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Reflect

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uzairansaruzi
reflect

Delegate three parallel reviewers over the active thread, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.

Overview

Publisheruzairansaruzi
Repositoryp3-stack
Skill namereflect
Stars
155
Forks
20
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by uzairansaruzi 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/uzairansaruzi/p3-stack/tree/main/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/uzairansaruzi/p3-stack.git /tmp/p3-stack
mkdir -p .claude/skills
cp -r /tmp/p3-stack/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

Mine the current conversation for durable learnings, then route them into skill edits.

When to invoke

Invoke when the user says "reflect" or "/reflect". Skip when the conversation is trivial, off-topic, or already covered by an existing skill the parent followed correctly. One-offs are not learnings.

Process

1. Locate the active transcript

The transcript is the current T3 thread. Read it with t3_thread_read; use t3_thread_search to pull exact passages. Do not search across other threads. That crosses workspace boundaries and reads private chats from unrelated projects.

Reviewers get only their brief, never the parent's context. Pass the thread id and tell them to read it with t3_thread_read, or write a tight digest of the session and pass that instead.

2. Delegate three reviewers in parallel

One turn, three delegate_task calls with mode: "async". Reviewers need tool access for context lookups (tickets, chat threads, observability traces referenced in the transcript), so do not restrict their tools.

Resolve each model from the role line in p3-models.md via orchestrator_capabilities. Never hardcode a slug.

LensRole linePrompt template
Judgmentreflect judgment, divergent, synthesizerreferences/judgment-reviewer.md
Toolingreflect toolingreferences/tooling-reviewer.md
Divergentreflect judgment, divergent, synthesizerreferences/divergent-reviewer.md

Pass each template verbatim as the brief, substituting the thread id or digest where marked. Drain with task_status; reviewers return findings in their task result.

3. Synthesize

One delegate_task call, model from the reflect judgment, divergent, synthesizer line. The synthesizer's quality check includes spot-verifying citations, which can require tool access. Use references/synthesizer.md verbatim as the brief, with each reviewer's full output inlined where marked. The synthesizer returns a structured Accepted / Rejected / Backlog list.

4. Structural enforcement check

Sanity-check the synthesizer's Accepted list. For any item that would be enforced more reliably by a lint rule, script, metadata flag, or runtime check, move it from Accepted to Backlog. See the encode-lessons-in-structure principle skill.

5. Apply

Before applying any Accepted edit, present the synthesizer's full Accepted/Rejected/Backlog output to the user and wait for explicit approval. The user picks which subset to apply and may redirect routings. Skill changes affect every future agent in the org. Do not auto-apply.

Backlog items file to whatever devex / backlog tracker your team uses automatically. Only the Accepted list waits for approval.

For each approved Accepted item, follow the Routing field exactly:

  • Trivial existing-skill edit (a one-line bullet, a tightened sentence, a stale fact corrected): parent does directly.
  • Substantive existing-skill edit (a new section, a new pattern table, more than ~10 lines): follow the authoring-a-skill playbook and run its draft / test / iterate loop.
  • tune description: <skill path> (the skill exists but didn't trigger when it should have): follow the authoring-a-skill playbook and run its description-optimization loop.
  • new skill via authoring-a-skill: <kebab-name>: hand creation to the authoring-a-skill playbook. Do not invent the shape ad hoc.

If your environment ships a SKILL.md validator, run it on every touched skill before declaring done. Skip this step if it doesn't.

6. Summarize for the user

Short list, no preamble:

  • Edits applied: <skill path>. What changed, one line each.
  • New skills created: <skill path>. One line each (rare).
  • Backlog filed to the devex tracker: <issue title> (<tags>). One line each.
  • Dropped: one line per rejected finding + reason from the synthesizer.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Reflect AI skill do?

Delegate three parallel reviewers over the active thread, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.

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/uzairansaruzi/p3-stack/tree/main/skills/reflect. TypingMind reads its SKILL.md and bundles its files 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 uzairansaruzi 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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