| name | memento-reflect |
| description | Use after completing any significant task to self-evaluate what worked or failed, update learned skill metrics, and discover reusable patterns worth capturing as new skills |
Memento Reflect
Post-task self-evaluation that closes the learning loop. This is the Write phase of Read-Write Reflective Learning.
Protocol
1. Trace Analysis
Review the current conversation and identify:
- What task was performed?
- Which tools and approaches were used?
- Were any learned skills referenced or applied?
- How many iterations/retries were needed?
2. Self-Evaluation
Judge the outcome using these criteria:
- User accepted result without corrections? — strong success signal
- Completed in one pass? — success. Multiple retries — partial or failure.
- Errors encountered during execution? — note which phase failed
- Final result meets the original request? — ultimate success criterion
Verdict: success or failure with one-sentence reasoning.
3. Update Metrics
If a learned skill was used during this task, update its metrics file.
Read the appropriate metrics.json (~/.claude/memento/metrics.json for global skills, .claude/memento/metrics.json for project skills). Then update the skill entry:
usage_count: increment by 1success_count: increment if successfailure_count: increment if failurelast_used: today's dateutility: success_count / (success_count + failure_count)trigger_log: append {"task": "short description", "result": "success|failure", "date": "today"} — keep max 5 most recent
Write the updated JSON back. Create the directory and file if they don't exist yet — initialize with:
{
"version": 1,
"config": {
"utility_threshold": 0.4,
"min_samples_for_judgment": 3,
"prune_after_days_unused": 60
},
"skills": {}
}
4. Pattern Discovery
Ask yourself: "Did I do something reusable that no existing skill covers?"
Criteria for a new skill:
- The pattern would apply to future tasks (not one-off)
- It required non-obvious steps or domain knowledge
- An agent without this experience would likely struggle
If yes — tell the user what pattern you found and suggest: "Want me to capture this as a learned skill? I'll use /memento create."
5. Failure-Driven Optimization
If a learned skill was used but the task failed:
- Was the skill's Procedure incomplete or wrong?
- Was it applied to the wrong type of task (routing problem)?
If the skill itself is at fault — suggest: "The {skill-name} skill may need improvement. Want me to run /memento optimize {skill-name}?"
Important
- Always show the user what you're updating before writing
- Never silently modify metrics — announce changes
- One reflect per task — don't batch multiple tasks
