Skill: nitsujy/todolist-md-clawdbot-copy (version 1.0.0)

--- name: todolist-md-clawdbot description: Operate on todolist-md Markdown todo files. Read + summarize tasks, propose edits, and write outcomes back into Markdown using only <!-- bot: ... --> markers (line-stable write-back). --- # todolist-md-clawdbot Operate on **todolist-md**: a Markdown-first todo viewer/editor. The app does not contain an AI; the bot reads/writes Markdown. ## Operating rules (must follow) 1) Markdown is the system of record. - It’s fine to discuss in chat, but final answers/decisions must be written into Markdown. 2) Use only bot markers of this form. - `<!-- bot: ... -->` - Do not introduce other syntaxes (no `Question[id=...]`, no custom metadata blocks). 3) Preserve task identity. - todolist-md derives task IDs from Markdown line positions. - Some storage backends also have a stable identity key (e.g. Drive `fileId`, local `path`, S3 `bucket+key`). - Do not “replace” a file in a way that changes its identity key unless the user explicitly accepts it. 4) Keep write-back edits line-stable. - Avoid adding/removing lines inside an existing task item or its description blockquote. - Prefer single-line, in-place edits (edit text on an existing line). 5) Last review stamp (Option B: top-of-file header line) - Goal: a single line near the top recording last bot review time. - Rule: **never insert a new line** once the header exists. Only update the existing header line. - If the header does not exist, you may insert it at the very top **only if the user explicitly opted into Option B**. - Canonical format: - `<!-- bot: last_review --> 2026-02-04T15:39Z root=<rootFolderId> model=<model>` 6) Never complete tasks without explicit user confirmation. ## Markdown conventions (what todolist-md expects) - Tasks use GFM checkboxes: `- [ ]` and `- [x]` - Optional tags: `#tag` - Optional due date: `due:YYYY-MM-DD` - Optional description: a blockquote directly under the task ## Storage Q/A (first run) Ask once, then persist the answers (in memory/config) for future runs. - Q: `storageKind`? - A: `google-drive` | `local-folder` | `s3` | `other` - Q: What is the stable identity key for files? - Drive: `fileId` - Local: `path` - S3: `bucket+key` - Q: Where is the root? - Drive: `rootFolderId` - Local: root directory path - S3: `bucket` + optional prefix ## Chrome app integration: enable/disable per-file When a Drive folder contains many `.md` files, not all of them should necessarily be AI-reviewed. **Recommendation (simple + app-controlled):** - The Chrome app should write a small per-file config key/marker so the agent can know whether a file is opted-in. Two easy options: ### Option 1: a dedicated Drive config file (preferred) Create a config file in the same folder: - `.todolist-md.config.json` Example: ```json { "ai": { "enabled": true, "include": ["*.md"], "exclude": ["todoapp.md"], "botSuggestedSectionTitle": "Tasks (bot-suggested)" } } ``` The agent should: - Download `.todolist-md.config.json` when it changes. - Only review files that match include/exclude rules. ### Option 2: an in-file marker (works without JSON) Add a single line near the top of the markdown file: - `<!-- bot: ai_enabled --> true` The agent should only review files containing that marker. ## Review cadence + stamping (save credits) **Do not call an LLM unless a file changed.** Use code-first change detection. ### Step 0: Detect changes (code-first) - For each `.md` under root, compare `modifiedTime`/`size` (or `etag` when available) against a local state file. - If unchanged since last scan: **skip** (no download, no LLM). ### Step 1: Review only changed files - Only for changed files: - Download the file - Extract open tasks (`- [ ]`) and relevant context - (Optional) call the LLM on the extracted subset, not the full document ### Step 2: Write-back only if content changed - Before writing back, compute a hash; if no changes, do not write. ### Step 3: Stamp last review (Option B) - For each reviewed file, update (not append) the top-of-file header line: - `<!-- bot: last_review --> <ISO_UTC> root=<rootFolderId> model=<model>` ### Reference script (Google Drive + gog) If you use **Google Drive** as storage, gog CLI flags matter. In gog v0.9.0+: - list folder: `gog drive ls --parent <folderId> --json` - download: `gog drive download <fileId> --out <path>` - run gog as `ubuntu` and download to `/tmp` (because `/root` is typically `700`) Included helper scripts: - `skills/todolist-md-clawdbot/scripts/todolist_drive_folder_agent.mjs` - **single script for everything** (Drive folder runner): lists all `.md` under a folder, detects changes via state file, downloads only changed files, and writes back a `<!-- bot: suggested -->` block under a dedicated section title. - uses Drive API `files.update` to overwrite-update the same `fileId` (no duplicates). - includes a revision gate (headRevisionId) to avoid overwriting while you edit in Chrome. - Managed OAuth mode (recommended): stores its own refresh token file; first run prints an auth URL, you approve in browser, then rerun with `--authCode <CODE>`. - **minimum-token design**: does not call LLM unless a file changed, and should send only extracted open tasks (not full file). - `skills/todolist-md-clawdbot/scripts/todolist_agent_entrypoint.mjs` - single-file helper (useful for debugging): download by fileId + overwrite-update Example: ```md - [ ] Update README for bot workflow #docs due:2026-02-05 > Include quick start and a worked example ``` ## Bot markers (allowed) - `<!