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Reflect

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bradygaster
reflect

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.

Overview

Publisherbradygaster
Repositorysquad
Skill namereflect
Stars
3.3K
Forks
506
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by bradygaster on GitHub. Read the source before you install it.

Installation

Install the Reflect AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/reflect
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/bradygaster/squad.git /tmp/squad
mkdir -p .claude/skills
cp -r /tmp/squad/packages/squad-cli/templates/skills/reflect .claude/skills/reflect
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Reflect in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Reflect on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Reflect is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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. 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

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. 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:

text
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:

text
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:

text
┌─────────────────────────────────────────────────────────────┐
│ 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:

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 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.

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

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

Frequently asked questions

What does the Reflect AI skill do?

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.

Why use Reflect on TypingMind?

Because you install it once and use it with any model. Reflect is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Reflect in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bradygaster/squad/tree/dev/packages/squad-cli/templates/skills/reflect. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Reflect?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Reflect?

As many as you like. As long as a model supports skills, you can use Reflect with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Reflect AI skill free?

Yes. It is published on GitHub by bradygaster under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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