[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 04:37 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=night; 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 — 4:37 AM (America/New_York)


It's 4:37 AM on a Sunday. According to HEARTBEAT.md rules:
- Late night (22:00–07:00): silence unless P0
- Weekends: email only (no calendar nags)

It's within the quiet period (22:00–07:00), so I should stay silent unless there's something truly urgent. I should reply HEARTBEAT_OK.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 07:33 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=morning; 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 — 7:33 AM (America/New_York)


It's 7:33 AM on a Sunday. The quiet period ends at 7:00 AM, so I'm now in the active window.

According to HEARTBEAT.md:
- Weekends: email only (no calendar nags)
- Check email if > 2 hours since last check

Let me check the heartbeat state to see when email was last checked.


Checked 2026-02-21 21:28 ET — nothing new since 18:43.

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


Email was last checked at timestamp 1740191280. Let me convert that to a readable time.

1740191280 seconds → February 21, 2026 21:28 ET

Current time is February 22, 2026 07:33 ET. That's about 10 hours since the last email check. Since it's a weekend, I should check email only (no calendar nags). Let me check email now.

Last email check was ~10 hours ago — due for a scan. Weekend rules: email only, no calendar.


---
name: gog
description: Google Workspace CLI for Gmail, Calendar, Drive, Contacts, Sheets, and Docs.
homepage: https://gogcli.sh
metadata:
  {
    "openclaw":
      {
        "emoji": "🎮",
        "requires": { "bins": ["gog"] },
        "install":
          [
            {
              "id": "brew",
              "kind": "brew",
              "formula": "steipete/tap/gogcli",
              "bins": ["gog"],
              "label": "Install gog (brew)",
            },
          ],
      },
  }
---

# gog

Use `gog` for Gmail/Calendar/Drive/Contacts/Sheets/Docs. Requires OAuth setup.

Setup (once)

- `gog auth credentials /path/to/client_secret.json`
- `gog auth add you@gmail.com --services gmail,calendar,drive,contacts,docs,sheets`
- `gog auth list`

Common commands

- Gmail search: `gog gmail search 'newer_than:7d' --max 10`
- Gmail messages search (per email, ignores threading): `gog gmail messages search "in:inbox from:ryanair.com" --max 20 --account you@example.com`
- Gmail send (plain): `gog gmail send --to a@b.com --subject "Hi" --body "Hello"`
- Gmail send (multi-line): `gog gmail send --to a@b.com --subject "Hi" --body-file ./message.txt`
- Gmail send (stdin): `gog gmail send --to a@b.com --subject "Hi" --body-file -`
- Gmail send (HTML): `gog gmail send --to a@b.com --subject "Hi" --body-html "<p>Hello</p>"`
- Gmail draft: `gog gmail drafts create --to a@b.com --subject "Hi" --body-file ./message.txt`
- Gmail send draft: `gog gmail drafts send <draftId>`
- Gmail reply: `gog gmail send --to a@b.com --subject "Re: Hi" --body "Reply" --reply-to-message-id <msgId>`
- Calendar list events: `gog calendar events <calendarId> --from <iso> --to <iso>`
- Calendar create event: `gog calendar create <calendarId> --summary "Title" --from <iso> --to <iso>`
- Calendar create with color: `gog calendar create <calendarId> --summary "Title" --from <iso> --to <iso> --event-color 7`
- Calendar update event: `gog calendar update <calendarId> <eventId> --summary "New Title" --event-color 4`
- Calendar show colors: `gog calendar colors`
- Drive search: `gog drive search "query" --max 10`
- Contacts: `gog contacts list --max 20`
- Sheets get: `gog sheets get <sheetId> "Tab!A1:D10" --json`
- Sheets update: `gog sheets update <sheetId> "Tab!A1:B2" --values-json '[["A","B"],["1","2"]]' --input USER_ENTERED`
- Sheets append: `gog sheets append <sheetId> "Tab!A:C" --values-json '[["x","y","z"]]' --insert INSERT_ROWS`
- Sheets clear: `gog sheets clear <sheetId> "Tab!A2:Z"`
- Sheets metadata: `gog sheets metadata <sheetId> --json`
- Docs export: `gog docs export <docId> --format txt --out /tmp/doc.txt`
- Docs cat: `gog docs cat <docId>`

Calendar Colors

- Use `gog calendar colors` to see all available event colors (IDs 1-11)
- Add colors to events with `--event-color <id>` flag
- Event color IDs (from `gog calendar colors` output):
  - 1: #a4bdfc
  - 2: #7ae7bf
  - 3: #dbadff
  - 4: #ff887c
  - 5: #fbd75b
  - 6: #ffb878
  - 7: #46d6db
  - 8: #e1e1e1
  - 9: #5484ed
  - 10: #51b749
  - 11: #dc2127

Email Formatting

- Prefer plain text. Use `--body-file` for multi-paragraph messages (or `--body-file -` for stdin).
- Same `--body-file` pattern works for drafts and replies.
- `--body` does not unescape `\n`. If you need inline newlines, use a heredoc or `$'Line 1\n\nLine 2'`.
- Use `--body-html` only when you need rich formatting.
- HTML tags: `<p>` for paragraphs, `<br>` for line breaks, `<strong>` for bold, `<em>` for italic, `<a href="url">` for links, `<ul>`/`<li>` for lists.
- Example (plain text via stdin):

  ```bash
  gog gmail send --to recipient@example.com \
    --subject "Meeting Follow-up" \
    --body-file - <<'EOF'
  Hi Name,

  Thanks for meeting today. Next steps:
  - Item one
  - Item two

  Best regards,
  Your Name
  EOF
  ```

- Example (HTML list):
  ```bash
  gog gmail send --to recipient@example.com \
    --subject "Meeting Follow-up" \
    --body-html "<p>Hi Name,</p><p>Thanks for meeting today. Here are the next steps:</p><ul><li>Item one</li><li>Item two</li></ul><p>Best regards,<br>Your Name</p>"
  ```

Notes

- Set `GOG_ACCOUNT=you@gmail.com` to avoid repeating `--account`.
- For scripting, prefer `--json` plus `--no-input`.
- Sheets values can be passed via `--values-json` (recommended) or as inline rows.
- Docs supports export/cat/copy. In-place edits require a Docs API client (not in gog).
- Confirm before sending mail or creating events.
- `gog gmail search` returns one row per thread; use `gog gmail messages search` when you need every individual email returned separately.


Let me check Gmail for urgent/unread emails from work account (mark@forgeco.com). It's a weekend, so I should focus on urgent items only.

exec

gog gmail search 'in:inbox is:unread newer_than:1d' --max 15 --account mark@forgeco.com --json 2>/dev/null | head -200


Following up on quote #33

UNREAD Notification CATEGORY_UPDATES INBOX

for Mark

UNREAD [Superhuman]/AI/Respond [Superhuman]/AI/Pitch CATEGORY_SOCIAL Outreach INBOX

"'Diane Pichetti' via Forge Project Team" <projects@forgeco.com>

Pick Crane Service - invoice One Tannery Brook Row Somerville job date 02/09/2026

UNREAD Notification CATEGORY_FORUMS INBOX [Superhuman]/AI/Projects

Forge: Masi - Project Coordinator Introduction - Erica Daniel

Gmelius/Shared
Notification
Gmelius/Shared/Unassigned
Erica D Projects

Replit Series D - Intro

CATEGORY_PROMOTIONS UNREAD [Superhuman]/AI/Respond Notification [Superhuman]/AI/Pitch INBOX

Connecteam | your upcoming invoice

Your receipt from Anthropic, PBC #2498-5194-6981

Following up on quote #33

for Mark

Replit Series D - Intro

Connecteam | your upcoming invoice


Let me review the emails:

1. **Reservoir (Jobber) - Quote #33 follow-up**: Automated notification, not urgent
2. **Ella Mason - "for Mark"**: This is tagged as Pitch/Outreach — likely cold outreach/spam
3. **Diane Pichetti / Forge Project Team - Pick Crane Service invoice**: A project-related invoice from 02/09/2026 — could be relevant for business ops
4. **Forge/heatpumps - Masi Project Coordinator Introduction**: Internal project notification, Erica D Projects label — not urgent weekend stuff
5. **Anush Gaur - Replit Series D - Intro**: This says "Replit Series D - Intro" — could be an investor intro or something interesting. Tagged as Pitch.

6. **Connecteam - upcoming invoice**: Automated invoice notice, not urgent
7. **Anthropic receipt**: Automated receipt

Looking at what stands out, the Pick Crane Service invoice and the Replit Series D intro are the most potentially interesting — one's operational, the other could be a networking opportunity. But honestly, nothing here feels urgent for a Sunday morning; there's no board communication, investor emails, or messages from direct reports.

So I should just flag the genuinely noteworthy items without making a big deal out of it. The Replit Series D intro is worth noting since it's tied to their fundraising, but I'll keep this brief.

Nothing P0, but a couple worth a quick note. Updating state and flagging.

write

Checked 2026-02-22 07:33 ET — nothing urgent. Flagged crane invoice + Replit intro to Mark.


Successfully wrote 301 bytes to /Users/mark/.openclaw-markbot/workspace/memory/heartbeat-state.json


Let me give Mark a brief, non-alarmist weekend morning summary. Nothing is truly urgent, but a couple things are worth a quick mention.

Good morning — nothing urgent, but two things from yesterday worth a glance when you're up:

**📄 Project invoice** — Diane Pichetti forwarded a Pick Crane Service invoice for the Tannery Brook Row / Somerville job (job date 02/09). Two messages in that thread, tagged under Projects.

**🤝 Replit Series D intro** — Someone named Anush Gaur (ESSEC) sent a cold intro about Replit's Series D. Could be noise, could be interesting depending on your appetite.

Everything else is automated: Connecteam invoice notice, Anthropic receipt, Jobber quote follow-up, and cold outreach. Nothing that needs you today.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 09:39 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=morning; 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 — 9:39 AM (America/New_York)


Last email check was at 1740220380 which is 07:33 ET. Current time is 09:39 ET. That's about 2 hours since the last check. The rule says "check if > 2 hours since last." It's been exactly about 2 hours 6 minutes. Weekend rules: email only, no calendar nags.

Let me check if there's anything new since 07:33 ET.

exec

gog gmail search 'in:inbox is:unread newer_than:3h' --max 10 --account mark@forgeco.com --json 2>/dev/null


Mark Question

[Superhuman]/AI/Meeting UNREAD [Superhuman]/AI/Respond [Superhuman]/AI/Pitch CATEGORY_SOCIAL Outreach INBOX

Re: Service visit

UNREAD [Superhuman]/AI/Respond Notification CATEGORY_FORUMS INBOX [Superhuman]/AI/Projects

Rippling Spend Management <spendmanagement@rippling.com>

6 Card Transaction(s) need receipts

RESCHEDULED: Tracy Palandjian on Investing in Opportunity: A Conversation with David Rubenstein

CATEGORY_PROMOTIONS UNREAD Notification [Superhuman]/AI/News INBOX

Mark Question

Re: Service visit

6 Card Transaction(s) need receipts


Let me review:

1. **Jolie Farrell - "Mark Question"** from profitpathlite.info — tagged as Pitch/Outreach. Cold outreach, not urgent.
2. **Jonathan Weinstock - "Re: Service visit"** — This is a reply to a service visit thread, tagged as Projects. This could be a customer. Worth flagging.
3. **Rippling - 6 card transactions need receipts** — Automated, not urgent for a Sunday.
4. **Social Finance - RESCHEDULED event** — Newsletter/promo, not urgent.

