Agent Learning

Agent Learning Patterns

What It Does

Defines when to record learnings, quality standards for entries, and retrieval strategies. Loaded by memory-related commands and agents for consistent learning behavior.

When to Use

Use when yellow-ruvector plugin commands or agents need guidance on learning triggers, quality gates, or retrieval ranking.

Usage

This skill is not user-invocable. It provides shared context for the yellow-ruvector plugin's learning workflows.

Learning Triggers

Record a Context Entry When (type: context)

• Test failure that required a code fix
• Lint or type error that needed resolution
• User corrected the agent's approach
• Agent retried after an error and succeeded
• Build or deploy failure

Record a Decision Entry When (type: decision)

• Complex operation succeeded on first attempt
• User explicitly praised a technique
• Clean solution to a recurring problem
• Novel approach that worked well

Record a Code Entry When (type: code)

• Debugging revealed "X caused Y"
• Configuration change resolved an issue
• Performance investigation found a bottleneck
• Dependency update triggered a regression

Skip Recording When

• Trivial file reads or searches
• Simple, routine operations
• Information already captured in a previous entry
• The operation has no notable outcome

Quality Gates

Every learning entry must meet these criteria:

1. Minimum length: 20 words in the content field
2. Structure: Must include context (what happened), insight (why), and action (what to do)
3. Specificity: Reference concrete files, functions, or error messages — not vague generalizations
4. Actionability: The "action" must be something a future agent can follow

Good Examples

Context:

"Test auth.test.ts:testTokenRefresh failed because the mock JWT was expired. Fix: always set mock token expiry to Date.now() + 3600000 instead of a hardcoded timestamp. Applied in commit abc123."

Decision:

"Batch database inserts wrapped in a transaction are 10x faster than individual inserts for the users table. Use db.transaction(async (tx) => { ... }) pattern when inserting more than 5 rows."

Bad Examples

"Fixed a bug" — No context, no insight, no action. "Tests should pass" — Not specific, not actionable.

Retrieval Strategy

Use Reciprocal Rank Fusion (RRF) to combine multiple ranking signals:

final_score = sum(1 / (rank_i + 60)) for each signal i

Ranking Signals

1. Semantic similarity — Vector cosine distance to query
2. Recency — Time-decay: newer entries rank higher

(Retrieval frequency is not a usable signal: the recall result shape exposes only content, type, score, and created — there is no retrieval-count field to read.)

Context Budget

• Load max 5 learnings per session start (via SessionStart hook)
• Prioritize by RRF score
• Each loaded learning should be a concise, actionable reminder

Dedup Threshold

• Cosine similarity > 0.82 = likely duplicate (canonical constant from the memory-query skill's protocol constants)
• Warn user before storing near-duplicates
• Don't apply hard threshold on search results — always return top-k, filter below 0.5

Skill Promotion

When a recurring context pattern appears 3+ times across sessions, consider promoting it to a reusable decision entry:

1. Identify the recurring pattern from prior context entries
2. Formulate as a positive "do this" rule (not "don't do that")
3. Store as a decision entry with broader context
4. Optionally add to project CLAUDE.md if it's a project-wide convention