reflect

name	reflect
description	Learning capture system that extracts HIGH/MED/LOW confidence patterns from conversations to prevent repeating mistakes. Use after user corrections ("no", "wrong"), praise ("perfect", "exactly"), or when discovering edge cases.
license	MIT
metadata	{"version":"1.0.0","adapted_from":"Continuous improvement pattern library"}

Reflect Skill

Critical learning capture system for agent continuous improvement. Prevents repeating mistakes and preserves successful patterns across sessions.

Analyze conversations and propose improvements to agent knowledge based on what worked, what didn't, and edge cases discovered. Every correction is a learning opportunity.

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Integration with Learning Systems

Reflect complements existing learning mechanisms:

1. Agent history — Permanent learnings from completed work
2. Team decisions — Shared guidelines all agents respect
3. Skill improvements — Recommendations to skill documentation
4. Reflect skill — Captures in-flight learnings that may graduate to permanent systems

Workflow:

• Use reflect during work to capture learnings
• At session end, review captured learnings
• Promote HIGH confidence patterns for team adoption
• Promote agent-specific patterns to agent history

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Triggers

🔴 HIGH Priority (Invoke Immediately)

Trigger	Example	Why Critical
User correction	"no", "wrong", "not like that", "never do"	Captures mistakes to prevent repetition
Architectural insight	"you removed that without understanding why"	Documents design decisions
Immediate fixes	"debug", "root cause", "fix all"	Learns from errors in real-time

🟡 MEDIUM Priority (Invoke After Multiple)

Trigger	Example	Why Important
User praise	"perfect", "exactly", "great"	Reinforces successful patterns
Tool preferences	"use X instead of Y", "prefer"	Builds workflow preferences
Edge cases	"what if X happens?", "don't forget", "ensure"	Captures scenarios to handle

🟢 LOW Priority (Invoke at Session End)

Trigger	Example	Why Useful
Repeated patterns	Frequent use of specific commands/tools	Identifies workflow preferences
Session end	After complex work	Consolidates all session learnings

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Process

Phase 1: Identify Learning Target

Determine what system should be updated:

1. Agent-specific learning — Agent's personal history and preferences
2. Team-wide decision — Guidelines for all agents
3. Skill-specific improvement — Document recommendation for skill owner

Phase 2: Analyze Conversation

Scan for learning signals with confidence levels:

HIGH Confidence: Corrections

User actively steered or corrected output.

Detection patterns:

• Explicit rejection: "no", "not like that", "that's wrong"
• Strong directives: "never do", "always do", "don't ever"
• User provided alternative implementation

Example:

User: "No, use the code search tool instead of raw API calls"
→ [HIGH] + Add constraint: "Prefer code search tools over direct API"

MEDIUM Confidence: Success Patterns

Output was accepted or praised.

Detection patterns:

• Explicit praise: "perfect", "great", "yes", "exactly"
• User built on output without modification
• Output was committed without changes

Example:

User: "Perfect, that's exactly what I needed"
→ [MED] + Add preference: "Include usage examples in documentation"

MEDIUM Confidence: Edge Cases

Scenarios not anticipated.

Detection patterns:

• Questions not answered
• Workarounds user had to apply
• Error handling gaps discovered

Example:

User: "What if the file doesn't exist?"
→ [MED] ~ Add edge case: "Handle missing file scenario"

LOW Confidence: Preferences

Accumulated patterns over time.

Example:

User consistently uses --no-pager flag
→ [LOW] ~ Note for review: "User prefers --no-pager for commands"

Phase 3: Propose Learnings

Present findings:

┌─────────────────────────────────────────────────────────────┐
│ REFLECTION: {target}                                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│ [HIGH] + Add constraint: "{specific constraint}"            │
│   Source: "{quoted user correction}"                        │
│   Target: Agent knowledge base                              │
│                                                             │
│ [MED]  + Add preference: "{specific preference}"            │
│   Source: "{evidence from conversation}"                    │
│   Target: Shared guidelines                                 │
│                                                             │
│ [LOW]  ~ Note for review: "{observation}"                   │
│   Source: "{pattern observed}"                              │
│   Target: Session notes only                                │
│                                                             │
├─────────────────────────────────────────────────────────────┤
│ Apply changes? [Y/n/edit]                                   │
└─────────────────────────────────────────────────────────────┘

Confidence Threshold:

Threshold	Action
≥1 HIGH signal	Always propose (user explicitly corrected)
≥2 MED signals	Propose (sufficient pattern)
≥3 LOW signals	Propose (accumulated evidence)
1-2 LOW only	Skip (insufficient evidence)

Phase 4: Persist Learnings

ALWAYS show changes before applying.

After user approval:

1. For Agent History:

   • Append to agent history under ## Learnings section
   • Format: Date, assignment context, key learning
   • Agent self-updates at end of session

2. For Team Decisions:

   • Create proposal in decisions inbox
   • Team lead reviews and merges if appropriate
   • Format follows decision template

3. For Skills:

   • Document recommendation in session notes
   • Skill owner reviews and updates skill documentation

Memory Format Example:

### {Date}: {Assignment Title}

**Context:** {Brief description}

**Learnings:**
1. [HIGH] {Constraint learned} — Source: "{user quote}"
2. [MED] {Preference discovered} — Pattern observed across {N} instances
3. [MED] {Edge case} — Scenario: {description}

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Usage Examples

Example 1: User Correction

Conversation:

Agent: "I'll use grep to search the repository"
User: "No, use the code search tools first, grep is too slow"
Agent: "Got it. Let me reflect on this correction..."

Reflection Output:

[HIGH] + Add constraint: "Use code search tools before grep"
  Source: "No, use the code search tools first, grep is too slow"
  Target: Agent knowledge base

Example 2: Success Pattern

Conversation:

Agent: [Creates detailed documentation with examples]
User: "Perfect! This is exactly the format I want for all reports"
Agent: "Let me capture this success pattern..."

Reflection Output:

[MED] + Add preference: "Include examples in documentation"
  Source: User praised detailed format
  Target: Team guidelines

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Integration Notes

Reflect provides a learning workflow for any agent:

• No framework dependency
• Works with any knowledge storage system (Git, databases, files)
• Captures HIGH/MED/LOW confidence learnings
• Routes team-wide learnings to appropriate decision-makers
• Agent-specific learnings captured in agent history

Core principle: Every user correction is a learning opportunity.

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When to Use

✅ Use reflect when:

• User says "no", "wrong", "not like that" (HIGH priority)
• User says "perfect", "exactly", "great" (MED priority)
• You discover edge cases or gaps
• Complex work session with multiple learnings
• At end of sprint/milestone to consolidate patterns

❌ Don't use reflect when:

• Simple one-off questions with no pattern
• User is just exploring ideas (no concrete decisions)
• Learning is already captured in agent history
• Trivial preferences unlikely to recur

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See Also

• News Broadcasting — Share team updates
• Fact Checking — Verify accuracy
• Distributed Coordination — Coordinate across agents