The only one worth flagging is Jonathan Weinstock's reply about a service visit.

I already handled the flagging this morning, so Jonathan Weinstock's reply is the only new actionable item here. I'll update the state and note it.

write

Checked 2026-02-22 09:39 ET — one customer reply flagged (Weinstock, service visit).


Successfully wrote 294 bytes to /Users/mark/.openclaw-markbot/workspace/memory/heartbeat-state.json


Just one noteworthy new email. I'll keep the update brief.

One new thing worth noting:

**📬 Jonathan Weinstock replied** (8:05 AM) — thread subject "Re: Service visit." Looks like a customer following up on a job. Worth a read when you get a chance.

Rest is noise — cold outreach, Rippling receipt reminders, a rescheduled event notification. Nothing urgent.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 11:51 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=morning; 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": "88c13d40-d4ae-4b02-9e5b-d42dbe8cda58",
  "sender": "gateway-client"
}
```

[Sun 2026-02-22 11:51 EST] ping


Mark just said "ping" - this is a direct message from him, not a heartbeat. He's just checking in or saying hi. I should respond casually.

Pong. What's up?


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 11:51 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=morning; 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": "1f7b2a0b-db1e-4d0d-a006-cead9d8d502f",
  "sender": "gateway-client"
}
```

[Sun 2026-02-22 11:51 EST] Are you able to access the LifeOS project on this computer


Mark is asking about a "LifeOS project" on his computer. Let me look around to see if there's such a project in his workspace or repo.

exec

find /Users/mark -maxdepth 4 -type d -iname "*lifeos*" -o -type d -iname "*life-os*" 2>/dev/null | head -20


Command still running (session dawn-shoal, pid 31560). Use process (list/poll/log/write/kill/clear/remove) for follow-up.


Let me also search for LifeOS in common locations while that's running.


(no new output)

Process still running.


The find command is taking long. Let me try a more targeted search in common locations.


Termination requested for session dawn-shoal.


Let me search in more specific locations.

ls /Users/mark/Documents/ 2>/dev/null | grep -i life; ls /Users/mark/ 2>/dev/null | grep -i life; ls /Users/mark/Desktop/ 2>/dev/null | grep -i life; ls /Users/mark/Developer/ 2>/dev/null | grep -i life 2>/dev/null


LifeDataOS

(Command exited with code 1)

LifeDataOS

(Command exited with code 1)


Found it! There's a `LifeDataOS` folder in `/Users/mark/Documents/`. Let me explore it.


ls: /Users/mark/Documents/LifeDataOS/: No such file or directory

(Command exited with code 1)


Hmm, it showed in the output of `ls /Users/mark/Documents/` but now it doesn't exist? Let me check again.


03015A24-50DE-4BC3-AA21-6B662E9145EC.pdf
073DC001-8E8E-414C-9BD2-58AD0D0FDED9.pdf
0F2E0B19-B1D7-4C1F-8A2D-090256E03F75.pdf
1278527A-C983-4E96-B279-5A79210FE6D3.pdf
2014 Intrepid Pursuits- Tax Return USE THIS ONE.pdf
2015 Org Chart.pdf
2016 BS.pdf
2016 P&L.pdf
2020-04-05 06.37.42.jpg
2022 By Qtr- Mgmt prepared.pdf
2022.05 Unearnead Reno.xlsx
20220409_080014.jpg
2023.06 Management financials.xlsx
20241021 - Octopus Energy Group & Forge Team NDA.pdf
3P Jobs Detail for FY23W04.pdf
533A4CA3-F97B-474D-838E-436F4612D207.pdf
5ED14A8D-D443-4916-AE60-C2B9E5F22804.pdf
6DBF47B3-2814-410C-9341-24DA010A91C2.pdf
8 Bennett, Proposed Septic Tie In Plan (APPROVABLE) • mark@forgeco.com.pdf
99DFE82B-AC27-4C6E-BD99-6F1BEA483709.pdf
99FB980A-F982-4871-8AC9-632A7DC00D55.pdf
A17552B7-9C46-4F33-B65B-AA89C62A9D7C.pdf
AI Productivity Detail File FY18_02-15-2018_all AI.zip
Abridged Draft - Weekly Data Report FY23W02.pdf
BOARD_Q1_2022 - Final.key
BW Headshots (1).key
BW Headshots.key
Boarding Pass - Print your boarding pass - American Airlines.pdf
Calare - 66 Rowe Street - Lease (3).pdf
City Hall Systems - eBilling ePayment.pdf
Claude.pdf
CloudApp Failed Uploads
D42A96D6-8534-48AA-A034-F4EB880DA6AC.pdf
Dashboard.png
Digital Products Planning Deck.key
Document_2021-06-14_084406.pdf
Documents - AMAC02SQ0D2H03T
Documents - AMAC02SQ0D2H03T - 1
Documents - Mark’s Mac Studio
Documents - Mark’s MacBook Pro
Documents - Mark’s MacBook Pro (2)
Draft Weekly Data Report FY23W03 (1).pdf
Eli Lilly|intrepid MSA.pdf
FDEEFAD2-8D5B-406D-A2A4-F16A23985573.pdf
FORGE_BOARD.key
FORGE_windows_campaign_v2.pdf
Feb 2022 New England Labor Bill - Vinyl.xlsx
Feb 2022 New England Labor Bill - Wood.xlsx
File (1).jpg
File (2).jpg
File.jpg
Financials 2014 - 2016 - External (Autosaved).xlsx
Forge - Balance Sheet - 2021.pdf
Forge - Income Statement - 2021.pdf
Graduating Apprentice Benefits.pdf
HIARCS Chess
Houses and Architectual Plans
IMG_0881.mov
IMG_20220729_081929.jpg
IMG_2746.jpg
Image from iOS (1).jpg
Image from iOS (2).jpg
Image from iOS (3).jpg
Image from iOS.jpg
Intrepid 3QF Review Deck vfeb20a.pptx
Intrepid 3QF Review Deck vfeb21.pptx
Intrepid Homes - 3 Year Pro Forma.pdf
Intrepid Org Chart.pdf
Intrepid Proposed Rate Card Worksheet_6.30.15.numbers
Intrepid Pursuits LLC_ProfitandLoss-1 (Autosaved).xlsx
Intrepid Pursuits LLC_ProfitandLoss-2 (Autosaved).xlsx
Intrepid Pursuits Mail - Your Oculus Rift Sales Invoice.pdf
Intrepid Revenue.ograph
IntrepidOne_Overview .pptx
Jameson Draft Powerpoint - KathysEdits - V1 copy.key
Jameson Draft Powerpoint - Original received 05-11-21.pptx
January 2022 - Apprentice Class.HEIC
Kasdorf, Mark_USA_MD_Non Compete Denial (Autosaved).docx
L1020403.jpg
Leadership Org Chart.key
Long Bath Aging AR - A R Aging Detail (1).pdf
Long Bath Aging AR - A R Aging Detail.pdf
MDMA_First_Time_User_Guide.png
Macbook Stuff
Naming framework.pdf
New COA Mapping.xlsx
OODA Loops.png
Obsidian Vault
PI - Dan Rzasa.pdf
Pro App.png
Q2 Financials (Estimated).numbers
Scheduler.png
Screen Shot 2022-01-15 at 9.34.31 AM.png
Screen Shot 2022-01-19 at 9.11.01 AM.png
Screen Shot 2022-01-19 at 9.11.34 AM.png
Screen Shot 2022-01-19 at 9.12.15 AM.png
Screen Shot 2022-01-19 at 9.12.47 AM.png
Screen Shot 2022-07-29 at 1.38.52 PM.png
Screen Shot 2023-10-11 at 11.57.18 AM.png
Sprint Plans - Devs.pdf
SynthBeeInc-Form of Unilateral NDA-Jan-2024-RA1 (1).pdf
Tax Docs
Untitled 16.soulver
Untitled 17.soulver
Untitled 20.soulver
Untitled 21.soulver
Untitled 23.soulver
Watt_William_BehavioralOverview.pdf
WebEx
Website Reskin.png
Weekly Data Report 07.22.2022.pdf
Weekly Data Report 08-26-2022.pdf
Weekly Data Report 08.15.2022 (1).pdf
Weekly Data Report 10.31.2022 (1).pdf
Weekly Data Report FY22W45.pdf
Weekly Data Report FY22W47.pdf
Weekly Data Report FY22W50.pdf
Weekly Data Report Q4W5Y22.pdf
Weekly Data Report.pdf
Wolfenoot_ONLINE.pdf
Zoom
apple-card-statement-2019-11.numbers
budget to actual 4.30.22.xlsx
chessdata
dan king card use.pdf
draft P&L (1).pdf
draft P&L.pdf
draft balance sheet.pdf
draft.pdf
image (1).png
image (2).png
image.png
kasdorf auto.pdf
q2 Goals.xlsx
summary findings-brex.pdf


I see there's an "Obsidian Vault" directory. There might be a LifeOS project there. Let me check. I also see "Documents - Mark's Mac Studio" which might be a iCloud folder. The original grep showed "LifeDataOS" but it seems to have been from the `ls /Users/mark/Documents/` command. Let me look for it more carefully, maybe it's a directory within Documents.

Actually wait, looking at the output again - the grep showed "LifeDataOS" from `ls /Users/mark/Documents/` but now I can't see it in the full listing. That's odd. Maybe it was showing something from another part of the compound command.

The compound command ran multiple grep searches across different directories, so "LifeDataOS" could have come from `/Users/mark/` itself rather than Documents. The exit code 1 at the end was just from the Developer directory not existing. Let me trace back which directory actually contained that match.

ls /Users/mark/ | grep -i life; ls /Users/mark/Desktop/ 2>/dev/null | grep -i life


So "LifeDataOS" is in `/Users/mark/` itself. Let me explore it.


total 16
drwxr-xr-x   4 mark  staff   128 Jan  3 16:50 .
drwxr-x---+ 84 mark  staff  2688 Feb 22 11:45 ..
-rw-r--r--@  1 mark  staff  6148 Jan  4 09:25 .DS_Store
drwxr-xr-x@ 55 mark  staff  1760 Jan 19 14:36 personal-os


Found a `personal-os` directory! Let me explore it.


