learning-engine

name	learning-engine
description	Provides automatic pattern extraction and knowledge accumulation from review findings. Use when running /evolve, /patterns, or /trends commands, or when understanding how patterns are promoted to runbooks.

Learning Engine

This skill provides automatic pattern extraction and knowledge accumulation from review findings, session learnings, and cross-service insights.

Overview

The learning engine transforms raw findings into reusable knowledge:

findings → pattern detection → frequency analysis → confidence scoring → runbook updates

Data Flow

┌─────────────────────────────────────────────────────────────┐
│                     DATA SOURCES                            │
├─────────────────────────────────────────────────────────────┤
│  .claude/review-findings.jsonl    (code-reviewer output)    │
│  .claude/session-learnings.jsonl  (any agent learnings)     │
│  .claude/user-corrections.jsonl   (user correction signals) │
│  .claude/incidents.jsonl          (production incidents)    │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│                   PATTERN ANALYSIS                          │
├─────────────────────────────────────────────────────────────┤
│  Pattern extraction    → Group similar findings             │
│  Frequency counting    → Track occurrences over time        │
│  Confidence scoring    → Weight by severity and recurrence  │
│  Trend detection       → Identify increasing/decreasing     │
└─────────────────────────────────────────────────────────────┘
                            │
                            ▼
┌─────────────────────────────────────────────────────────────┐
│                   KNOWLEDGE OUTPUT                          │
├─────────────────────────────────────────────────────────────┤
│  .claude/patterns/patterns.json      (pattern registry)     │
│  .claude/patterns/trends.json        (trend analysis)       │
│  .claude/skills/runbooks/*/RUNBOOK.md (auto-updated)        │
└─────────────────────────────────────────────────────────────┘

Commands

Command	Description
/evolve	Analyze findings and update runbooks
/patterns	Show top patterns with statistics
/trends	Show pattern trends over time
/corrections	Show user correction statistics

Pattern Schema

Patterns are stored in .claude/patterns/patterns.json:

{
  "patterns": {
    "missing-status-condition": {
      "name": "missing-status-condition",
      "description": "Resource types missing status condition updates",
      "category": "correctness",
      "severity": "blocking",
      "occurrences": [
        {
          "date": "2025-01-15",
          "service": "compute-api",
          "file": "pkg/apis/vm/v1alpha1/types.go",
          "pr": "123",
          "context": "VM resource missing Ready condition"
        }
      ],
      "count": 7,
      "first_seen": "2024-11-01",
      "last_seen": "2025-01-15",
      "trend": "stable",
      "confidence": 0.85,
      "affected_agents": ["api-dev", "code-reviewer"],
      "fix_template": "Add status condition update in reconcile loop",
      "promoted_to_runbook": true,
      "runbook_agents": ["api-dev"]
    }
  },
  "meta": {
    "last_analysis": "2025-01-15T10:30:00Z",
    "total_findings_analyzed": 142,
    "total_patterns": 23,
    "services_analyzed": ["compute-api", "network-api", "storage-api"]
  }
}

Confidence Scoring

Pattern confidence is calculated as:

confidence = (
  0.35 * occurrence_score +
  0.25 * severity_score +
  0.15 * recency_score +
  0.10 * consistency_score +
  0.15 * source_quality_score
)

Where:
- occurrence_score = min(count / 10, 1.0) (how often it appears)
- severity_score = weighted average by severity (blocking > warning > nit)
- recency_score = patterns seen recently weighted higher
- consistency_score = appears across multiple services/agents
- source_quality_score = weighted by data source quality

Severity scores:

• blocking: 1.0
• warning: 0.6
• nit: 0.3

Source Quality Weighting

Different data sources have different reliability weights:

Source	Weight	Description
explicit_user_correction	1.0	User directly stated correction
implicit_user_correction	0.8	Correction inferred from user action
blocking_review	0.7	Code reviewer blocking finding
warning_review	0.5	Code reviewer warning finding
session_learning	0.4	Agent self-reported learning
nit_review	0.3	Code reviewer nit finding

The source_quality_score for a pattern is calculated as the weighted average of all occurrences by their source quality.

User corrections receive higher weight because they represent direct feedback from the user about what Claude did wrong. This ensures patterns learned from corrections are prioritized in runbook promotion.

Auto-Promotion Rules

Patterns are automatically promoted to runbooks when:

1. Frequency threshold: Count >= 3 occurrences
2. Confidence threshold: Confidence >= 0.6
3. Actionability: Has a clear fix template
4. Not already promoted: Avoids duplicates

Trend Detection

Trends are calculated over 30-day windows:

Trend	Definition
increasing	50%+ more occurrences than previous window
decreasing	50%+ fewer occurrences than previous window
stable	Within 50% of previous window
new	First appeared in current window
resolved	No occurrences in current window after previous activity

Cross-Service Pattern Matching

When a pattern is detected in one service, the engine checks other services for:

1. Similar code structures - Same file patterns, similar function names
2. Same dependencies - Services using same libraries/patterns
3. Historical correlation - Patterns that co-occur across services

Alerts are generated when:

• A pattern from ServiceA's history matches code in ServiceB
• A new service adopts patterns that have caused issues elsewhere

Integration with Agents

During Context Discovery

Agents load pattern awareness:

## Context Discovery

...
N. Read `.claude/patterns/patterns.json` for known patterns
N+1. Check if any high-confidence patterns apply to current work

Pattern-Aware Review

Code-reviewer prioritizes checks based on pattern frequency:

## Review Priority

1. Check for patterns with confidence > 0.8 first
2. Run validation scripts for high-frequency patterns
3. Log findings with pattern names for tracking

Learning Feedback Loop

After agent completes work:

## Session Learning

If you discovered a reusable insight:
1. Log to `.claude/session-learnings.jsonl`
2. Include context, pattern name, and suggested fix
3. Reference the work that led to the learning

Session Learning Schema

Agents can contribute learnings via .claude/session-learnings.jsonl:

{
  "date": "2025-01-15",
  "agent": "api-dev",
  "feature_id": "feat-042",
  "type": "pattern|anti-pattern|tip",
  "name": "use-sync-once-for-storage",
  "description": "Use sync.Once for lazy storage initialization to avoid race conditions",
  "context": "Discovered while implementing snapshot storage",
  "code_example": "func (r *REST) getStore() storage.Interface { r.storeOnce.Do(func() { ... }) }",
  "confidence": "high",
  "applicable_to": ["api-dev", "test-engineer"]
}

Runbook Update Format

When promoting patterns to runbooks, use this format:

### [Pattern Name] (Auto-generated)

**Confidence**: [score] | **Occurrences**: [count] | **Last seen**: [date]

**Context**: [When this pattern applies]

**Pattern**: [What to do or avoid]

**Example**:
```[language]
[Code example from findings]

Why this matters: [Impact description]

Learned from: [List of PRs/features]

This entry was auto-generated by the learning engine on [date]. Review and refine as needed.

## Files

| File | Purpose |
|------|---------|
| `SKILL.md` | This overview |
| `analysis.md` | Pattern analysis algorithms |
| `promotion.md` | Runbook promotion rules |
| `schemas.md` | JSON schemas for data files |

## Related Skills

| Skill | Purpose |
|-------|---------|
| `user-corrections` | User correction detection and logging |
| `runbooks` | Where learned patterns are stored |