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Captures and learns from direct user corrections to Claude Code's outputs. Complements the code-reviewer-driven learning system with user-driven feedback for continuous improvement.
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Basé sur la classification professionnelle SOC
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| name | user-corrections |
| description | Captures and learns from direct user corrections to Claude Code's outputs. Complements the code-reviewer-driven learning system with user-driven feedback for continuous improvement. |
This skill captures and learns from direct user corrections to agent outputs, enabling continuous improvement through user-driven feedback.
User corrections are a high-value learning signal. When a user corrects Claude's output, it indicates a gap between expected and actual behavior that should inform future work.
User corrects Claude → Agent detects correction → Logs to user-corrections.jsonl
↓
/evolve analyzes alongside review-findings
↓
High-confidence corrections → runbooks
↓
/corrections command for periodic review
┌─────────────────────────────────────────────────────────────┐
│ DATA SOURCES │
├─────────────────────────────────────────────────────────────┤
│ .claude/review-findings.jsonl (code-reviewer output) │
│ .claude/session-learnings.jsonl (any agent learnings) │
│ .claude/user-corrections.jsonl (user correction signals) │ ← NEW
│ .claude/incidents.jsonl (production incidents) │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ PATTERN ANALYSIS │
├─────────────────────────────────────────────────────────────┤
│ Pattern extraction → Group similar corrections │
│ Source weighting → Weight by correction source │
│ Frequency counting → Track occurrences over time │
│ Confidence scoring → Combine all signals │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ KNOWLEDGE OUTPUT │
├─────────────────────────────────────────────────────────────┤
│ .claude/patterns/patterns.json (pattern registry) │
│ .claude/patterns/trends.json (trend analysis) │
│ .claude/skills/runbooks/*/RUNBOOK.md (auto-updated) │
└─────────────────────────────────────────────────────────────┘
| Type | Definition | Example |
|---|---|---|
code_quality | User fixes bugs, style, or logic | User fixes off-by-one error Claude introduced |
code_completeness | User adds code Claude missed | User adds error handling Claude skipped |
approach_rejection | User rejects overall approach | "Let's try a different approach" |
expectation_mismatch | Claude did something unexpected | "I didn't ask for that" |
communication_gap | Claude misunderstood request | User rephrases same request |
preference_conflict | User prefers different pattern | "I prefer X over Y" |
| Command | Description |
|---|---|
/corrections | Show recent corrections summary |
/corrections --agent <name> | Filter by agent |
/corrections --type <type> | Filter by correction type |
/corrections --analyze | Run pattern extraction |
/evolve | Analyze corrections alongside findings |
Corrections are weighted differently based on source quality:
| Source | Weight | Rationale |
|---|---|---|
| Explicit user correction | 1.0 | Direct feedback, highest confidence |
| Implicit user correction | 0.8 | User action indicates issue |
| Blocking review finding | 0.7 | Code reviewer found issue |
| Warning review finding | 0.5 | Less severe but notable |
| Session learning | 0.4 | Agent self-observation |
| Nit review finding | 0.3 | Minor conventions |
These weights apply to the source_quality_score component of confidence calculation.
Agents detect corrections in real-time:
## Correction Detection
When a user corrects your output:
1. Detect explicit signals ("wrong", "no", "actually...")
2. Detect implicit signals (user edits your code, re-requests differently)
3. Log correction to `.claude/user-corrections.jsonl`
4. Continue with corrected approach
Agents check for patterns from corrections:
## Context Discovery
...
N. Read `.claude/patterns/patterns.json` for known patterns
N+1. Note patterns with high correction-based confidence
N+2. Apply lessons from user corrections to current work
User corrections feed into the same learning engine as review findings:
.claude/user-corrections.jsonl/evolve groups similar corrections| File | Purpose |
|---|---|
SKILL.md | This overview |
schemas.md | JSON schema for user-corrections.jsonl |
detection.md | How agents detect and log corrections |
learning-engine — Pattern analysis and runbook promotionrunbooks — Where learned patterns are stored