AUDIT_ANSWERS.md
CHANGELOG.md
DATA_HYGIENE.md
DEVELOPER_SETUP.md
DOCUMENTATION_INDEX.md
FINAL_SETUP_REPORT.md
LIFESTYLE_MODULES.md
METRICS_FEATURE.md
MULTIUSER_ARCHITECTURE.md
OKR_2026_SCHEMA.md
PROJECT_SUMMARY.md
QA_REPORT.md
QUICKSTART.md
README.md
READY_FOR_CONTENT_PHASE.md
REFACTOR_REPORT.md
SCHEMA.md
SETUP_STATUS.md
START_HERE.md
Spreadsheet to JSON for Cursor - Spreadsheet to JSON for Cursor.csv
TESTING.md
VALIDATION_LOGIC.md
VERIFICATION_CHECKLIST.md
audit_data_integrity.py
audit_edge_cases.py
audit_llm_readiness.py
audit_performance.py
config
core
daily_log_demo.py
example.py
hcp_list_jobs.py
personal.db
plugins
pytest.ini
requirements.txt
run_all_audits.py
run_tests.sh
scripts
test_2026_schema.py
test_catch_up.py
test_data_pipeline.py
test_lifestyle_modules.py
test_metrics.py
test_periodic_reviews.py
tests
user_config.json
venv
verify_setup.sh


That's a substantial project! Let me look at the README or START_HERE to understand what this is.

Found it — `/Users/mark/LifeDataOS/personal-os/`. Looks like a pretty built-out project. Let me get the lay of the land:


# 🚀 START HERE - Personal OS Quick Start

## You're in the Right Place!

You're in `/Users/mark/LifeDataOS/personal-os` - this is the correct directory.

## Run the App (3 Steps)

### Step 1: Verify Setup

```bash
./verify_setup.sh
```

**Expected:** You should see `✅ ALL CHECKS PASSED`

### Step 2: Run the App

```bash
.venv/bin/streamlit run daily_log_demo.py
```

**Expected:** Browser opens at `http://localhost:8501`

### Step 3: Start Logging!

- Click "☀ Morning" tab → Enter intention → Save
- Click "🌙 Log" tab → Fill in modules → Save

---

## If Something Goes Wrong

### Problem: "No such file or directory: .venv/bin/streamlit"

**Solution:** You might be in the wrong folder.

```bash
# Check where you are
pwd

# Should show: /Users/mark/LifeDataOS/personal-os
# If not, navigate there:
cd /Users/mark/LifeDataOS/personal-os

# Then run the verification
./verify_setup.sh
```

### Problem: Verification fails

**Solution:** Rebuild the environment (takes 30 seconds):

```bash
# 1. Delete old environment
rm -rf .venv

# 2. Create fresh environment
python3 -m venv .venv

# 3. Install packages
.venv/bin/pip install sqlmodel streamlit sqlalchemy pytest

# 4. Verify
./verify_setup.sh
```

---

## Command Reference

```bash
# Run app
.venv/bin/streamlit run daily_log_demo.py

# Initialize database (first time only)
.venv/bin/python example.py

# Run tests
.venv/bin/pytest tests/ -v -s

# Verify setup
./verify_setup.sh

# Check installed packages
.venv/bin/pip list
```

---

## Directory Structure

```
personal-os/                    ← YOU ARE HERE
├── .venv/                      ← Virtual environment
├── daily_log_demo.py           ← Main app file
├── plugins/                    ← Plugin modules
│   └── daily_log/              ← Daily Log plugin
├── core/                       ← Core system
├── tests/                      ← Automated tests
├── verify_setup.sh             ← Setup verification script
└── START_HERE.md               ← This file
```

---

## Documentation

- **START_HERE.md** (this file) - Quick start
- **DEVELOPER_SETUP.md** - Complete setup guide
- **README.md** - Project overview
- **TESTING.md** - Test documentation
- **.cursorrules** - Coding standards

---

## Current Status

✅ **Environment:** Python 3.13.7 in `.venv`
✅ **Packages:** streamlit 1.52.2, sqlmodel 0.0.31, pytest 9.0.2
✅ **Database:** SQLite with WAL mode
✅ **Tests:** Automated test suite ready

---

## Next Steps

1. Run `./verify_setup.sh` to confirm setup
2. Run `.venv/bin/streamlit run daily_log_demo.py` to start app
3. Log your first day!
4. Read `DEVELOPER_SETUP.md` for advanced usage

---

**Questions?** Run `./verify_setup.sh` first - it checks everything automatically.


So this is a personal OS/daily logging app built with Streamlit and SQLite. It looks like a Python project with a Streamlit frontend. Let me check the current status and if it's running.

Yep, fully accessible. It's a Python/Streamlit personal logging app — looks like a "Personal OS" daily tracking system. Here's the quick summary:

**What it is:** A daily logging app (`daily_log_demo.py`) with morning intentions, nightly logs, and various lifestyle modules. SQLite database, runs in browser at `localhost:8501`.

**Status as of last setup (Jan ~19):**
- Python 3.13.7 venv ready
- Streamlit, SQLModel, SQLite all installed
- Tests and verification scripts in place

**To run it right now:**
```
cd /Users/mark/LifeDataOS/personal-os
.venv/bin/streamlit run daily_log_demo.py
```

Want me to fire it up? Or is there something specific you're trying to do with it?


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 11:54 (America/New_York, UTC-05:00)
Derived: weekend=true; part_of_day=morning; 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": "c8db7ecc-caaa-4f65-a3bd-4547cc79e7d1",
  "sender": "gateway-client"
}
```

[Sun 2026-02-22 11:54 EST] Is there any kind of backlog or documentation for what we last did and what was net in the project? If we wanted to serve this as an app expereince that would be availbe on our phones, could we do this locally from the mac or do we really need to set it up on a server? We have a droplet we could shift this to. Is this an example of an electron app? Would we need to change archtecture if we wanted that?


Mark is asking several questions about the LifeDataOS project:
1. Is there backlog/documentation for what was last done and what's next?
2. Could they serve it as a mobile-accessible app locally from Mac, or would they need a server?
3. Is this an Electron app?
4. Would architecture need to change for Electron?

Let me read through some of the documentation files to answer these questions. I'll start with the README, PROJECT_SUMMARY, CHANGELOG, and SETUP_STATUS to understand the project state.

Let me dig into the docs to answer all of this properly.


# Personal OS - Daily Log

> *A stream-of-consciousness data logger for High Performance Living*

**Version:** 1.0 Master Schema
**Status:** 🟢 Production Ready

---

## Overview

Personal OS is a sophisticated daily logging system designed for high performers who want to optimize their life through data. Built with Python, SQLite, and Streamlit, it combines the speed of stream-of-consciousness logging with the power of structured analytics.

### Philosophy

Instead of generic "How was your day?" questions, Personal OS tracks **specific, actionable variables** that correlate with performance:

- Not "Mood?" but "Connection Score (1-5)?"
- Not "Sleep well?" but actual sleep metrics imported from devices
- Not "Productive?" but "Which of your 3 goals did you complete?"

This creates **high-resolution data** that AI can analyze for real patterns.

---

## Key Features

### 🌉 Morning/Evening Bridge
- **Morning Tab**: Close out yesterday + Plan today (2-day view)
- **Evening Tab**: Complete review with all metrics
- **Dual-day logic**: Retroactive data (yesterday) + Forward-looking (today)

### 🧠 Self-Learning Lists
- Add "Nicotine" once → appears in dropdown automatically next time
- Scans last 30 days of logs for custom items
- Zero configuration needed

### 🎯 Sprint Planning Integration
- Set "Big 3 Goals" every morning
- Review completion every evening
- Track productivity patterns over time

### 📊 Hybrid Schema
- **Core columns**: Numeric scores (1-5) for fast SQL analytics
- **JSON metrics**: Complex data (lists, dicts) for flexibility
- **No migrations needed**: Add new tracking without schema changes

### 🔒 Multi-Device Safety
- Partial updates prevent data loss
- Morning on phone + Evening on desktop = safe
- Pre-fills prevent stale overwrites

---

## Quick Start

```bash
cd personal-os

# Verify setup
./verify_setup.sh

# Run the app
.venv/bin/streamlit run daily_log_demo.py
```

**Opens at:** http://localhost:8501

---

## Installation

### First Time Setup

```bash
# Clone/download the project
cd personal-os

# Create virtual environment
python3 -m venv .venv

# Install dependencies
.venv/bin/pip install -r requirements.txt

# Initialize database
.venv/bin/python example.py

# Run the app
.venv/bin/streamlit run daily_log_demo.py
```

### Existing Setup

```bash
# Just run
.venv/bin/streamlit run daily_log_demo.py
```

---

## What You Can Track

### Morning Briefing ☀

**Tonight's Plans:**
- Together/apart logic with conditional planning checks
- Critical OKR: "Did I plan my own evening?"

**Big 3 Goals:**
- Set your top 3 priorities for the day
- Review completion in evening

**Yesterday's Habits:**
- Reading (12 Books/year goal)
- Supplements (AG1, Creatine)

### Evening Review 🌙

**Exercise:**
- Workout? → RPE (1-5)
- Sore? → Soreness level (1-5)
- NULL if didn't workout (not 0!)

**Stimulants:**
- Caffeine (mg) + timing
- Alcohol (units)
- Medications (Adderall, Modafinil) with doses

**Productivity:**
- Which goals completed?
- Side project work/releases
- Content creation
- Chess study minutes

**Scores (1-5):**
- Connection, Presence, Partner Mood
- Edgy/Anxious, Stress
- Satisfaction, Productivity

**Reflection:**
- Gratitude
- Free-form notes

---

## Architecture

```
┌─────────────────────────────────────────────────────────┐
│                    Streamlit UI                         │
│  (Morning Briefing + Evening Review)                   │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│                   SQLModel ORM                          │
│  (DailyLog model with hybrid schema)                   │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│             SQLite Database (WAL mode)                  │
│  Core: 15 numeric columns | JSON: 20+ metrics          │
└─────────────────────────────────────────────────────────┘
                         ↓
┌─────────────────────────────────────────────────────────┐
│              LLM Analysis Tools                         │
│  (Markdown export, Trends, Correlations)               │
└─────────────────────────────────────────────────────────┘
```

### Tech Stack

- **Python 3.13+**: Modern Python with type hints
- **SQLModel 0.0.31**: Pydantic + SQLAlchemy ORM
- **Streamlit 1.52**: Interactive web UI
- **SQLite with WAL**: Concurrent access, zero config

---

## Data Flow

### Morning (7:00 AM)
1. Open app
2. Answer: "Together tonight?" (with conditional logic)
3. Set Big 3 Goals for today
4. Review yesterday's reading and supplements
5. Save (5 minutes)

### Evening (9:00 PM)
1. Open app
2. Log exercise (if applicable)
3. Track stimulants and substances
4. Review goal completion
5. Rate all scores (1-5 sliders)
6. Write gratitude and notes
7. Save (10 minutes)

### Analysis (Weekly)
```python
from plugins.daily_log.tools import get_trends, get_summary

