Reflect

Self-improvement through conversation analysis. Extracts learnings from corrections and success patterns, proposes updates to agent files or creates new skills. Philosophy: "Correct once, never again." Use when: (1) User explicitly corrects behavior ("never do X", "always Y"), (2) Session ending or context compaction, (3) User requests /reflect, (4) Successful pattern worth preserving.

Reflect - Self-Improvement Skill

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 low-confidence learnings
/reflect [agent]	Focus on specific agent

Core Philosophy

"Correct once, never again."

When users correct behavior, those corrections become permanent improvements encoded into the agent system - across all future sessions.

Workflow

Step 1: Initialize State

Check and initialize state files using the state manager:

bash

# Check for existing state
python scripts/state_manager.py init

# State directory is configurable via REFLECT_STATE_DIR env var
# Default: ~/.reflect/ (portable) or ~/.claude/session/ (Claude Code)

State includes:

• reflect-state.yaml - Toggle state, pending reviews
• reflect-metrics.yaml - Aggregate metrics
• learnings.yaml - Log of all applied learnings

Step 2: Scan Conversation for Signals

Use the signal detector to identify learnings:

bash

python scripts/signal_detector.py --input conversation.txt

Signal Confidence Levels

Confidence	Triggers	Examples
HIGH	Explicit corrections	"never", "always", "wrong", "stop", "the rule is"
MEDIUM	Approved approaches	"perfect", "exactly", accepted output
LOW	Observations	Patterns that worked, not validated

See signal_patterns.md for full detection rules.

Step 3: Classify & Match to Target Files

Map each signal to the appropriate target:

Learning Categories:

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	.claude/skills/{name}/SKILL.md

See agent_mappings.md for mapping rules.

Step 4: 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

See skill_template.md for skill creation guidelines.

Step 5: Generate Proposals

Produce output in this format:

markdown

# Reflection Analysis

## Session Context
- **Date**: [timestamp]
- **Messages Analyzed**: [count]
- **Focus**: [all agents OR specific agent name]

## Signals Detected

| # | Signal | Confidence | Source Quote | Category |
|---|--------|------------|--------------|----------|
| 1 | [learning] | HIGH | "[exact words]" | Code Style |
| 2 | [learning] | MEDIUM | "[context]" | Architecture |

## Proposed Agent Updates

### Change 1: Update [agent-name]

**Target**: `[file path]`
**Section**: [section name]
**Confidence**: [HIGH/MEDIUM/LOW]
**Rationale**: [why this change]

```diff
--- a/path/to/agent.md
+++ b/path/to/agent.md
@@ -82,6 +82,7 @@
 ## Section

 * Existing rule
+* New rule from learning

Proposed New Skills

Skill 1: [skill-name]

Quality Gate Check:

• Reusable: [why]
• Non-trivial: [why]
• Specific: [trigger conditions]
• Verified: [how verified]
• No duplication: [checked against]

Will create: .claude/skills/[skill-name]/SKILL.md

Conflict Check

• No conflicts with existing rules detected
• OR: Warning - potential conflict with [file:line]

Commit Message

text

reflect: add learnings from session [date]

Agent updates:
- [learning 1 summary]

New skills:
- [skill-name]: [brief description]

Extracted: [N] signals ([H] high, [M] medium, [L] low confidence)

Review Prompt

Apply these changes?

• Y - Apply all changes and commit
• N - Discard all changes
• modify - Adjust specific changes
• 1,3 - Apply only changes 1 and 3
• s1 - Apply only skill 1
• all-skills - Apply all skills, skip agent updates

text

### Step 6: Handle User Response

**On `Y` (approve):**
1. Apply each change using Edit tool
2. Run `git add` on modified files
3. Commit with generated message
4. Update learnings log
5. Update metrics

**On `N` (reject):**
1. Discard proposed changes
2. Log rejection for analysis
3. Ask if user wants to modify any signals

**On `modify`:**
1. Present each change individually
2. Allow editing the proposed addition
3. Reconfirm before applying

**On selective (e.g., `1,3`):**
1. Apply only specified changes
2. Log partial acceptance
3. Commit only applied changes

### Step 7: Update Metrics

