweekly_progress_{log.date}",
                    height=120,
                    placeholder="What progress did you make on your big goals?"
                )
                review_data['biweekly_progress'] = biweekly_progress

            with col2:
                biweekly_adjustments = st.text_area(
                    "🔄 Strategy Adjustments",
                    key=f"biweekly_adjustments_{log.date}",
                    height=120,
                    placeholder="What needs to change?"
                )
                review_data['biweekly_adjustments'] = biweekly_adjustments

        elif review_type == "Monthly":
            st.markdown("### Monthly Review")

            monthly_focus = st.text_input(
                "🎯 Big Focus for Next Month",
                key=f"monthly_focus_{log.date}",
                placeholder="What's the ONE thing to focus on?"
            )
            review_data['monthly_focus'] = monthly_focus

            col1, col2, col3 = st.columns(3)
            with col1:
                monthly_goal_1 = st.text_input(
                    "Goal 1",
                    key=f"monthly_goal_1_{log.date}",
                    placeholder="Monthly goal..."
                )
                review_data['monthly_goal_1'] = monthly_goal_1

            with col2:
                monthly_goal_2 = st.text_input(
                    "Goal 2",
                    key=f"monthly_goal_2_{log.date}",
                    placeholder="Monthly goal..."
                )
                review_data['monthly_goal_2'] = monthly_goal_2

            with col3:
                monthly_goal_3 = st.text_input(
                    "Goal 3",
                    key=f"monthly_goal_3_{log.date}",
                    placeholder="Monthly goal..."
                )
                review_data['monthly_goal_3'] = monthly_goal_3

        elif review_type == "Quarterly":
            st.markdown("### Quarterly Review")

            quarterly_theme = st.text_input(
                "🎨 Theme for This Quarter",
                key=f"quarterly_theme_{log.date}",
                placeholder="What's the overarching theme?"
            )
            review_data['quarterly_theme'] = quarterly_theme

            quarterly_okrs = st.text_area(
                "📊 Top 3 OKRs",
                key=f"quarterly_okrs_{log.date}",
                height=120,
                placeholder="List your 3 most important objectives..."
            )
            review_data['quarterly_okrs'] = quarterly_okrs

        elif review_type == "Annual":
            st.markdown("### 🎉 Annual Review")

            st.markdown("**Looking Back:**")
            annual_wins = st.text_area(
                "Top 5 Wins from Last Year",
                key=f"annual_wins_{log.date}",
                height=120
            )
            review_data['annual_wins'] = annual_wins

            st.markdown("**Looking Forward:**")
            annual_vision = st.text_area(
                "Vision for This Year",
                key=f"annual_vision_{log.date}",
                height=120,
                placeholder="What do you want this year to be about?"
            )
            review_data['annual_vision'] = annual_vision

        st.markdown("---")

        # Two-button layout: Save vs Dismiss
        col_save, col_dismiss = st.columns(2)

        with col_save:
            if st.button(
                f"💾 Save {review_type} Review",
                type="primary",
                use_container_width=True,
                key=f"save_review_{review_type}_{log.date}"
            ):
                # Save review data to metrics
                session = get_session()
                try:
                    log_obj = session.get(DailyLog, log.id)
                    if log_obj:
                        if not log_obj.metrics:
                            log_obj.metrics = {}

                        # Store review data
                        for key, value in review_data.items():
                            log_obj.metrics[key] = value

                        # Mark as completed
                        log_obj.metrics[f'{review_key}_review_completed'] = True

                        session.add(log_obj)
                        session.commit()

                        st.success(f"✅ {review_type} Review saved!")
                        st.balloons()
                        st.rerun()  # Refresh to hide the review

                except Exception as e:
                    session.rollback()
                    st.error(f"❌ Error saving review: {e}")
                finally:
                    session.close()

        with col_dismiss:
            if st.button(
                "❌ Dismiss",
                use_container_width=True,
                key=f"dismiss_review_modal_{review_type}_{log.date}"
            ):
                # Mark as dismissed without saving answers
                session = get_session()
                try:
                    log_obj = session.get(DailyLog, log.id)
                    if log_obj:
                        if not log_obj.metrics:
                            log_obj.metrics = {}

                        log_obj.metrics[f'{review_key}_review_dismissed'] = True

                        session.add(log_obj)


def _get_active_reviews(check_date: dt.date) -> List[str]:
    """Determine which periodic reviews should be active for a given date.