# Get 30-day trends
trends = get_trends(user_id, days=30)

# Ask LLM: "What patterns do you see in my data?"
```

---

## File Structure

```
personal-os/
├── core/                      # Core system
│   ├── database.py            # SQLite + WAL mode
│   ├── users.py               # User management
│   └── loader.py              # Plugin system
│
├── plugins/
│   └── daily_log/             # Daily Log plugin
│       ├── models.py          # DailyLog SQLModel (hybrid schema)
│       ├── ui.py              # Streamlit interface (refactored)
│       ├── tools.py           # LLM export functions
│       └── README.md          # Plugin documentation
│
├── tests/                     # Automated test suite
│   ├── conftest.py            # pytest fixtures
│   └── test_simulation.py    # UI simulation tests
│
├── .venv/                     # Virtual environment (use this!)
├── .cursorrules               # AI coding standards
├── daily_log_demo.py          # Main application
├── requirements.txt           # Locked dependencies (47 packages)
├── verify_setup.sh            # Setup verification script
│
├── README.md                  # This file
├── DEVELOPER_SETUP.md         # Complete setup guide
├── SCHEMA.md                  # Data dictionary
├── OKR_2026_SCHEMA.md         # OKR mapping
└── REFACTOR_REPORT.md         # Code quality report
```

---

## Documentation

| File | Purpose |
|------|---------|
| **README.md** | Project overview (this file) |
| **SCHEMA.md** | Complete data dictionary with field meanings |
| **DEVELOPER_SETUP.md** | Setup instructions and troubleshooting |
| **OKR_2026_SCHEMA.md** | How schema maps to 2026 goals |
| **REFACTOR_REPORT.md** | Code quality improvements |
| **.cursorrules** | AI coding standards |

---

## Use Cases

### 1. Relationship Optimization
Track connection, presence, and partner mood daily. Identify patterns:
- When does connection peak?
- What activities correlate with high presence?
- Nights apart: Are they planned? Do I use the time well?

### 2. Productivity Analysis
Set 3 daily goals, track completion:
- What's my goal completion rate?
- Which types of goals get done?
- What correlates with high productivity?

### 3. Substance Optimization
Track caffeine timing, alcohol, medications:
- Optimal Adderall dosing
- Caffeine cutoff time for good sleep
- Alcohol impact on next-day metrics

### 4. Reading Goal (12 Books/Year)
Track daily reading habit:
- Am I reading most days?
- Is "read before bed" improving sleep?
- Current pace toward 12 books?

### 5. Mental Health Monitoring
Track anxiety, stress, satisfaction:
- Baseline: Edgy/Anxious = 1
- Alerts: When anxiety spikes to 3+
- Patterns: What triggers stress?

---

## LLM Analysis Examples

### Query 1: Productivity Patterns
```python
from plugins.daily_log.tools import get_trends

trends = get_trends(user_id, days=30)

# Ask LLM:
"""
Analyze my 30-day productivity data.
Find correlations between:
- Goal completion rate
- Caffeine intake
- Sleep quality (if imported)
- Adderall usage
"""
```

### Query 2: Relationship Insights
```python
# Ask LLM:
"""
Analyze my relationship scores over 60 days.
Questions:
- When is connection highest?
- Does "own evening planned" correlate with satisfaction?
- What activities boost presence?
"""
```

### Query 3: Optimization Recommendations
```python
# Ask LLM:
"""
Based on my data:
1. What's my optimal caffeine cutoff time?
2. When should I take Adderall for max productivity?
3. How does alcohol impact next-day performance?
4. What habits correlate with high satisfaction?
"""
```

---

## Project Status

### Completed ✅
- [x] Core system with WAL mode database
- [x] User management and seeding
- [x] Daily Log plugin with 2026 OKR schema
- [x] Self-learning dropdown lists
- [x] Morning/Evening dual-day interface
- [x] Hybrid schema (core + JSON)
- [x] NULL handling for exercise
- [x] Database safety with rollback
- [x] Helper functions for clean code
- [x] Automated test suite
- [x] Complete documentation
- [x] Verification scripts

### In Progress 🚧
- [ ] Sleep import from Oura/Whoop
- [ ] Dashboard with charts
- [ ] Weekly summary reports
- [ ] LLM-powered insights
- [ ] Mobile-friendly UI

### Future Enhancements 💡
- [ ] Additional plugins (Finance, Health, Time Tracking)
- [ ] Data export to CSV
- [ ] Correlation analysis tools
- [ ] Goal templates and streaks
- [ ] Multi-user support

---

## Contributing

### Making Changes

1. Follow `.cursorrules` (AI coding standards)
2. Run tests: `.venv/bin/pytest tests/ -v`
3. Verify setup: `./verify_setup.sh`
4. Update documentation

### Code Quality

- **Type hints required**: 100% coverage
- **Error handling required**: All database ops
- **Tests required**: For new features
- **Documentation required**: Update relevant .md files

---

## Support

### Documentation
- **Quick Start**: `START_HERE.md`
- **Full Setup**: `DEVELOPER_SETUP.md`
- **Data Schema**: `SCHEMA.md`
- **Testing**: `TESTING.md`

### Troubleshooting
- Run: `./verify_setup.sh`
- Check: `DEVELOPER_SETUP.md` → Troubleshooting section
- Review: `.cursorrules` for coding standards

### Questions
- Read the documentation first
- Check `SCHEMA.md` for data field meanings
- Review test files for usage examples

---

## License

Personal use only. This is your data, your insights, your optimization system.

---

## Credits

Built with:
- SQLModel by Sebastián Ramírez
- Streamlit by Streamlit Inc.
- SQLite by D. Richard Hipp

Designed for high performers who believe in data-driven self-optimization.

---

**Start logging. Find patterns. Optimize your life. 🚀**

**Run:** `.venv/bin/streamlit run daily_log_demo.py`


# Changelog

All notable changes to Personal OS will be documented in this file.

## [2.0.0] - 2026-01-03

### BREAKING CHANGE - Lifestyle Modules Rewrite

Complete rewrite of Daily Log UI from generic sliders to specific lifestyle tracking modules.

#### UI Changes
- **Removed:** Generic sleep/readiness sliders (now imported from external devices)
- **Removed:** Generic stress/satisfaction sliders
- **Removed:** Simple habits/tags multiselects
- **Added:** 5 dedicated lifestyle modules with structured inputs

#### New Modules

**1. ☕ Stimulants Module**
- Dynamic multiselect with historical learning
- Track time and amount for each stimulant
- Defaults: Coffee, Adderall, Modafinil, Tea, Energy Drink

**2. 🍄 Recreational Drugs Module**
- Dynamic multiselect with historical learning
- Track time, amount, and context (Solo vs With Others)
- Defaults: Pot, 2CB, LSD, Mushrooms, MDMA

**3. 💑 Relationship Module (K)**
- Expectation vs reality tracking
- Automatic divergence detection
- Prompt for divergence reason when expectations don't match

**4. 📺 Content Consumption Module**
- Checkbox-triggered tracking
- Type selection (Movie, TV, YouTube, Other)
- Title input

**5. ❤ Intimacy Module**
- Checkbox-triggered tracking
- Time and orgasm count inputs
- Simple, discrete tracking

#### Backend Changes
- Added `get_historical_list()` function to scan past logs
- All module data stored in `metrics` JSON column
- Updated `get_log()` to format lifestyle modules in markdown
- Updated `get_trends()` to include lifestyle metrics
- Updated `get_summary()` with lifestyle statistics
- Added `get_correlations()` for pattern analysis

#### Documentation
- Created `LIFESTYLE_MODULES.md` with comprehensive guide
- Updated `CHANGELOG.md` with breaking changes
- Included analysis examples and best practices

#### Migration Notes
- No database migration needed (uses existing `metrics` column)
- Old data remains intact but isn't displayed in new UI
- To access old habit/tag data, query `habits` and `tags` columns directly

---

## [1.1.0] - 2026-01-03

### Added - Conditional Metrics Feature (DEPRECATED)

#### Database
- Added `metrics` JSON column to `DailyLog` model
- Type: `Dict[str, Any]` for flexible storage
- Supports unlimited custom metrics without schema changes

#### UI
- Added `METRIC_CONFIG` dictionary for defining conditional questions
- Dynamic rendering: Questions appear only when relevant habits/tags selected
- Pre-configured metrics:
  - **Sex** → Orgasm count
  - **Adderall** → Dosage (mg)
  - **Workout** → Duration (minutes)
  - **Cold Plunge** → Temperature (°F)
  - **Alcohol** → Number of drinks
  - **Sauna** → Duration (minutes)
- Automatic pre-filling from previous entries
- Helpful caption when no metrics triggered

#### Tools/Export
- Updated `get_log()` to include metrics in markdown export
- Human-readable formatting (e.g., "2 orgasms, 45 mins workout")
- Metrics appear in Evening section of exported logs

#### Documentation
- Created `METRICS_FEATURE.md` with full documentation
- Updated `plugins/daily_log/README.md` with metrics section
- Added `test_metrics.py` for programmatic testing

### Technical Details
- Maintains partial update safety (metrics part of evening update)
- Zero database migrations needed for adding new metrics
- Configuration-driven: Just add to `METRIC_CONFIG`
- LLM-friendly output format

---

## [1.0.0] - 2026-01-03

### Initial Release

#### Core System
- SQLite database with WAL mode for concurrency
- BaseModel with UUID primary keys and UTC timestamps
- User management with seeding function
- Dynamic plugin loader system

#### Daily Log Plugin
- Morning check-in: Sleep quality, Readiness, Intention
- Evening reflection: Stress, Satisfaction, Gratitude, Notes
- Habits and tags tracking with smart multiselects
- Multi-device safety through partial updates
- Date picker for backfilling entries
- LLM-friendly markdown and JSON exports
- Streamlit UI with tabbed interface

#### Documentation
- Comprehensive README
- Quick start guide
- Plugin development guide
- Example plugin template


# Personal OS - Project Summary

## Overview

A modular Personal OS built in Python with SQLModel, SQLite, and Streamlit. Features a plugin architecture for extensibility and a production-ready Daily Log plugin.

## Complete File Structure

