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Record and analyze recommendation sessions and feedback to drive the self-learning loop
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Checkpoint episodes and create handoff packs for multi-agent session continuity
Configure, test, benchmark, and manage embedding providers for semantic search and vector operations
Create and traverse typed relationships between episodes for dependency tracking and knowledge graphs
Manage episode tags for organization, filtering, and retrieval across the memory system
Generate task playbooks and explain patterns using learned strategies from episodic memory
Use and troubleshoot the Memory MCP server for episodic memory retrieval and pattern analysis. Use when working with MCP server tools, validating the MCP implementation, or debugging MCP server issues.
| name | recommendation-feedback |
| description | Record and analyze recommendation sessions and feedback to drive the self-learning loop |
1. Agent starts task → query_memory / recommend_patterns
2. System recommends patterns/playbooks → record_recommendation_session
3. Agent completes task → record_recommendation_feedback
4. System adjusts future recommendations based on outcomes
| Command | Purpose |
|---|---|
do-memory-cli feedback record-session -e <ID> -p <patterns> -P <playbooks> | Record what was recommended |
do-memory-cli feedback record-feedback -s <SESSION> -a <applied> -o <outcome> -m <msg> | Record what worked |
do-memory-cli feedback stats | View recommendation statistics |
| Tool | Parameters | Purpose |
|---|---|---|
record_recommendation_session | episode_id, patterns, playbooks | Log recommended items |
record_recommendation_feedback | session_id, applied, consulted, outcome, rating | Log what was used |
get_recommendation_stats | - | Aggregate effectiveness stats |
When patterns are recommended to an agent:
do-memory-cli feedback record-session \
--episode-id "abc-123" \
--patterns "pat-1,pat-2,pat-3" \
--playbooks "pb-1"
After the episode completes:
do-memory-cli feedback record-feedback \
--session "session-uuid" \
--applied "pat-1,pat-3" \
--consulted "ep-old-1" \
--outcome "success" \
--message "Pattern 1 was directly applicable, pat-2 was irrelevant" \
--rating 0.8
| Value | Meaning |
|---|---|
success | Task completed successfully using recommendations |
partial | Some recommendations helped, others didn't |
failure | Recommendations were not useful |
do-memory-cli feedback stats
Returns: total sessions, average rating, most-applied patterns, patterns with highest success correlation.
Feedback data directly influences rank_patterns() in future queries: