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reflect

Extract lessons from corrections, praise, and unexpected cases in conversations. Organize findings by confidence level and propose updates to individual agent histories or shared team decisions, with changes shown for approval before saving.

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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. Complements .squad/agents/{agent}/history.md and .squad/decisions.md.

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适用场景

  • Capture lessons after correcting an agent
  • Record successful output formats
  • Consolidate lessons at the end of a session
  • Propose shared rules for an agent team

包含的文件(1)

  • SKILL.md8.0 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Reflect Skill

Critical learning capture system for Squad. Prevents repeating mistakes and preserves successful patterns across sessions.

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


Integration with Squad Architecture

Reflect complements existing Squad knowledge systems:

  1. .squad/agents/{agent}/history.md — Permanent learnings from completed work (append-only; each agent updates their own file; Scribe propagates cross-agent updates)
  2. .squad/decisions.md — Team-wide decisions that all agents respect
  3. reflect skill — Captures in-flight learnings from conversations that may graduate to history.md or decisions.md

Workflow:

  • Use reflect during work to capture learnings
  • At session end, review captured learnings
  • Promote HIGH confidence patterns → lead agent for decision.md review
  • Promote agent-specific patterns → {agent}/history.md updates

Triggers

🔴 HIGH Priority (Invoke Immediately)

TriggerExampleWhy 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 (Chesterton's Fence)
Immediate fixes"debug", "root cause", "fix all"Learns from errors in real-time

🟡 MEDIUM Priority (Invoke After Multiple)

TriggerExampleWhy 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)

TriggerExampleWhy Useful
Repeated patternsFrequent use of specific commands/toolsIdentifies workflow preferences
Session endAfter complex workConsolidates all session learnings

Process

Phase 1: Identify Learning Target

Determine what knowledge system should be updated:

  1. Agent-specific learning → .squad/agents/{agent}/history.md
  2. Team-wide decision → .squad/decisions/inbox/{agent}-{topic}.md
  3. Skill-specific improvement → Document in session, recommend to 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 azure-devops MCP tool instead of raw API calls"
→ [HIGH] + Add constraint: "Prefer azure-devops MCP tools over REST 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

LOW Confidence: Preferences

Accumulated patterns over time.


Phase 3: Propose Learnings

Present findings:

┌─────────────────────────────────────────────────────────────┐
│ REFLECTION: {target (agent/decision/skill)}                  │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│ [HIGH] + Add constraint: "{specific constraint}"            │
│   Source: "{quoted user correction}"                        │
│   Target: .squad/decisions/inbox/{agent}-{topic}.md         │
│                                                             │
│ [MED]  + Add preference: "{specific preference}"            │
│   Source: "{evidence from conversation}"                    │
│   Target: .squad/agents/{agent}/history.md                  │
│                                                             │
│ [LOW]  ~ Note for review: "{observation}"                   │
│   Source: "{pattern observed}"                              │
│   Target: Session notes only                                │
│                                                             │
├─────────────────────────────────────────────────────────────┤
│ Apply changes? [Y/n/edit]                                   │
└─────────────────────────────────────────────────────────────┘

Confidence Threshold:

ThresholdAction
≥1 HIGH signalAlways propose (user explicitly corrected)
≥2 MED signalsPropose (sufficient pattern)
≥3 LOW signalsPropose (accumulated evidence)
1-2 LOW onlySkip (insufficient evidence)

Phase 4: Persist Learnings

ALWAYS show changes before applying.

After user approval:

  1. For Agent History:

    • Append to .squad/agents/{agent}/history.md under ## Learnings section
    • Format: Date, assignment context, key learning
  2. For Team Decisions:

    • Create .squad/decisions/inbox/{agent}-{topic}.md
    • Lead agent reviews and merges to decisions.md if appropriate
  3. For Skills:

    • Document recommendation in session notes
    • Squad lead reviews and routes to skill owner

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"

Reflection Output:

[HIGH] + Add constraint: "Use code intelligence tools before grep"
  Source: "No, use the code search tools first, grep is too slow"
  Target: .squad/agents/{agent}/history.md

Example 2: Success Pattern

Conversation:

Agent: [Creates PR with detailed description and test plan]
User: "Perfect! This is exactly the format I want for all PRs"

Reflection Output:

[MED] + Add preference: "Include test plan in PR descriptions"
  Source: User praised detailed PR format
  Target: .squad/decisions/inbox/pr-format.md (for team adoption)

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 history.md/decisions.md

See Also

  • .squad/decisions.md — Team-wide decisions
  • .squad/agents/*/history.md — Agent-specific learnings
  • .squad/routing.md — Work assignment patterns

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