name: reflect description: Self-improvement through conversation analysis. Extracts learnings from corrections and success patterns, permanently encoding them into agent definitions. Philosophy - Correct once, never again. version: "2.0.0" user-invocable: true triggers: - reflect - self-reflect - review session - what did I learn - extract learnings - analyze corrections allowed-tools: - Read - Write - Edit - Grep - Glob - Bash metadata: clawdbot: emoji: "🪞" config: stateDirs: ["~/.reflect"]

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

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:

See signal_patterns.md for full detection rules.

Step 2: Classify & Match to Target Files

Map each signal to the appropriate target:

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

```diff
+ 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`):

```yaml
# 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

  小葱技能7w4.net有完整的技能分类。

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

这个 Skill 的设计理念很好，文档非常详细，信号检测和分类的逻辑清晰，安全机制也很完善。不过它目前还只是个「使用手册」，没有实际的代码程序能用。换句话说，它告诉你应该怎么做，但没给你真正能运行的工具。对于想实际使用的用户来说，可能还需要等待开发者补充可执行的代码实现。