reflect:recall

/reflect:recall — Retrieve relevant prior learnings

Queries the global learnings KB (GraphRAG + vector) and surfaces the top-N most relevant learnings for the current work, reranked by confidence, recency, and tag overlap.

When to use

• Starting work in a project or on a new branch — "what do we know about X"
• Debugging a recurring issue — "have we seen this error before"
• Before implementing a feature — "has this pattern been tried"
• When the user references past work ("like we did in Y")

Also fires automatically via the SessionStart hook (see hooks/session_start_recall.py) with a 3-result cap, any confidence (reranked by confidence/recency/tag-overlap). This skill is the explicit, higher-limit path.

Quick reference

Invocation	Behavior
/reflect:recall <query>	Default — 10 results, any confidence, markdown out
/reflect:recall <query> --limit 5 --confidence HIGH	Tight filter
/reflect:recall <query> --mode local	Graph-neighborhood search (finds related concepts)
/reflect:recall <query> --mode global	Community-based (broad patterns)
/reflect:recall <query> --format json	Structured output for programmatic use
/reflect:recall <query> --no-cache	Skip cache, force fresh query

Workflow

1. Build the query — combine the user's question with project context: current cwd, branch name, any relevant tags the user mentioned.
2. Run recall — invoke {{HOME_TOOL_DIR}}/skills/recall/scripts/recall.py:

   uv run {{HOME_TOOL_DIR}}/skills/recall/scripts/recall.py "$QUERY" --limit 10 --format markdown
   

3. Inspect results — each result has [lrn-id], key insight, and how-to-apply.
4. Fetch full docs if needed — for any interesting learning ID, the user can run learnings search <id> or check ~/.learnings/documents/learnings/.

Query construction tips

• Short, focused queries beat long sentences (the backend does vector similarity).
• Include proper nouns: project names, tool names, error snippets.
• Add tags explicitly with --tags a,b,c for reranking boost.

Backend details

• Retrieval: wraps ~/.learnings/cli/learnings search as subprocess.
• Ranking: confidence × recency × (1 + tag_overlap_bonus).
  • Confidence: HIGH=1.0, MEDIUM=0.7, LOW=0.4
  • Recency: exp(-days_ago / 90), half-life ~60 days
  • Tag bonus: 0.1 × count(query_tags ∩ learning_tags)
• Cache: per-query SHA1 hash at ~/.reflect/recall_cache/, 1h TTL.
• Log: every recall is appended to ~/.reflect/recall_log.jsonl for future helpfulness analysis (Phase 6 of the retrieval plan).

Related

• /reflect:ingest — populate the KB
• /reflect-status — KB health, coverage, pending reviews
• SessionStart hook — auto-recall on project entry (see hooks/settings-snippet.json)