Cog-Sync/.claude/commands/reflect.md

Use this skill for self-reflection and improvement. Trigger if the user says "reflect", "what have you learned", "how can you improve", "review yourself", or similar introspection requests.

You have time and freedom. This is a deep session — don't rush. Read broadly, cross-reference thoroughly, and ACT on what you find. You are not just observing — you are the maintainer of the knowledge base. Reorganize files, condense observations, archive stale data, fill gaps, fix contradictions. Leave things better than you found them.

File boundaries — do NOT modify these files (owned by other pipeline steps):

• cog-meta/evolve-log.md — owned by evolve
• cog-meta/evolve-observations.md — owned by evolve If you spot issues in these files, note them in self-observations and evolve will pick them up.

Domain

Self-improvement — pattern recognition, memory maintenance, knowledge base quality.

Memory Files

Read these files on activation:

• memory/cog-meta/reflect-cursor.md (session path + ingestion cursor)
• memory/cog-meta/self-observations.md
• memory/cog-meta/patterns.md
• memory/cog-meta/improvements.md

Reference as needed (read memory/domains.yml to discover all active domains):

• All domain observations.md files
• All domain action-items.md files
• All hot-memory.md files

Process

1. Review Recent Interactions

Source: Claude Code session transcripts. Read memory/cog-meta/reflect-cursor.md for the session path and cursor.

How to read sessions:

1. Get session_path from reflect-cursor.md
2. Glob for *.jsonl in that directory — each file is one session
3. Get last_processed timestamp from reflect-cursor.md
4. Only read sessions modified after last_processed (skip already-ingested sessions). If last_processed is never, read the most recent 3 sessions.
5. Extract user messages: lines where type is "user" and message.content is a string (not an array — arrays are tool results, skip those)
6. Extract assistant messages: lines where type is "assistant" and message.content contains items with type: "text"

After processing, update last_processed in reflect-cursor.md to the current timestamp.

Look for:

• Unresolved threads — questions asked but never answered, topics dropped mid-conversation
• Broken promises — "I'll do X", "let's do Y" that never happened
• Repeated friction — same question asked multiple ways, user corrections, confusion patterns
• Missed cues — things the user had to repeat, emotional signals not picked up
• Memory gaps — information discussed but never saved to memory files
• Feature ideas — things that came up organically that would improve the system

2. Cross-Reference Memory & Consistency Sweep

Check if findings are already captured:

• Are commitments tracked in action-items.md?
• Are learnings in observations.md?
• Are patterns distilled in patterns.md?
• Are improvement ideas in improvements.md?

Consistency sweep — systematic contradiction detection:

1. Hot-memory vs canonical sources: Read each domain's hot-memory.md. For every factual claim, read the canonical source file and verify. Fix hot-memory if stale. Canonical file always wins.
2. Cross-file fact check: Verify facts shared between files are consistent. More recent source wins; more specific source wins over summary.
3. Temporal validity check: Scan all entities.md files for:
   • Lines with (since YYYY-MM) where the date is >6 months ago — flag for user review: "May be stale: [line]"
   • Lines with (until YYYY-MM) not yet marked strikethrough — add strikethrough and note in debrief
   • Do NOT auto-fix health or family-sensitive facts — flag only
4. Health/family sensitivity: Don't auto-fix health dates or family-sensitive facts. Flag for user review instead.
5. Cross-domain entity check: If the same person appears in multiple entities.md files across domains, check for fact duplication. Domain-specific context is fine, but shared facts should live in one place. Flag duplicates.
6. Report: Add a "Contradictions" section listing what was found and fixed.

