Self review

Self Review

If $ARGUMENTS is empty or non-numeric, analyze the current conversation. If it is a number, treat it as a request to mine that many days of historical sessions.

Current Conversation Mode

For each mistake, capture:

1. What the assistant concluded
2. What the actual answer turned out to be
3. What information was missing
4. Whether that information was discoverable
5. What concrete change would prevent the same mistake

Output shape:

### Mistake: <what went wrong>
**Assistant said:** "<quoted claim>"
**Actual answer:** "<what turned out to be true>"
**Missing info:** <missing context>
**Findable?** YES / NO
**Fix:** <skill, prompt, hook, data-source, or workflow change>

After listing mistakes, propose concrete edits and ask the user which to implement.

Historical Mode

Step 1: Pre-Filter

Use a mining script or equivalent search only as a pre-filter to find candidate sessions.

Skip frozen or retry-only sessions caused by infrastructure issues rather than assistant behavior.

Step 2: Deep Read

Read flagged sessions message by message. For each user intervention, ask whether the assistant could have already done that without being told.

Step 3: Choose The Right Fix Mechanism

Map the failure to the right control surface:

Mechanism	Best for
CLAUDE.md or equivalent global instruction	Universal behavior rules
Path-scoped rules	File-type-specific behavior
Skills	Situational knowledge
Memory	Persistent decisions or preferences
Hooks	Automated guardrails that must always run
Agents	Isolated delegated task types
Commands	Explicitly user-invoked prompts
Settings	Permissions, env vars, model config

Decision heuristic:

• "Assistant keeps forgetting X" -> Hook or always-loaded rule
• "Assistant should do X when editing Y" -> Path-scoped rule
• "Assistant lacks knowledge about X during Y" -> Skill
• "Assistant should delegate X" -> Agent plus routing rule
• "I want to run X on demand" -> Command
• "Remember this decision" -> Memory

Step 4: Present Findings With Examples

Every pattern must include concrete examples from transcripts. Do not present regex counts as findings.

For each pattern include:

• What happens
• Two or three concrete examples
• Proposed fix: bucket, file, specific edit, and why it helps

Step 5: Validate Against User Experience

Check with the user that the proposed pattern matches real experience before editing prompts or skills.