Reflect - Agent Self-Improvement Skill

Transform your AI assistant into a continuously improving partner. Every correction becomes a permanent improvement that persists across all future sessions.

Quick Reference

| Command | Action |

|---------|--------|

| reflect | Analyze conversation for learnings |

| reflect on | Enable auto-reflection |

| reflect off | Disable auto-reflection |

| reflect status | Show state and metrics |

| reflect review | Review pending learnings |

When to Use

• After completing complex tasks
• When user explicitly corrects behavior ("never do X", "always Y")
• At session boundaries or before context compaction
• When successful patterns are worth preserving

Workflow

Step 1: Scan Conversation for Signals

Analyze the conversation for correction signals and learning opportunities.

Signal Confidence Levels:

| Confidence | Triggers | Examples |

|------------|----------|----------|

| HIGH | Explicit corrections | "never", "always", "wrong", "stop", "the rule is" |

| MEDIUM | Approved approaches | "perfect", "exactly", "that's right", accepted output |

| LOW | Observations | Patterns that worked but not explicitly validated |

See signal_patterns.md for full detection rules.

Step 2: Classify & Match to Target Files

Map each signal to the appropriate target:

| Category | Target Files |

|----------|--------------|

| Code Style | code-reviewer, backend-developer, frontend-developer |

| Architecture | solution-architect, api-architect, architecture-reviewer |

| Process | CLAUDE.md, orchestrator agents |

| Domain | Domain-specific agents, CLAUDE.md |

| Tools | CLAUDE.md, relevant specialists |

| New Skill | Create new skill file |

See agent_mappings.md for mapping rules.

Step 3: Check for Skill-Worthy Signals

Some learnings should become new skills rather than agent updates:

Skill-Worthy Criteria:

• Non-obvious debugging (>10 min investigation)
• Misleading error (root cause different from message)
• Workaround discovered through experimentation
• Configuration insight (differs from documented)
• Reusable pattern (helps in similar situations)

Quality Gates (must pass all):

• [ ] Reusable: Will help with future tasks
• [ ] Non-trivial: Requires discovery, not just docs
• [ ] Specific: Can describe exact trigger conditions
• [ ] Verified: Solution actually worked
• [ ] No duplication: Doesn't exist already

Step 4: Generate Proposals

Present findings in structured format:

# Reflection Analysis

## Session Context
- **Date**: [timestamp]
- **Messages Analyzed**: [count]

## Signals Detected

| # | Signal | Confidence | Source Quote | Category |
|---|--------|------------|--------------|----------|
| 1 | [learning] | HIGH | "[exact words]" | Code Style |

## Proposed Changes

### Change 1: Update [agent-name]
**Target**: `[file path]`
**Section**: [section name]
**Confidence**: HIGH

+ New rule from learning

## Review Prompt
Apply these changes? (Y/N/modify/1,2,3)

Step 5: Apply with User Approval

On Y (approve):

1. Apply each change using Edit tool
2. Commit with descriptive message
3. Update metrics

On N (reject):

1. Discard proposed changes
2. Log rejection for analysis

On modify:

1. Present each change individually
2. Allow editing before applying

On selective (e.g., 1,3):

1. Apply only specified changes
2. Commit partial updates

State Management

State is stored in ~/.reflect/ (configurable via REFLECT_STATE_DIR):

# reflect-state.yaml
auto_reflect: false
last_reflection: "2026-01-26T10:30:00Z"
pending_reviews: []

Metrics Tracking

# reflect-metrics.yaml
total_sessions_analyzed: 42
total_signals_detected: 156
total_changes_accepted: 89
acceptance_rate: 78%
confidence_breakdown:
  high: 45
  medium: 32
  low: 12
most_updated_agents:
  code-reviewer: 23
  backend-developer: 18
skills_created: 5

Safety Guardrails

Human-in-the-Loop

• NEVER apply changes without explicit user approval
• Always show full diff before applying
• Allow selective application

Incremental Updates

• ONLY add to existing sections
• NEVER delete or rewrite existing rules
• Preserve original structure

Conflict Detection

• Check if proposed rule contradicts existing
• Warn user if conflict detected
• Suggest resolution strategy

Output Locations

Project-level (versioned with repo):

• .claude/reflections/YYYY-MM-DD_HH-MM-SS.md - Full reflection
• .claude/skills/{name}/SKILL.md - New skills

Global (user-level):

• ~/.reflect/learnings.yaml - Learning log
• ~/.reflect/reflect-metrics.yaml - Aggregate metrics

Examples

Example 1: Code Style Correction

User says: "Never use var in TypeScript, always use const or let"

Signal detected:

• Confidence: HIGH (explicit "never" + "always")
• Category: Code Style
• Target: frontend-developer.md

Proposed change:

## Style Guidelines
+ * Use `const` or `let` instead of `var` in TypeScript

Example 2: Process Preference

User says: "Always run tests before committing"

Signal detected:

• Confidence: HIGH (explicit "always")
• Category: Process
• Target: CLAUDE.md

Proposed change:

## Commit Hygiene
+ * Run test suite before creating commits

Example 3: New Skill from Debugging

Context: Spent 30 minutes debugging a React hydration mismatch

Signal detected:

• Confidence: HIGH (non-trivial debugging)
• Category: New Skill
• Quality gates: All passed

Proposed skill: react-hydration-fix/SKILL.md

Troubleshooting

No signals detected:

• Session may not have had corrections
• Check if using natural language corrections

Conflict warning:

• Review the existing rule cited
• Decide if new rule should override
• Can modify before applying

Agent file not found:

• Check agent name spelling
• May need to create agent file first