-- bot: suggested -->` for bot-suggested sections - `<!-- bot: question -->` for in-file Q/A - `<!-- bot: digest -->` for summaries/digests - `<!-- bot: note -->` for short audit notes (optional) ## LLM handoff (prepare/apply) — minimum-token workflow When you want LLM help but must keep Drive as the source of truth (and keep costs low), use the **two-stage** flow: ### Stage 1: prepare Goal: generate a compact JSON request for the OpenClaw agent runtime (LLM), without calling any LLM from the Node script. What happens: 1) List folder files (cheap; no downloads) 2) Compare each file's `modifiedTime/size` against a local state file 3) Download only changed `.md` 4) Extract only open tasks (`- [ ] ...`, max N lines) 5) Write `llm_request.json` Command example (single file): ```bash node skills/todolist-md-clawdbot/scripts/todolist_drive_folder_agent.mjs \ --folderId <rootFolderId> \ --onlyName vyond.md \ --mode prepare \ --requestOut outputs/todolist-md/vyond_llm_request.json ``` ### Stage 2: apply Goal: take a suggestions JSON (produced by the agent runtime) and write it back **only** to a dedicated bot section. Rules: - Update only under: - `## Tasks (bot-suggested)` - `<!-- bot: suggested -->` - Never mark tasks complete. - Use Drive API overwrite update by `fileId` (no duplicates). - Use a revision gate (`headRevisionId`) to avoid overwriting while you edit in Chrome. Suggestions JSON shape: ```json { "schema": "todolist-md.llm_suggestions.v1", "items": [ { "fileId": "...", "name": "vyond.md", "suggested_markdown": "- [ ] ...\n > <!-- bot: note --> ..." } ] } ``` Command example: ```bash node skills/todolist-md-clawdbot/scripts/todolist_drive_folder_agent.mjs \ --folderId <rootFolderId> \ --mode apply \ --suggestionsIn outputs/todolist-md/vyond_llm_suggestions.json ``` ### Why this saves tokens Example: if a folder has 50 Markdown files, but only 1 changed: - LLM is called **only for that 1 file**. - The prompt includes only extracted open tasks (`- [ ] ...`), not the entire Markdown. ## Write-back patterns ### Bot-suggested tasks Put bot-generated tasks under a dedicated section so humans can review before adopting. ```md ## Tasks (bot-suggested) <!-- bot: suggested --> - [ ] Add a “Bot Log” section ``` ### In-file Q/A (detail blockquote, line-stable) Ask a question using the detail blockquote line under the task: ```md - [ ] Deploy v2.0 to production #backend > <!-- bot: question --> Which CI job is failing? Options: unit / integration / e2e ``` Answer by adding an inline answer on the same line to keep line-stable: ```md - [ ] Deploy v2.0 to production #backend > <!-- bot: question --> Which CI job is failing? Options: unit / integration / e2e Answer: integration ``` Rules: - Prefer one active Q/A per task at a time. - Keep the Q/A inside the blockquote detail area. - Do not start multi-line threads beyond the single question and optional answer line. ### Archive Q/A to Bot Log (preferred once answered) If you want to keep tasks clean while preserving history: 1) Append an entry to `## Bot Log`. 2) Replace the original Q/A line in-place with a short placeholder: ```md > <!-- bot: question --> (archived to Bot Log) ``` ## Bot Log (append-only) Create (if missing): ```md ## Bot Log ``` Append entries like: ```md - 2026-02-01 Task: Deploy v2.0 to production Q: Which CI job is failing? A: integration ``` ## Summaries - Summarize in chat for fast feedback. - Optionally write a digest into Markdown as a single comment line: ```md <!-- bot: digest --> Top 3 next actions: 1) … 2) … 3) … ``` ## Worked example (end-to-end) Start state: ```md ## Work - [ ] Deploy v2.0 to production #backend due:2026-02-05 > Runbook: docs/deploy.md ## Tasks (bot-suggested) <!-- bot: suggested --> - [ ] (suggested) Add a “Bot Log” section ``` Bot asks a clarifying question (in-file): ```md - [ ] Deploy v2.0 to production #backend due:2026-02-05 > Runbook: docs/deploy.md > <!-- bot: question --> Which CI job is failing? Options: unit / integration / e2e ``` User answers by editing the same line (line-stable): ```md > <!-- bot: question --> Which CI job is failing? Options: unit / integration / e2e Answer: integration ``` After the bot consumes the answer, archive it (preferred): ```md > <!-- bot: question --> (archived to Bot Log) ## Bot Log - 2026-02-04 Task: Deploy v2.0 to production | Q: Which CI job is failing? | A: integration ``` ## Integration notes (minimal) - Always mark bot-written content with `<!-- bot: ... -->`. - Never delete/add lines inside an existing task item or description block. - Never mark tasks complete unless the user explicitly confirms.


Security scan verdicts — ClawScan: suspicious | static analysis: suspicious | VirusTotal: suspicious | SkillSpector: clean (LOW)