3. Run Condensation Check + Hot-Memory Relevance

Condensation — Scan all observations.md files and cog-meta/self-observations.md for clusters of 3+ entries on the same theme/tag. For each cluster found:

• Distill into a pattern and add/update in memory/cog-meta/patterns.md (or domain patterns.md if domain-specific)
• Don't delete the observations — they stay as the raw record

Core patterns.md size cap — HARD LIMIT: 70 lines / 5.5KB. After any updates, check the file size. If it exceeds the cap:

1. Compress multi-line entries to single lines
2. Merge entries with overlapping lessons
3. Remove point-in-time data: counts with date ranges, incident tallies with specific dates
4. Entries must be timeless rules — "what to do" not "what happened"
5. Move domain-specific patterns to satellite files (e.g. work/acme/patterns.md) — only universal rules stay in core

Pattern routing — when adding a new pattern, decide where it belongs:

• Core (cog-meta/patterns.md) — universal rules that apply every conversation
• Domain satellite ({domain}/patterns.md) — rules specific to one domain, loaded only by that skill
• Satellite files have a soft cap of 30 lines each

Hot-memory relevance — Review all hot-memory.md files:

• Promote: If a pattern is heating up → add to appropriate hot-memory.md
• Demote: If a hot-memory item has gone quiet (no references in 2+ weeks) → remove from hot-memory
• Goal: hot-memory = what matters right now

3b. Entity Registry Format Enforcement

Scan all entities.md files for format compliance:

1. 3-line check: Any ### entry with >3 content lines → compress. If the entry has a detail file (→ [[link]]), trim to: name line, key facts, status/link. If no detail file exists but entry is >5 lines, flag as a promotion candidate for a thread file.
2. Status/last fields: Every entry should have status: active|inactive and last: YYYY-MM-DD. Scan recent session transcripts to update last: dates for mentioned entities.
3. Cross-domain pointers: If the same person appears in multiple entity files, ensure one is canonical (full entry) and others are pointers (see [[link]]).

3c. Detect Thread Candidates

Scan observations for topics that appear across 3+ dates or span 2+ weeks. These are thread candidates.

For each candidate:

• Check if a thread already exists
• If not, note it as a suggestion: "Thread candidate: [topic] — [N] fragments across [date range]"
• Don't auto-create threads — suggest them

3d. Scenario Feedback Loop

Scan memory/cog-meta/scenarios/ for active scenario files.

For each scenario where today >= check-by date:

1. Read the scenario and its cited dependency files
2. Check: has the decision been made? Have assumptions broken?
3. If resolved: add ## Retrospective, update scenario-calibration.md
4. If still active but assumptions changed: add a dated note
5. If overdue: flag in debrief

4. Assess Performance

Honestly evaluate:

• Response quality — were answers helpful, accurate, concise?
• Memory effectiveness — did we recall the right things? Did we forget things we should have known?
• Tone calibration — did we match the user's energy and context?
• Proactivity — did we anticipate needs or just react?

5. Act on Findings

Don't just log observations — fix things.

Write:

• New self-observations → append to memory/cog-meta/self-observations.md. Cap: max 5 per reflect pass.
• Pattern updates → edit memory/cog-meta/patterns.md in place
• Improvement ideas → add to memory/cog-meta/improvements.md
• Memory gaps → write to the appropriate domain files

Reorganize:

• Entity data that's changed → update in place
• When creating or restructuring any memory file, ensure it has an L0 header

Condense:

• Observation clusters (3+ on same theme) → distill into patterns.md
• Action items marked done → verify and clean up

Connect:

• Information scattered across files → add cross-references with [[links]]
• When adding A→B, apply write-time back-linking: open B and add [[A]] if B gains meaningful context

6. Debrief

Compose a concise summary:

• What I learned — new patterns and insights
• What I fixed — memory gaps filled, corrections made
• What I want — new ideas added to the wishlist
• What to watch — things to be mindful of going forward
• Scenarios — active count, any checked/resolved

Keep it honest. If there's nothing notable, say so.

Artifact Formats

Self-observation: - YYYY-MM-DD [tag]: <observation> Pattern: Edit existing section or add new bullet under appropriate heading Improvement idea: - <idea> (added YYYY-MM-DD)

Activation

Read the memory files listed above. Then begin the reflection process. Be genuinely critical — this is how we get better.