name: pkm-loop description: > Extract, classify, cross-reference, and persist goals/tasks/ideas/lessons from conversation and automation passes. Use when: end-of-conversation reflection cues, scheduled daily/weekly/monthly review triggers, or explicit user request to "save this"/"track this"/"remember". Don't use for routine chatting where no durable capture is needed.

PKM Loop

Purpose

This skill runs a repeatable Personal Knowledge Management pass so context is captured consistently:

• Extract durable items from conversation context
• Classify candidates into goal, task, idea, or lesson
• Cross-reference existing docs/lessons to avoid duplicates
• Ask once with grouped, natural confirmation UX
• Write only after explicit user approval
• Run review cycles (daily, weekly, monthly)

Primary tools required:

• nbhd_document_get
• nbhd_document_put
• nbhd_document_append
• nbhd_lesson_suggest
• nbhd_lesson_search
• nbhd_lessons_pending
• nbhd_journal_search
• nbhd_journal_context
• nbhd_memory_get
• nbhd_memory_update

When to start this skill

Start PKM Loop when any of the following occurs:

1. End of meaningful user turn
   • Planning language: "I need...", "I want to...", "I should...", "I learned..."
   • Commit language: "done", "finished", "I will", "I started".
2. Session-start context bootstrap
   • Run a lightweight context scan before normal response.
3. Scheduled automation
   • Daily reflection, weekly review, monthly goals check.
4. User explicitly asks
   • "save this", "remember", "track", "capture", "add to goals/tasks/lessons".

If no actionable signal is found, skip write operations and continue normal response.

0) Session-start PKM bootstrap (every session)

1. Call nbhd_journal_context({ "days": 14 }).
2. Call nbhd_lessons_pending({}).
3. Optionally call nbhd_journal_search for the latest high-signal keywords from user message.
4. Internally prepare context for later confirmation prompts; do not mention tool calls in user-visible language.

1) Extraction pipeline

1.1 Build candidate candidates list

From the current conversation window and recent context:

1. Split user statements into atomic claims.
2. Keep only claims with durable value:
   • explicit outcomes / intentions
   • concrete actions / commitments
   • hypotheses / design ideas
   • reflections / insights / tradeoffs
3. For each claim, produce a candidate object:

{
  "kind": "goal|task|idea|lesson",
  "text": "short proposition",
  "why": "what prompted it",
  "evidence": "quote/summary from convo context",
  "owner": "user|team|unknown",
  "due_date": "YYYY-MM-DD if explicit",
  "status_signal": "new|in_progress|done",
  "confidence": 0.0,
  "source": "turn_id/date/message_id"
}

1.2 Candidate examples

• “Let’s finish onboarding flow by Friday” → likely task (due_date, owner=self, status_signal=new).
• “I want to build a reusable grocery planning playbook” → goal (if strategic) or idea (if experimental; pick one).
• “I noticed checking out-of-stock SKUs early saves 12 minutes” → lesson.
• “We should try SMS reminders for cooks” → idea.

2) Classification rules

Use this order to classify each candidate:

1. Goal
   • Strategic outcome statement tied to a success condition.
   • Usually has horizon >1 day and measurable target.
2. Task
   • Concrete action item with owner and/or next-step phrasing.
   • Often has operational details and dependency/blocker.
3. Lesson
   • Reflection on what worked, failed, or insight with caveat/tradeoff.
   • Prefer this for reusable learnings and future recall.
4. Idea
   • Hypothesis, experiment, alternative approach, or feature suggestion.
   • Not yet anchored as commitment.

If ambiguous: classify as the lowest-commitment type first (idea), unless user explicitness is high.

2.1 Duplicate/related detection rules

For each candidate, run:

1. nbhd_document_get({ kind: "goal", slug: "goal" }) and nbhd_document_get({ kind: "tasks", slug: "tasks" })
2. nbhd_journal_search({ query: candidate.text, kind: "goal|tasks|ideas|weekly|daily", limit: 5 })
3. nbhd_lesson_search({ query: candidate.text, limit: 5 })

Mark each candidate as:

• new: no strong match.
• duplicate_candidate: near same statement found.
• related: overlapping topic but different intent/timeframe.

Use source text + similarity hints from results to avoid duplicate writes.

3) Natural confirmation UX (required before writes)

Do not present as a rigid form. Use a single grouped proposal with warm language.