    Triggers:
    - Weekly: Every Sunday
    - Bi-Weekly: Even-numbered weeks (Sunday)
    - Monthly: 1st of every month
    - Quarterly: 1st of Jan/Apr/Jul/Oct
    - Annual: January 1st

    Args:
        check_date: Date to check for active reviews

    Returns:
        List of active review types (in priority order)
    """
    active = []

    # Weekly: Every Sunday
    is_sunday = check_date.weekday() == 6
    if is_sunday:
        active.append("Weekly")

    # Bi-Weekly: Even weeks (on Sunday)
    if is_sunday:
        week_number = check_date.isocalendar()[1]
        if week_number % 2 == 0:
            active.append("Bi-Weekly")

    # Monthly: 1st of month
    is_first_of_month = check_date.day == 1
    if is_first_of_month:
        active.append("Monthly")

    # Quarterly: 1st of Q months (Jan/Apr/Jul/Oct)
    if is_first_of_month and check_date.month in [1, 4, 7, 10]:
        active.append("Quarterly")

    # Annual: January 1st
    if is_first_of_month and check_date.month == 1:
        active.append("Annual")

    return active

def _get_missing_logs(user_id: uuid.UUID, days: int = 7) -> List[dt.date]:
    """Get list of dates in last N days that have no log entry.

    Args:
        user_id: User UUID
        days: Number of days to check (default: 7)

    Returns:
        List of dates with no logs, sorted oldest first
    """
    from sqlmodel import select, and_

    today = dt.date.today()
    missing_dates = []

    session = get_session()
    try:
        for i in range(days):
            check_date = today - dt.timedelta(days=i)

            statement = select(DailyLog).where(
                and_(
                    DailyLog.user_id == user_id,
                    DailyLog.date == check_date
                )
            )
            existing = session.exec(statement).first()

            if not existing:
                missing_dates.append(check_date)

        # Return oldest first (chronological order)
        return sorted(missing_dates)

    finally:
        session.close()

def _get_outstanding_reviews(user_id: uuid.UUID, days_back: int = 30) -> List[Tuple[str, dt.date]]:
    """Get list of outstanding periodic reviews.


Now I have a really good understanding of what's in the codebase. Let me summarize what exists and what's missing:

**What EXISTS:**
1. **Periodic review triggers** in `config/questions.py` - Weekly, Bi-Weekly, Monthly, Quarterly, Annual all defined with question configs
2. **Active review detection** - `_get_active_reviews()` determines which reviews to show based on date (Sundays for weekly, even weeks for bi-weekly, 1st of month for monthly, etc.)
3. **Outstanding review detection** - `_get_outstanding_reviews()` looks back 30 days for uncompleted reviews

4. **Review rendering** - Forms get generated for each review type, and there's save/dismiss logic that either stores results in metrics or permanently skips them
5. **"The Interceptor" pattern** - Reviews appear as blocking modals that users must address before continuing

**What's MISSING or UNCLEAR:**
The question sets themselves are pretty generic right now—just basic prompts like weekly wins and blockers—without the depth and specificity that would make each time scale truly valuable.

Beyond that, there's no dedicated review dashboard or historical view. Reviews only surface when you happen to open the daily log on a trigger date, and there's no way to browse back through past reviews. The monthly and quarterly reviews especially feel underdeveloped, lacking any real retrospective structure. Even the annual review is pretty thin.

So when Mark asks about the spec for time-scale-specific questions and how to present them, he's really asking a design question: what should we actually be asking people at each cadence, and how do we surface those reviews in a way that matches the rhythm? This needs a conversation first before I start building anything.

Here's where things stand:

**The scaffolding exists, but it's thin.** The app already has:
- ✅ Trigger logic (Weekly=Sunday, Bi-Weekly=even weeks, Monthly=1st, Quarterly=Q starts, Annual=Jan 1)
- ✅ Review detection that looks back 30 days for missed reviews
- ✅ A blocking modal ("Interceptor") that surfaces reviews when you open the daily log
- ✅ Save/dismiss per review, stored in daily log metrics

**What's missing / underdeveloped:**
- ❌ **The questions are thin** — weekly is just win/blocker/lessons; monthly is focus + 3 goals. No real depth or differentiation in *how you think* at each cadence
- ❌ **No dedicated review page** — reviews only appear as a modal inside the daily log flow, not as a standalone experience you can revisit
- ❌ **No historical review browsing** — can't look back at past weekly/monthly reviews
- ❌ **No UX design spec** — what should a monthly review *feel* like vs. a weekly? Different depth, different format, different prompts

This is a design+product question before it's a code question. The right move is to actually design what you want each cadence to ask and feel like before building — otherwise we'll end up with more thin generic prompts.

Creating a ticket for us to discuss first: