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.

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Integration with Squad Architecture

Reflect complements existing Squad knowledge systems:

1. Governed runtime memory — Durable observations and decisions persisted through configured state tools
2. Tracked governing source — Coordinator prompts, skills, and other behavior-defining files changed through focused pull requests
3. reflect skill — Identifies learnings that may warrant either runtime memory or an approved tracked behavior change

Workflow:

• Use reflect during work to capture learnings
• At session end, review captured learnings
• Persist evidence through governed runtime memory when available
• Promote accepted behavior changes through the accountable specialist as tracked source changes in a focused PR
• Report adjacent improvements separately; do not expand the current scope without explicit user approval

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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 (Chesterton's Fence)
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 knowledge system should be updated:

1. Observation or preference → Governed runtime memory, when configured
2. Accepted team-wide behavior change → The accountable tracked instruction or skill source, through a focused PR
3. Adjacent improvement → Report separately and require explicit user approval before adding it to scope

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.

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Phase 3: Propose Learnings

Present findings:

┌─────────────────────────────────────────────────────────────┐
│ REFLECTION: {target (agent/decision/skill)}                  │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│ [HIGH] + Add constraint: "{specific constraint}"            │
│   Source: "{quoted user correction}"                        │
│   Target: tracked governing source via focused PR            │
│                                                             │
│ [MED]  + Add preference: "{specific preference}"            │
│   Source: "{evidence from conversation}"                    │
│   Target: governed runtime memory                            │
│                                                             │
│ [LOW]  ~ Note for review: "{observation}"                   │
│   Source: "{pattern observed}"                              │
│   Target: report only; no persistence                        │
│                                                             │
├─────────────────────────────────────────────────────────────┤
│ 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 Runtime Memory:

   • Use the configured governed memory or state tool.
   • If no runtime persistence tool is available, report the learning without claiming it was saved.

2. For Behavioral Changes:

   • Route the accountable specialist to update the minimum tracked governing source.
   • Validate the change and open a focused PR.

3. Never Use Throwaway Proposal Files:

   • Do not create local or gitignored Markdown proposals, including .squad/decisions/inbox/*.md, for reflection or directive capture.
   • Runtime memory may preserve context, but it does not replace the tracked source change required to alter behavior.

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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"

Reflection Output:

[HIGH] + Add constraint: "Use code intelligence tools before grep"
  Source: "No, use the code search tools first, grep is too slow"
  Target: accountable tracked instruction via focused PR; governed runtime
          memory for context only

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: governed runtime memory; if adopted as team behavior, update the
          accountable tracked instruction in a focused PR

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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 history.md/decisions.md

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See Also

• .squad/decisions.md — Runtime-backed team decisions
• .github/agents/squad.agent.md — Coordinator behavior
• .squad/routing.md — Work assignment patterns