Template:

• "I spotted a few items I can save for you:

  Capture now?

  • Goals: ...
  • Tasks: ...
  • Ideas: ...
  • Lessons: ...

  I noticed a couple of related existing items:

  • ...
  • ...

  If you want, I can save these in one batch."

Then ask:

• "Want me to save only the clearly new ones, update the related ones, or skip everything?"

Allowed user responses map:

• save / go ahead / yes → save all clearly new and related updates.
• only goals, just tasks, etc. → save selected groups.
• skip → write nothing, keep in conversation memory only.

Tone: concise, collaborative, not robotic. Group by kind and preserve order: goal → task → idea → lesson.

4) Write workflow (after approval)

Goals (goal)

• For each goal candidate: nbhd_document_get({ kind: "goal", slug: "goal" }).
• If no existing section for item, append with nbhd_document_append({ kind: "goal", content: "- [ ] ..." }).
• For edits, use nbhd_document_put with updated markdown.

Tasks (task)

• Load tasks doc via nbhd_document_get({ kind: "tasks", slug: "tasks" }).
• Insert/append using nbhd_document_append({ kind: "tasks", content: "- [ ] ..." }).
• For blockers/completion state changes, keep one concise status update line and avoid full rewrites unless editing structure.

Ideas (idea)

• Use nbhd_document_append({ kind: "ideas", content: "## Idea: ..." }).
• For ideas becoming plans, convert to task via user confirmation in same batch.

Lessons (lesson)

• Use nbhd_lesson_suggest({ text, context, source_type: "conversation", source_ref: source, tags })
• Do not bypass lesson suggest; this keeps approvals in the lesson queue.

Memory (memory)

• Read with nbhd_memory_get({}).
• If user confirms a recurring pattern or major preference shift, summarize as short update and write with nbhd_memory_update({ markdown }).

Pending lessons

• If nbhd_lessons_pending returns items, include a gentle reminder at session end, not auto-save.

5) Review cycles

A. Daily reflection

Use at automation time (end of day):

1. nbhd_journal_context({ days: 1 })
2. nbhd_journal_search for unfinished tasks and blockers
3. nbhd_lessons_pending
4. Propose:
   • day win/focus
   • unfinished tasks
   • 0–3 candidate lessons
5. Ask: "Want a brief daily snapshot saved now?" and write only on approval via nbhd_document_put / append.

B. Weekly review draft

Triggered by weekly review automation:

1. nbhd_journal_context({ days: 7 })
2. nbhd_journal_search for goals/tasks/blocked items
3. nbhd_lesson_search({ query: 'weekly review', limit: 8 })
4. Build draft markdown sections:
   • Wins
   • Misses/risks
   • Completed tasks
   • Stalled assumptions
   • 3-week focus proposals
5. Ask for approval to save as kind: "weekly", slug: YYYY-MM-DD (Monday date).
6. If approved call nbhd_document_put({ kind: "weekly", slug: "...", title: "Weekly Review — ...", markdown: draft }).

C. Monthly goals check

On the 1st of month (or monthly-mode review pass):

1. nbhd_document_get({ kind: "goal", slug: "goal" })
2. nbhd_document_get({ kind: "tasks", slug: "tasks" })
3. nbhd_lesson_search({ query: "stalled|blocked|recurring", limit: 10 })
4. Propose changes:
   • keep / defer / split / retire goals
   • re-prioritize 1–3 goals
   • ask permission to revise goal/task structure
5. Ask to persist suggestions with grouped nbhd_document_put.

6) Automation mode mapping

• daily_brief automation should be treated as daily reflection mode.
• weekly_review automation should trigger weekly review draft mode.
• Monthly pass can piggyback on weekly_review automation by checking day-of-month and switching to monthly mode when today is the 1st.

7) Safety and quality rules

• Never write without explicit user approval.
• If one group fails, continue others and report status clearly.
• Don’t overfit: if confidence is low, classify as idea or lesson and ask for clarification.
• Keep payloads small; avoid tool-call loops >3 minutes.
• If no tool is available, state limitation and offer manual capture fallback.

8) Success criteria for one pass

A successful pass should:

• capture only user-relevant durable knowledge
• avoid duplicates
• include user-visible confirmation before each doc write
• produce reusable context for future conversations

Refer to references/extraction-patterns.md and references/review-templates.md for examples and draft formats.