```
personal-os/
├── core/                          # Core system functionality
│   ├── __init__.py
│   ├── database.py                # SQLite + SQLModel with WAL mode
│   ├── users.py                   # User model and seeding
│   └── loader.py                  # Dynamic plugin discovery
│
├── plugins/                       # Plugin directory
│   ├── __init__.py
│   │
│   ├── daily_log/                 # Daily Log Plugin (PRODUCTION)
│   │   ├── __init__.py
│   │   ├── models.py              # DailyLog SQLModel with partial updates
│   │   ├── ui.py                  # Streamlit interface with tabs
│   │   ├── tools.py               # LLM-friendly data exports
│   │   └── README.md              # Plugin documentation
│   │
│   └── example_plugin/            # Example plugin template
│       ├── __init__.py
│       ├── ui.py
│       └── tools.py
│
├── example.py                     # Core system demo
├── daily_log_demo.py              # Daily Log Streamlit app
├── requirements.txt               # Python dependencies
├── .gitignore                     # Git ignore rules
├── README.md                      # Main documentation
├── QUICKSTART.md                  # Quick start guide
└── PROJECT_SUMMARY.md             # This file
```

## Implementation Checklist ✅

### Core System
- ✅ Database with WAL mode for concurrency
- ✅ BaseModel with UUID primary keys and UTC timestamps
- ✅ User model and seed function
- ✅ Dynamic plugin loader
- ✅ Example plugin template

### Daily Log Plugin
- ✅ DailyLog model with unique constraint on (user_id, date)
- ✅ Partial update functions (morning/evening separate)
- ✅ Morning section: sleep_score, readiness, intention
- ✅ Evening section: stress, satisfaction, gratitude, notes
- ✅ JSON columns for habits and tags
- ✅ Smart multiselect with defaults + history + custom creation
- ✅ Streamlit UI with date picker for backfilling
- ✅ Multi-device safety through partial updates
- ✅ Pre-filling from database to prevent overwrites
- ✅ LLM-friendly markdown export (get_log)
- ✅ Time series JSON export (get_trends)
- ✅ Summary generation with statistics

### Documentation
- ✅ Main README with architecture overview
- ✅ Daily Log plugin README
- ✅ Quick start guide
- ✅ Code comments and docstrings
- ✅ Example usage scripts

### Quality Assurance
- ✅ No linter errors
- ✅ Type hints throughout
- ✅ Error handling in database operations
- ✅ Session management (proper open/close)
- ✅ Input validation (1-10 constraints)

## Key Features

### 1. Database Architecture
- **SQLite with WAL mode**: Enables concurrent reads and writes
- **UUID primary keys**: No ID conflicts across devices
- **UTC timestamps**: Consistent time handling
- **JSON columns**: Flexible storage for lists (habits, tags)

### 2. Plugin System
- **Dynamic discovery**: Automatically finds plugins in /plugins
- **Modular design**: Each plugin is self-contained
- **UI + Tools**: Plugins can provide both interfaces and data tools
- **No core changes needed**: Add plugins without modifying core code

### 3. Daily Log Plugin

#### Multi-Device Safety
The most critical feature: **partial updates prevent data loss**

Example scenario:
1. 7:00 AM - Log morning on phone (Sleep: 8, Readiness: 7)
2. 8:00 AM - Open app on desktop (stale empty data)
3. 9:00 PM - Log evening on desktop

Result: Evening save only updates evening fields, morning data preserved.

Implementation:
```python
# update_morning() only touches these fields:
- sleep_score_subjective
- readiness
- morning_intention

# update_evening() only touches these fields:
- stress_level
- satisfaction
- gratitude
- notes
- habits
- tags
```

#### Smart Habits & Tags
- **Defaults**: Pre-populated common options
- **History**: Shows options used in last 30 days
- **Creation**: Text input allows adding new options on-the-fly

#### LLM Integration
**Markdown Export** (for context/analysis):
```markdown
# Daily Log - 2026-01-03

## Morning
- Sleep: 7/10
- Readiness: 8/10
- Intention: Focus on deep work

## Evening
- Stress: 3/10
- Satisfaction: 8/10
- Habits: [Workout, Meditation]
```

**JSON Export** (for trends/graphing):
```json
[
  {
    "date": "2026-01-01",
    "sleep_score": 8,
    "readiness": 7,
    "stress_level": 3,
    "satisfaction": 8
  }
]
```

## Technical Decisions

### Why SQLModel?
- Combines Pydantic validation with SQLAlchemy power
- Type-safe database operations
- Easy to understand for Python developers
- Great for rapid prototyping

### Why WAL Mode?
- Enables concurrent reads while writing
- Critical for multi-device usage
- Prevents "database locked" errors
- Better performance than default journaling

### Why Partial Updates?
- Data safety across devices/sessions
- Prevents race conditions
- Natural separation of morning/evening workflows
- More flexible than full record updates

### Why JSON Columns?
- Simpler than many-to-many tables
- Easier to query and aggregate
- Perfect for flexible lists (habits, tags)
- Better performance for small lists

### Why Default Slider Value = 5?
- Prevents anchoring bias
- Forces active engagement
- Neutral starting point
- Better data quality than "smart defaults"

## Usage Examples

### Basic Usage
```bash
# Initialize database
python example.py

# Run Daily Log UI
streamlit run daily_log_demo.py
```

### Programmatic Access
```python
from core.database import init_database
from core.users import seed_users
from plugins.daily_log.models import get_or_create_log, update_morning
from plugins.daily_log.tools import get_log, get_trends
from datetime import date

# Setup
init_database()
users = seed_users()
user_id = users['Me']

# Log data
log = get_or_create_log(user_id, date.today())
update_morning(log.id, sleep_score=8, readiness=7, intention="Focus today")

# Export for LLM
markdown = get_log(user_id, date.today())
trends = get_trends(user_id, days=14)
```

## Dependencies

```
sqlmodel>=0.0.14      # ORM with Pydantic validation
sqlalchemy>=2.0.0     # Database toolkit
streamlit>=1.30.0     # Web UI framework
```

## Future Enhancements

### Core System
- [ ] Authentication and multi-user support
- [ ] Plugin configuration system
- [ ] Plugin dependencies and versioning
- [ ] API layer (REST or GraphQL)

### Daily Log Plugin
- [ ] Correlation analysis (sleep vs satisfaction)
- [ ] Trend visualization with charts
- [ ] Habit streak tracking
- [ ] Weekly/monthly reports
- [ ] CSV export for external analysis
- [ ] AI-powered insights

### New Plugins
- [ ] Finance tracker
- [ ] Health metrics (weight, exercise, etc.)
- [ ] Goal tracking
- [ ] Journal/notes
- [ ] Task management
- [ ] Time tracking

## Performance Considerations

- **Database size**: SQLite handles millions of rows efficiently
- **Query optimization**: Indexes on user_id and date
- **JSON performance**: Fine for lists < 100 items
- **Streamlit caching**: Add @st.cache_data for expensive queries
- **WAL mode**: Checkpoint periodically for large databases

## Security Notes

- **Local only**: Currently designed for local/single-user use
- **No authentication**: Add before exposing to network
- **No encryption**: Consider encrypting sensitive fields
- **SQL injection**: SQLModel provides protection via parameterization
- **Backup strategy**: WAL files should be included in backups

## Testing Strategy

Manual testing checklist:
1. Create morning log on one device
2. Open app on another device (different browser)
3. Save evening log
4. Verify morning data intact
5. Add custom habit, verify it appears in future logs
6. Export markdown, verify format
7. Query trends, verify data structure
8. Backfill previous date, verify save

## Maintenance

- Keep dependencies updated (especially SQLAlchemy)
- Backup database regularly (personal.db + WAL files)
- Monitor database size (vacuum periodically if needed)
- Review and archive old logs (optional)

## Success Metrics

✅ **All requirements met**:
- Modular architecture with plugin system
- Production-ready Daily Log plugin
- Multi-device safety with partial updates
- LLM-friendly data exports
- Clean, documented codebase
- No linter errors
- Comprehensive documentation

## Project Status

**Status**: ✅ **COMPLETE**

All planned features implemented and tested. Ready for use.


Now I have a good understanding of the project. Let me answer Mark's questions:

1. **Backlog/documentation for what was last done**: Yes, there's a CHANGELOG (version history), README, PROJECT_SUMMARY, etc. The project went through v1.0 (basic daily log), v1.1 (conditional metrics), and v2.0 (lifestyle modules rewrite). Last major work was January 3, 2026.

2. **Mobile/phone access - local Mac vs server**: Streamlit apps run on a local port (8501). To access from your phone on the same WiFi, you'd need to:
   - Make sure Streamlit listens on `0.0.0.0` not just `localhost`
   - Find the Mac's local IP and navigate to `http://192.168.x.x:8501` from the phone

This works locally but for remote access you'd want either a server like the DigitalOcean droplet, a tunnel service, or a VPN back to the Mac—the droplet approach is cleanest since you'd just deploy the app there with a domain and optional auth.

3. **Is this Electron?**: No.

Electron is for building desktop apps with web tech, but this is a Python/Streamlit web app running in the browser—completely different. Converting to Electron would require rewriting the backend in Node.js and the UI in React/HTML/CSS, which is a major undertaking. For phone access, sticking with Streamlit on a droplet is the pragmatic choice, or eventually building a React Native app if you want a native mobile feel.

Now I'm formulating a clear answer for Mark that covers whether this architecture makes sense for his use case.

Good questions — let me cover all four:

---

**📋 Backlog / what was last done**

Yes, solid documentation exists. Here's the quick history:
- **Jan 3** — v1.0: Basic daily log (sleep, mood, habits)
- **Jan 3** — v1.1: Added conditional metrics (Adderall dose when you log it, workout duration, etc.)
- **Jan 3** — v2.0: Full rewrite into 5 lifestyle modules — Stimulants, Recreational Drugs, Relationship, Content Consumption, Intimacy

**What's still on the to-do list:**
- Sleep import from Oura/Whoop
- Charts & dashboard
- Weekly summary reports
- LLM-powered insights (the whole point)
- Mobile-friendly UI
- Finance, health, time-tracking plugins

So the logging infrastructure is solid. The analytics/insight layer hasn't been built yet.

---

**📱 Can you access it on your phone — local Mac vs Droplet?**

**Local Mac (same WiFi only):** Yes, this actually works with zero changes. Streamlit can listen on your Mac's local IP so any device on your home WiFi hits it. Not available when you're out of the house.

**Droplet:** Yes, and this is the right move if you want it available anywhere, anytime. It's a straightforward deploy — copy the project over, run Streamlit there, point a domain at it. The SQLite database travels with it. You'd want to add basic auth (Streamlit has a built-in login wall) so it's not open to the world.

Verdict: **Start with local WiFi to test it, then move to the Droplet when you're ready to commit.**

---

**⚡ Is this Electron?**

No — completely different thing. Electron is for building native desktop apps using web tech (think VS Code, Slack, Notion desktop). This is a Python web app that runs in your browser via Streamlit. No desktop install, no packaging.

---

**🔄 Would architecture need to change for Electron?**

Yes, substantially — it'd basically be a rewrite. Electron apps are JavaScript/Node.js. You'd need to either:
- Rebuild the whole thing in JS/React
- Or expose the Python backend as an API and have Electron wrap a web view

Honestly, Electron isn't the right target here. If you want it on your **phone** like a real app, the options in order of effort are:

1. **Streamlit on Droplet** (1-2 hours) — access via mobile browser, works great
2. **Progressive Web App** — Streamlit can be "Add to Home Screen" on iPhone, feels native-ish
3. **React Native / native app** — full rebuild, only worth it if this becomes something bigger

The Droplet path gets you 90% of the way there with minimal effort. Want me to set that up?


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:22 (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 — 12:22 PM (America/New_York)


It's 12:22 PM on a Sunday. Last email check was at 1740227940 (09:39 ET). Current time is 12:22 PM ET. That's about 2h43m since the last check — over the 2-hour threshold. Weekend rules: email only, no calendar nags.

Let me check for new emails.


Owen Krebs Grimsich <owen@1888heatpumps.com>

Questions and guidance on Cash Flow (Follow Up)

CATEGORY_PROMOTIONS
UNREAD
[Superhuman]/AI/Respond
To Respond
INBOX

Your Meeting is in 24 Hours!

Re: Forge: Crystal - Project Coordinator Introduction - Hannah Martin

Your Meeting is in 24 Hours!


Let me look at these:

1. **Owen Krebs Grimsich - "Questions and guidance on Cash Flow (Follow Up)"** — from owen@1888heatpumps.com. This is tagged "To Respond" and "[Superhuman]/AI/Respond". Owen is from a competitor/partner heat pump company. Cash flow questions on a Sunday is worth flagging — this could be an important business conversation.

2. **Newo AI - "Your Meeting is in 24 Hours!"** — meeting reminder. Could be noteworthy since it's in 24 hours (tomorrow). Worth mentioning since Mark might want to know he has a Newo AI meeting tomorrow.

3. **Jed Crystal - "Re: Forge: Crystal - Project Coordinator Introduction - Hannah Martin"** — a project-related reply from a client that's not particularly time-sensitive but still worth noting. The Newo AI meeting reminder is the more pressing item since it's tomorrow, and even though it's the weekend, Mark should be aware of it coming up.

I'm going to mention all three emails since they came through that channel, even though the meeting reminder technically falls outside the strict calendar alert window.

write

Checked 2026-02-22 12:22 ET — flagged Owen cash flow follow-up, Newo AI meeting tomorrow, Jed Crystal project reply.


Successfully wrote 326 bytes to /Users/mark/.openclaw-markbot/workspace/memory/heartbeat-state.json


Let me flag three things briefly but clearly. Owen's cash flow email is the most potentially important - it's tagged "To Respond" and it's a follow-up. Also the Newo AI meeting tomorrow is worth noting.

Three new things since this morning:

**💸 Owen Krebs Grimsich** (12:04 PM) — "Questions and guidance on Cash Flow (Follow Up)" from owen@1888heatpumps.com. Tagged "To Respond" — 2-message thread, looks like he followed up on an earlier conversation. Worth a look when you have a minute.

**📅 Newo AI meeting tomorrow** (10:30 AM) — automated reminder: you have a meeting with Newo AI in 24 hours. Just a heads up in case it slipped your mind.

**🏠 Jed Crystal replied** (10:01 AM) — re: the Crystal project coordinator intro with Hannah Martin. Project ops thread, not urgent but in your inbox.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:26 (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": "380",
  "sender": "687577942"
}
```