```bash
python scripts/metrics_updater.py --accepted 3 --rejected 1 --confidence high:2,medium:1

Toggle Commands

Enable Auto-Reflection

bash

/reflect on
# Sets auto_reflect: true in state file
# Will trigger on PreCompact hook

Disable Auto-Reflection

bash

/reflect off
# Sets auto_reflect: false in state file

Check Status

bash

/reflect status
# Shows current state and metrics

Review Pending

bash

/reflect review
# Shows low-confidence learnings awaiting validation

Output Locations

Project-level (versioned with repo):

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

Global (user-level):

• ~/.claude/reflections/by-project/{project}/ - Cross-project
• ~/.claude/reflections/by-agent/{agent}/learnings.md - Per-agent
• ~/.claude/reflections/index.md - Global summary

Memory Integration

Some learnings belong in auto-memory (~/.claude/projects/*/memory/MEMORY.md) rather than agent files:

Learning Type	Best Target
Behavioral correction ("always do X")	Agent file
Project-specific pattern	MEMORY.md
Recurring bug/workaround	New skill OR MEMORY.md
Tool preference	CLAUDE.md
Domain knowledge	MEMORY.md or compound-docs

When a signal is LOW confidence and project-specific, prefer writing to MEMORY.md over modifying agents.

Safety Guardrails

Human-in-the-Loop

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

Git Versioning

• All changes committed with descriptive messages
• Easy rollback via git revert
• Learning history preserved

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

Integration

With /handover

If auto-reflection is enabled, PreCompact hook triggers reflection before handover.

With Session Health

At 70%+ context (Yellow status), reminders to run /reflect are injected.

Hook Integration (Claude Code)

The skill includes hook scripts for automatic integration:

bash

# Install hook to your Claude hooks directory
cp hooks/precompact_reflect.py ~/.claude/hooks/

Configure in ~/.claude/settings.json:

json

{
  "hooks": {
    "PreCompact": [
      {
        "hooks": [
          {
            "type": "command",
            "command": "uv run ~/.claude/hooks/precompact_reflect.py --auto"
          }
        ]
      }
    ]
  }
}

See hooks/README.md for full configuration options.

Portability

This skill works with any LLM tool that supports:

• File read/write operations
• Text pattern matching
• Git operations (optional, for commits)

Configurable State Location

bash

# Set custom state directory
export REFLECT_STATE_DIR=/path/to/state

# Or use default
# ~/.reflect/ (portable default)
# ~/.claude/session/ (Claude Code default)

No Task Tool Dependency

Unlike the previous agent-based approach, this skill executes directly without spawning subagents. The LLM reads SKILL.md and follows the workflow.

Git Operations Optional

Commits are wrapped with availability checks - if not in a git repo, changes are still saved but not committed.

Troubleshooting

No signals detected:

• Session may not have had corrections
• Try /reflect review to check pending items

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
• Use /reflect status to see available targets
• May need to create agent file first

File Structure

text

reflect/
├── SKILL.md                      # This file
├── scripts/
│   ├── state_manager.py          # State file CRUD
│   ├── signal_detector.py        # Pattern matching
│   ├── metrics_updater.py        # Metrics aggregation
│   └── output_generator.py       # Reflection file & index generation
├── hooks/
│   ├── precompact_reflect.py     # PreCompact hook integration
│   ├── settings-snippet.json     # Settings.json examples
│   └── README.md                 # Hook configuration guide
├── references/
│   ├── signal_patterns.md        # Detection rules
│   ├── agent_mappings.md         # Target mappings
│   └── skill_template.md         # Skill generation
└── assets/
    ├── reflection_template.md    # Output template
    └── learnings_schema.yaml     # Schema definition