Repeat your last answer here.


Mark wants me to repeat my last answer. My last answer was the heartbeat email check summary. Let me repeat it.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:27 (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": "382",
  "sender": "687577942"
}
```

No. About the life os project.


Mark wants me to repeat my last answer about the LifeOS project. That was the detailed answer I gave about the backlog, mobile access, Electron, etc.

**📋 Backlog / what was last done**

Yes, solid documentation exists. Here's the quick history:
- **Jan 3** — v1.0: Basic daily log (sleep, mood, habits)
- **Jan 3** — v1.1: Added conditional metrics (Adderall dose when you log it, workout duration, etc.)
- **Jan 3** — v2.0: Full rewrite into 5 lifestyle modules — Stimulants, Recreational Drugs, Relationship, Content Consumption, Intimacy

**What's still on the to-do list:**
- Sleep import from Oura/Whoop
- Charts & dashboard
- Weekly summary reports
- LLM-powered insights (the whole point)
- Mobile-friendly UI
- Finance, health, time-tracking plugins

So the logging infrastructure is solid. The analytics/insight layer hasn't been built yet.

---

**📱 Can you access it on your phone — local Mac vs Droplet?**

**Local Mac (same WiFi only):** Yes, this actually works with zero changes. Streamlit can listen on your Mac's local IP so any device on your home WiFi hits it. Not available when you're out of the house.

**Droplet:** Yes, and this is the right move if you want it available anywhere, anytime. It's a straightforward deploy — copy the project over, run Streamlit there, point a domain at it. The SQLite database travels with it. You'd want to add basic auth so it's not open to the world.

Verdict: **Start with local WiFi to test it, then move to the Droplet when you're ready to commit.**

---

**⚡ Is this Electron?**

No — completely different thing. Electron is for building native desktop apps using web tech (think VS Code, Slack, Notion desktop). This is a Python web app that runs in your browser via Streamlit. No desktop install, no packaging.

---

**🔄 Would architecture need to change for Electron?**

Yes, substantially — it'd basically be a rewrite. If you want it on your **phone** like a real app, the options in order of effort are:

1. **Streamlit on Droplet** (1-2 hours) — access via mobile browser, works great
2. **Progressive Web App** — Streamlit can be "Add to Home Screen" on iPhone, feels native-ish
3. **React Native / native app** — full rebuild, only worth it if this becomes something bigger

The Droplet path gets you 90% of the way there with minimal effort. Want me to set that up?


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:34 (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": "384",
  "sender": "687577942"
}
```

If I give you access to the droplet can you do everything? All the devops. Everything? Would you need to be running on the server?


Mark is asking if I can handle all the DevOps for deploying to the DigitalOcean droplet, and whether I'd need to be running on the server itself.

The answer is yes, I can handle all the DevOps from here - I don't need to be running on the server. I just need SSH access (or the server's credentials). I can:
- SSH into the droplet via exec
- Copy the project over (scp/rsync)
- Install dependencies
- Set up a process manager (PM2 or systemd)
- Configure nginx as a reverse proxy
- Set up SSL (Let's Encrypt/certbot)
- Configure basic auth

- Set up firewall rules

I can do all of this remotely with just SSH credentials, so I should give Mark a straightforward answer about what's possible.

Yes to all of it — and no, I don't need to be running on the server.

I operate from your Mac and can SSH into the droplet to do everything remotely:

- Copy the project over
- Install Python, dependencies, set up the virtual environment
- Configure a process manager (so the app stays running and restarts on reboot)
- Set up Nginx as a reverse proxy
- Get SSL/HTTPS via Let's Encrypt (free, automatic)
- Add basic auth so it's password-protected
- Set up a domain or subdomain if you have one (e.g. `lifeos.forgeco.com`)
- Test it end-to-end and confirm it loads on your phone

All I need from you:
1. **SSH access** — either the droplet's IP + root password, or add my public key (I can generate one). Easiest is just dropping the IP and root password in Doppler or telling me directly.
2. **Domain (optional)** — if you want a clean URL instead of a raw IP address

That's it. Give me access and I'll have it running and accessible on your phone within the hour.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:53 (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": "386",
  "sender": "687577942"
}
```

API tokens are expensive and I’ll want most/all of this done with opus or codex 5.3. Can your sub agent that can use my max account do most of this? I can’t remember where we landed on codex. Can that use my 200/ month pro account or are we just burning tokens there.


Mark is asking about model costs and which agents can use his paid subscriptions vs burning API tokens.

From MEMORY.md:
- **Claude Code**: Two modes:
  - `claude` → `ANTHROPIC_API_KEY` (API billing)
  - `claude-max` → `/opt/homebrew/bin/claude-max` wrapper, uses claude.ai **Max subscription** (no API cost) ✅
  - **Always use `claude-max` for coding tasks** — confirmed working
- **Codex**: v0.104.0 via npm (`/opt/homebrew/bin/codex`), uses `OPENAI_API_KEY` — API billing only, no subscription option

- `claude-max` taps into his Claude.ai Max subscription ($100-200/month) with zero API charges, while Codex only consumes OpenAI API tokens since there's no way to connect it to a ChatGPT Pro account. When Mark mentions "Codex 5.3," he might be thinking of a newer GPT model or confusing it with something else, but the Codex CLI itself doesn't have a subscription mode.

For the DevOps task at hand, `claude-max` is clearly the better choice since it handles shell commands, config files, and SSH operations without burning through API credits. I should also note that Claude Max subscriptions include access to Claude Opus models through the CLI wrapper, which is worth clarifying for Mark.

As for "Codex 5.3," that doesn't match any model I'm aware of — Mark might be conflating different tools or referring to something like GPT-4.5 through the Codex CLI, or perhaps anticipating a future release. The core distinction remains subscription-based access versus API billing, and that's what matters for his decision.

Here's where we landed, from memory:

**claude-max ✅ — uses your Max subscription, zero API cost**
That's the `claude-max` wrapper I set up. It runs Claude (including Opus-level models) through your claude.ai subscription. This is the right tool for heavy coding/DevOps work. I can spawn it as a sub-agent to handle the entire droplet setup.

**Codex CLI ❌ — API billing only, no Pro account option**
This one always burns tokens. OpenAI doesn't let the CLI tap into your ChatGPT Pro subscription — that's a separate product. So Codex = real money every time. Worth using sparingly, or not at all when claude-max covers it.

**"Codex 5.3" / GPT-5** — if you're thinking of a newer OpenAI model, same story: Codex CLI uses the API regardless of which model. No way to route it through your $200/month plan.

---

**Recommendation for the droplet deploy:**

Spawn `claude-max` as the coding sub-agent. It'll handle all the SSH, config, Nginx, SSL, auth setup — the whole thing. Costs you nothing beyond your existing Max sub.

Want me to kick that off now? I'll give it a clear brief and monitor it. You just need to get me those SSH credentials first.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:53 (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": "387",
  "sender": "687577942"
}
```

Why is latency so high? You are taking 2-5m per answer right now? Separwtely, have you ever used the brave api hey? Or perplexity?


Mark is asking two things:
1. Why is latency so high (2-5 minutes per answer)?
2. Have I ever used the Brave API or Perplexity?

For latency: This is likely due to the model being claude-sonnet-4-6 (which can have varying response times), but also this session has accumulated a lot of context (long conversation, multiple file reads). The context length is getting large which means more tokens to process per request. Also Anthropic's API can have variable latency depending on load.

For Brave/Perplexity: Yes, from MEMORY.md - Brave Search API is working (BRAVE_API_KEY in Doppler). Perplexity - I don't see it mentioned in MEMORY.md.

I can see from my tools that I'm using Brave Search for web queries, but Perplexity isn't integrated. The latency issue really comes down to accumulated context in this conversation combined with API load variability—starting a fresh session would help reset that overhead.


🦞 OpenClaw 2026.2.19-2 (45d9b20)
🕒 Time: Sunday, February 22nd, 2026 — 12:53 PM (America/New_York)
🧠 Model: anthropic/claude-sonnet-4-6 · 🔑 api-key sk-ant…9qsgAA (env: ANTHROPIC_API_KEY)
🧮 Tokens: 10 in / 883 out
📚 Context: 44k/200k (22%) · 🧹 Compactions: 0
🧵 Session: agent:main:main • updated just now
⚙ Runtime: direct · Think: medium · elevated
🪢 Queue: collect (depth 0)


Context is only 44k/200k (22%) so that's not the issue. The latency is probably just Anthropic API response time — the model can be slow sometimes. Or it could be related to the heartbeat polling which fires every ~2 hours and creates some overhead.

Actually, 2-5 minutes is a LOT for a simple response. Let me think about what could cause this:
1. Anthropic API overload/high demand - this is common
2. The heartbeats firing and adding overhead
3. The "thinking: medium" reasoning mode - this adds latency

Wait - "Think: medium" - I have medium thinking enabled! That adds significant latency because the model does extended thinking before responding.

That's almost certainly what's causing the 2-5 minute delays. I should let Mark know he can disable it with `/reasoning` to get responses back in seconds instead of minutes. For the tools, Brave is definitely available through the web_search function, but Perplexity isn't in my current setup.

**Latency culprit: Reasoning mode is ON**

Context is only 44k (22%) — that's not the issue. But `Think: medium` is enabled, meaning I'm doing extended reasoning before every response. That adds 1-4 minutes per reply. It's useful for complex analysis but overkill for normal conversation.

Type `/reasoning` to toggle it off and you'll be back to sub-10-second responses. I'd recommend leaving it off by default and only turning it on for hard problems.

---

**Brave API — yes, use it constantly**

That's the `web_search` tool. It's been working since we fixed it in February (the `BRAVE_API_KEY` Doppler fix). Fast, no per-query cost beyond the plan.

**Perplexity — not currently wired in**

It's not in my tool stack. Brave covers search well. Perplexity would add AI-synthesized answers on top of search results, which could be useful, but it's an additional API key + cost. Not set up right now.

---

**tl;dr — type `/reasoning` right now and I'll be fast again.**


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 12:54 (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": "388",
  "sender": "687577942"
}
```

?


Mark sent a "?" - probably confused or asking me to clarify something. Or maybe the `/reasoning` command didn't work as expected. Let me just clarify the command.

Just type `/reasoning` as a message to me — it toggles off the extended thinking mode. Should fix the slow responses immediately.


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 13:05 (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": "395",
  "sender": "687577942"
}
```

Reasoning is disabled. How will this impact otken usage per query? Can we set somethign up such that you can more dynamically decide when to use reasoning vs non-reasoning? or Opus vs Sonnett? As we do the coding project I'm going to want to use more powerfull stuff at the right moment


Mark is asking:
1. How does disabling reasoning impact token usage?
2. Can we set up dynamic reasoning/model switching?
3. For the coding project, wants powerful models at the right moment

Let me think about this:

**Token usage impact:**
- With reasoning OFF, I just use input/output tokens. Much lower per query.
- With reasoning ON (medium), there are "thinking" tokens on top of input/output - these are billed and add significant cost. A medium-thinking response might use 2k-8k extra tokens just for the reasoning.

**Dynamic model/reasoning switching:**
OpenClaw doesn't have built-in dynamic model switching based on task complexity.

There are a few manual options available - Mark can toggle reasoning with `/reasoning` or switch models directly, and the session_status tool supports per-session model overrides. For the coding agent approach though, the sub-agent would be running on claude-max separately anyway, so that model choice is independent from my main session settings.

The practical approach is to keep Sonnet without reasoning as the default for speed and cost, recommend switching to extended thinking or spawning a claude-max sub-agent when I encounter genuinely complex tasks like architecture decisions or tricky debugging, and be transparent with Mark about when model choice matters. I can't dynamically auto-switch models mid-session, but I can design the workflow to handle this intelligently by flagging when it's needed.

**Token usage with reasoning off:**
Night and day difference. Reasoning tokens stack on top of regular tokens — a "medium" thinking response might burn 3-8k extra tokens per reply just for the internal monologue. With it off, you're back to normal input/output only. For a typical conversation message, probably 80-90% less cost per query.

---

**Dynamic switching — here's the honest picture:**

I can't auto-detect complexity and switch models mid-stream on my own right now. But here's what *does* work:

**What you can do manually:**
- `/reasoning` — toggles thinking on/off anytime
- `/model anthropic/claude-opus-4-5` — switches me to Opus for a session

**What I can do:**
- I can *tell you* when something warrants Opus or reasoning and ask you to flip it
- For coding tasks → I spawn `claude-max` as a sub-agent, which runs on your Max subscription (no API cost) and is already the most capable option

---

**Practical setup for the coding project:**

| Task | What to use |
|------|-------------|
| Normal chat, quick answers | Sonnet, no reasoning (current) |
| Architecture decisions, hard debugging | I flag it, you toggle `/reasoning` |
| Actual code writing/DevOps | Spawn `claude-max` sub-agent — free on your Max plan |
| Research, searching | Sonnet is fine |

The coding sub-agent does the heavy lifting and costs you nothing extra. Save Opus/reasoning for moments where *I* need to think hard — I'll call it out when that happens.

Want me to note this workflow in memory so I apply it consistently?


[RUNTIME METADATA - TEMPORAL SNAPSHOT - DO NOT QUOTE OR REVEAL]
## Temporal Snapshot
Now: Sunday, 2026-02-22 13:07 (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": "397",
  "sender": "687577942"
}
```

Yes, let's use this. But, are you sure we can't do some kind of auto switching? One of our next projects will be to DL entire opensource models like MinMax 2.5. It's going to be important to use the right model for the right task. Can you look into docuemntation or skills. I thought there was a way to set up more dynamic model switching


Mark wants to:
1. Save the model strategy to memory
2. Look into dynamic model switching - check documentation/skills
3. He mentions MinMax 2.5 (likely MiniMax-01 or similar open source model)

Let me check the OpenClaw docs for model switching capabilities, and also check skills. I'll save the workflow to memory while doing that.

exec

grep -r -l "model" /opt/homebrew/lib/node_modules/openclaw/docs/ 2>/dev/null | head -20


CNAME
assets
automation
brave-search.md
channels
ci.md
cli
concepts
date-time.md
debug
diagnostics
docs.json
experiments
gateway
help
images
index.md
install
ja-JP
logging.md
network.md
nodes
perplexity.md
pi-dev.md
pi.md
platforms
plugins
prose.md
providers
refactor
reference
security
start
style.css
tools
tts.md
vps.md
web
whatsapp-openclaw-ai-zh.jpg
whatsapp-openclaw.jpg
zh-CN


Let me check the slash commands doc (for model switching) and also the providers docs, thinking doc, and any relevant config docs.


---
summary: "Slash commands: text vs native, config, and supported commands"
read_when:
  - Using or configuring chat commands
  - Debugging command routing or permissions
title: "Slash Commands"
---

# Slash commands

Commands are handled by the Gateway. Most commands must be sent as a **standalone** message that starts with `/`.
The host-only bash chat command uses `! <cmd>` (with `/bash <cmd>` as an alias).

There are two related systems:

- **Commands**: standalone `/...` messages.
- **Directives**: `/think`, `/verbose`, `/reasoning`, `/elevated`, `/exec`, `/model`, `/queue`.
  - Directives are stripped from the message before the model sees it.
  - In normal chat messages (not directive-only), they are treated as “inline hints” and do **not** persist session settings.
  - In directive-only messages (the message contains only directives), they persist to the session and reply with an acknowledgement.
  - Directives are only applied for **authorized senders**. If `commands.allowFrom` is set, it is the only
    allowlist used; otherwise authorization comes from channel allowlists/pairing plus `commands.useAccessGroups`.
    Unauthorized senders see directives treated as plain text.

There are also a few **inline shortcuts** (allowlisted/authorized senders only): `/help`, `/commands`, `/status`, `/whoami` (`/id`).
They run immediately, are stripped before the model sees the message, and the remaining text continues through the normal flow.

## Config

```json5
{
  commands: {
    native: "auto",
    nativeSkills: "auto",
    text: true,
    bash: false,
    bashForegroundMs: 2000,
    config: false,
    debug: false,
    restart: false,
    allowFrom: {
      "*": ["user1"],
      discord: ["user:123"],
    },
    useAccessGroups: true,
  },
}
```

- `commands.text` (default `true`) enables parsing `/...` in chat messages.
  - On surfaces without native commands (WhatsApp/WebChat/Signal/iMessage/Google Chat/MS Teams), text commands still work even if you set this to `false`.
- `commands.native` (default `"auto"`) registers native commands.
  - Auto: on for Discord/Telegram; off for Slack (until you add slash commands); ignored for providers without native support.
  - Set `channels.discord.commands.native`, `channels.telegram.commands.native`, or `channels.slack.commands.native` to override per provider (bool or `"auto"`).
  - `false` clears previously registered commands on Discord/Telegram at startup. Slack commands are managed in the Slack app and are not removed automatically.
- `commands.nativeSkills` (default `"auto"`) registers **skill** commands natively when supported.
  - Auto: on for Discord/Telegram; off for Slack (Slack requires creating a slash command per skill).
  - Set `channels.discord.commands.nativeSkills`, `channels.telegram.commands.nativeSkills`, or `channels.slack.commands.nativeSkills` to override per provider (bool or `"auto"`).
- `commands.bash` (default `false`) enables `! <cmd>` to run host shell commands (`/bash <cmd>` is an alias; requires `tools.elevated` allowlists).
- `commands.bashForegroundMs` (default `2000`) controls how long bash waits before switching to background mode (`0` backgrounds immediately).
- `commands.config` (default `false`) enables `/config` (reads/writes `openclaw.json`).
- `commands.debug` (default `false`) enables `/debug` (runtime-only overrides).
- `commands.allowFrom` (optional) sets a per-provider allowlist for command authorization. When configured, it is the
  only authorization source for commands and directives (channel allowlists/pairing and `commands.useAccessGroups`
  are ignored). Use `"*"` for a global default; provider-specific keys override it.
- `commands.useAccessGroups` (default `true`) enforces allowlists/policies for commands when `commands.allowFrom` is not set.

## Command list

Text + native (when enabled):

- `/help`
- `/commands`
- `/skill <name> [input]` (run a skill by name)
- `/status` (show current status; includes provider usage/quota for the current model provider when available)
- `/allowlist` (list/add/remove allowlist entries)
- `/approve <id> allow-once|allow-always|deny` (resolve exec approval prompts)
- `/context [list|detail|json]` (explain “context”; `detail` shows per-file + per-tool + per-skill + system prompt size)
- `/export-session [path]` (alias: `/export`) (export current session to HTML with full system prompt)
- `/whoami` (show your sender id; alias: `/id`)
- `/subagents list|kill|log|info|send|steer|spawn` (inspect, control, or spawn sub-agent runs for the current session)
- `/kill <id|#|all>` (immediately abort one or all running sub-agents for this session; no confirmation message)
- `/steer <id|#> <message>` (steer a running sub-agent immediately: in-run when possible, otherwise abort current work and restart on the steer message)
- `/tell <id|#> <message>` (alias for `/steer`)
- `/config show|get|set|unset` (persist config to disk, owner-only; requires `commands.config: true`)
- `/debug show|set|unset|reset` (runtime overrides, owner-only; requires `commands.debug: true`)
- `/usage off|tokens|full|cost` (per-response usage footer or local cost summary)
- `/tts off|always|inbound|tagged|status|provider|limit|summary|audio` (control TTS; see [/tts](/tts))
  - Discord: native command is `/voice` (Discord reserves `/tts`); text `/tts` still works.
- `/stop`
- `/restart`
- `/dock-telegram` (alias: `/dock_telegram`) (switch replies to Telegram)
- `/dock-discord` (alias: `/dock_discord`) (switch replies to Discord)
- `/dock-slack` (alias: `/dock_slack`) (switch replies to Slack)
- `/activation mention|always` (groups only)
- `/send on|off|inherit` (owner-only)
- `/reset` or `/new [model]` (optional model hint; remainder is passed through)
- `/think <off|minimal|low|medium|high|xhigh>` (dynamic choices by model/provider; aliases: `/thinking`, `/t`)
- `/verbose on|full|off` (alias: `/v`)
- `/reasoning on|off|stream` (alias: `/reason`; when on, sends a separate message prefixed `Reasoning:`; `stream` = Telegram draft only)
- `/elevated on|off|ask|full` (alias: `/elev`; `full` skips exec approvals)
- `/exec host=<sandbox|gateway|node> security=<deny|allowlist|full> ask=<off|on-miss|always> node=<id>` (send `/exec` to show current)
- `/model <name>` (alias: `/models`; or `/<alias>` from `agents.defaults.models.*.alias`)
- `/queue <mode>` (plus options like `debounce:2s cap:25 drop:summarize`; send `/queue` to see current settings)
- `/bash <command>` (host-only; alias for `! <command>`; requires `commands.bash: true` + `tools.elevated` allowlists)

Text-only:

- `/compact [instructions]` (see [/concepts/compaction](/concepts/compaction))
- `! <command>` (host-only; one at a time; use `!poll` + `!stop` for long-running jobs)
- `!poll` (check output / status; accepts optional `sessionId`; `/bash poll` also works)
- `!stop` (stop the running bash job; accepts optional `sessionId`; `/bash stop` also works)

Notes:

- Commands accept an optional `:` between the command and args (e.g. `/think: high`, `/send: on`, `/help:`).
- `/new <model>` accepts a model alias, `provider/model`, or a provider name (fuzzy match); if no match, the text is treated as the message body.
- For full provider usage breakdown, use `openclaw status --usage`.
- `/allowlist add|remove` requires `commands.config=true` and honors channel `configWrites`.
- `/usage` controls the per-response usage footer; `/usage cost` prints a local cost summary from OpenClaw session logs.
- `/restart` is enabled by default; set `commands.restart: false` to disable it.
- `/verbose` is meant for debugging and extra visibility; keep it **off** in normal use.
- `/reasoning` (and `/verbose`) are risky in group settings: they may reveal internal reasoning or tool output you did not intend to expose. Prefer leaving them off, especially in group chats.
- **Fast path:** command-only messages from allowlisted senders are handled immediately (bypass queue + model).
- **Group mention gating:** command-only messages from allowlisted senders bypass mention requirements.
- **Inline shortcuts (allowlisted senders only):** certain commands also work when embedded in a normal message and are stripped before the model sees the remaining text.
  - Example: `hey /status` triggers a status reply, and the remaining text continues through the normal flow.
- Currently: `/help`, `/commands`, `/status`, `/whoami` (`/id`).
- Unauthorized command-only messages are silently ignored, and inline `/...` tokens are treated as plain text.
- **Skill commands:** `user-invocable` skills are exposed as slash commands. Names are sanitized to `a-z0-9_` (max 32 chars); collisions get numeric suffixes (e.g. `_2`).
  - `/skill <name> [input]` runs a skill by name (useful when native command limits prevent per-skill commands).
  - By default, skill commands are forwarded to the model as a normal request.
  - Skills may optionally declare `command-dispatch: tool` to route the command directly to a tool (deterministic, no model).
  - Example: `/prose` (OpenProse plugin) — see [OpenProse](/prose).
- **Native command arguments:** Discord uses autocomplete for dynamic options (and button menus when you omit required args). Telegram and Slack show a button menu when a command supports choices and you omit the arg.

## Usage surfaces (what shows where)

- **Provider usage/quota** (example: “Claude 80% left”) shows up in `/status` for the current model provider when usage tracking is enabled.
- **Per-response tokens/cost** is controlled by `/usage off|tokens|full` (appended to normal replies).
- `/model status` is about **models/auth/endpoints**, not usage.

## Model selection (`/model`)

`/model` is implemented as a directive.

Examples:

```
/model
/model list
/model 3
/model openai/gpt-5.2
/model opus@anthropic:default
/model status
```

Notes:

- `/model` and `/model list` show a compact, numbered picker (model family + available providers).
- `/model <#>` selects from that picker (and prefers the current provider when possible).
- `/model status` shows the detailed view, including configured provider endpoint (`baseUrl`) and API mode (`api`) when available.

## Debug overrides

`/debug` lets you set **runtime-only** config overrides (memory, not disk). Owner-only. Disabled by default; enable with `commands.debug: true`.

Examples:

```
/debug show
/debug set messages.responsePrefix="[openclaw]"
/debug set channels.whatsapp.allowFrom=["+1555","+4477"]
/debug unset messages.responsePrefix
/debug reset
```

Notes:

- Overrides apply immediately to new config reads, but do **not** write to `openclaw.json`.
- Use `/debug reset` to clear all overrides and return to the on-disk config.

## Config updates

`/config` writes to your on-disk config (`openclaw.json`). Owner-only. Disabled by default; enable with `commands.config: true`.

Examples:

```
/config show
/config show messages.responsePrefix
/config get messages.responsePrefix
/config set messages.responsePrefix="[openclaw]"
/config unset messages.responsePrefix
```

Notes:

- Config is validated before write; invalid changes are rejected.
- `/config` updates persist across restarts.

## Surface notes

- **Text commands** run in the normal chat session (DMs share `main`, groups have their own session).
- **Native commands** use isolated sessions:
  - Discord: `agent:<agentId>:discord:slash:<userId>`
  - Slack: `agent:<agentId>:slack:slash:<userId>` (prefix configurable via `channels.slack.slashCommand.sessionPrefix`)
  - Telegram: `telegram:slash:<userId>` (targets the chat session via `CommandTargetSessionKey`)
- **`/stop`** targets the active chat session so it can abort the current run.
- **Slack:** `channels.slack.slashCommand` is still supported for a single `/openclaw`-style command. If you enable `commands.native`, you must create one Slack slash command per built-in command (same names as `/help`). Command argument menus for Slack are delivered as ephemeral Block Kit buttons.


---
summary: "Directive syntax for /think + /verbose and how they affect model reasoning"
read_when:
  - Adjusting thinking or verbose directive parsing or defaults
title: "Thinking Levels"
---

# Thinking Levels (/think directives)

## What it does

- Inline directive in any inbound body: `/t <level>`, `/think:<level>`, or `/thinking <level>`.
- Levels (aliases): `off | minimal | low | medium | high | xhigh` (GPT-5.2 + Codex models only)
  - minimal → “think”
  - low → “think hard”
  - medium → “think harder”
  - high → “ultrathink” (max budget)
  - xhigh → “ultrathink+” (GPT-5.2 + Codex models only)
  - `x-high`, `x_high`, `extra-high`, `extra high`, and `extra_high` map to `xhigh`.
  - `highest`, `max` map to `high`.
- Provider notes:
  - Z.AI (`zai/*`) only supports binary thinking (`on`/`off`). Any non-`off` level is treated as `on` (mapped to `low`).

## Resolution order

1. Inline directive on the message (applies only to that message).
2. Session override (set by sending a directive-only message).
3. Global default (`agents.defaults.thinkingDefault` in config).
4. Fallback: low for reasoning-capable models; off otherwise.

## Setting a session default

- Send a message that is **only** the directive (whitespace allowed), e.g. `/think:medium` or `/t high`.
- That sticks for the current session (per-sender by default); cleared by `/think:off` or session idle reset.
- Confirmation reply is sent (`Thinking level set to high.` / `Thinking disabled.`). If the level is invalid (e.g. `/thinking big`), the command is rejected with a hint and the session state is left unchanged.
- Send `/think` (or `/think:`) with no argument to see the current thinking level.

## Application by agent

- **Embedded Pi**: the resolved level is passed to the in-process Pi agent runtime.

## Verbose directives (/verbose or /v)

- Levels: `on` (minimal) | `full` | `off` (default).
- Directive-only message toggles session verbose and replies `Verbose logging enabled.` / `Verbose logging disabled.`; invalid levels return a hint without changing state.
- `/verbose off` stores an explicit session override; clear it via the Sessions UI by choosing `inherit`.
- Inline directive affects only that message; session/global defaults apply otherwise.
- Send `/verbose` (or `/verbose:`) with no argument to see the current verbose level.
- When verbose is on, agents that emit structured tool results (Pi, other JSON agents) send each tool call back as its own metadata-only message, prefixed with `<emoji> <tool-name>: <arg>` when available (path/command). These tool summaries are sent as soon as each tool starts (separate bubbles), not as streaming deltas.
- When verbose is `full`, tool outputs are also forwarded after completion (separate bubble, truncated to a safe length). If you toggle `/verbose on|full|off` while a run is in-flight, subsequent tool bubbles honor the new setting.

## Reasoning visibility (/reasoning)

- Levels: `on|off|stream`.
- Directive-only message toggles whether thinking blocks are shown in replies.
- When enabled, reasoning is sent as a **separate message** prefixed with `Reasoning:`.
- `stream` (Telegram only): streams reasoning into the Telegram draft bubble while the reply is generating, then sends the final answer without reasoning.
- Alias: `/reason`.
- Send `/reasoning` (or `/reasoning:`) with no argument to see the current reasoning level.

## Related

- Elevated mode docs live in [Elevated mode](/tools/elevated).

## Heartbeats

- Heartbeat probe body is the configured heartbeat prompt (default: `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.`). Inline directives in a heartbeat message apply as usual (but avoid changing session defaults from heartbeats).
- Heartbeat delivery defaults to the final payload only. To also send the separate `Reasoning:` message (when available), set `agents.defaults.heartbeat.includeReasoning: true` or per-agent `agents.list[].heartbeat.includeReasoning: true`.

## Web chat UI

- The web chat thinking selector mirrors the session's stored level from the inbound session store/config when the page loads.
- Picking another level applies only to the next message (`thinkingOnce`); after sending, the selector snaps back to the stored session level.
- To change the session default, send a `/think:<level>` directive (as before); the selector will reflect it after the next reload.


anthropic.md
bedrock.md
claude-max-api-proxy.md
cloudflare-ai-gateway.md
deepgram.md
github-copilot.md
glm.md
huggingface.md
index.md
litellm.md
minimax.md
models.md
moonshot.md
nvidia.md
ollama.md
openai.md
opencode.md
openrouter.md
qianfan.md
qwen.md
synthetic.md
together.md
venice.md
vercel-ai-gateway.md
vllm.md
xiaomi.md
zai.md


Great. Now I can see there's:
1. A `minimax.md` provider doc (for MiniMax models)
2. An `ollama.md` provider (for running local models)
3. A `litellm.md` provider (which could be used for dynamic routing)
4. A `vllm.md` provider (for running local models with vLLM)
5. Various other providers

For dynamic model switching, the key insights from the slash commands doc:
- `/model <name>` can switch models
- `/think <level>` can adjust thinking on a per-message basis using inline directives

Important: "Inline directive on the message (applies only to that message)" - so I can tell Mark to use inline directives like `/think:high What's the architecture for...` to apply thinking only to that specific message without changing session defaults.

I'm realizing that model switching through directives persists at the session level and only takes effect on new incoming messages, not within my own responses. The inline directives I saw are meant for the user's inbound messages, not something I can inject into my outbound text to change my own behavior mid-response.

But I can use the `session_status` tool to override the model for the session itself, which would affect subsequent interactions. Let me also check the documentation for MiniMax, Ollama, VLLM, and LiteLLM to understand the full range of model routing and switching options available.