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Reflect

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ScriptedAlchemy
reflect

Spawn three parallel review subagents over the active transcript, surface learnings, and route each to a concrete edit on an existing skill. Use when the user says reflect.

Overview

PublisherScriptedAlchemy
Repositorypstack-codex
Skill namereflect
Stars
77
Forks
7
Bundled files
5
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.

  • 5 bundled files

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

  • Open source

    Published by ScriptedAlchemy 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/ScriptedAlchemy/pstack-codex/tree/main/plugins/pstack/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/ScriptedAlchemy/pstack-codex.git /tmp/pstack-codex
mkdir -p .claude/skills
cp -r /tmp/pstack-codex/plugins/pstack/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.

Before following this workflow, read the Codex runtime contract. It defines plugin paths, model configuration, delegation, and persistence.

Reflect

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

Resolve the named role line in ~/.codex/pstack-models.md before each spawn, following CODEX.md: missing lines use the default, auto and inherit-parent omit both overrides, and unavailable choices are reported with inheritance as the explicit fallback. Split model @ effort into separate host fields.

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.

1. Locate the active transcript

Use the current transcript path or session ID exposed by the host when available. Otherwise inspect candidate ~/.codex/sessions/YYYY/MM/DD/*.jsonl files only in the relevant session date directories. Read each first session_meta record before any transcript body, require an exact payload.cwd workspace match, and match payload.id to the current session when known. Never sweep unrelated workspaces or use a transcript's body to infer workspace ownership.

If the active path cannot be established, pass a tight digest of this conversation instead. Older workspace-matched sessions are not substitutes for the active chat.

2. Spawn three reviewers in parallel

Three asynchronous spawn_agent calls in capacity-bounded waves using the default agent, the named role line and default on each, and normal write-capable mode. Reviewers need MCP access for context lookups referenced in the transcript. Subagents inherit the parent's available MCP tools. Pick reasoning effort for the job under the Subagents section of poteto-mode, using only the active execution tool’s supported values.

LensmodelPrompt template
Judgmentreflect judgment, divergent, synthesizer (default gpt-6-astra @ xhigh)references/judgment-reviewer.md
Toolingreflect tooling (default gpt-6.1-sol @ xhigh)references/tooling-reviewer.md
Divergentreflect judgment, divergent, synthesizer (default gpt-6-astra @ xhigh)references/divergent-reviewer.md

Pass each template verbatim, substituting the transcript path or digest where marked. Reviewers return findings in their final response.

3. Synthesize

One spawn_agent call using the default agent and reflect judgment, divergent, synthesizer line (default gpt-6-astra @ xhigh) in normal write-capable mode. The synthesizer spot-verifies citations and may need inherited MCP tools. Use references/synthesizer.md verbatim, with each reviewer's full output inlined where marked. The synthesizer returns a structured Accepted / Rejected / Backlog list.

4. Structural enforcement check

Move any Accepted item that a lint rule, script, metadata flag, or runtime check would enforce more reliably to Backlog. See the encode-lessons-in-structure principle skill.

5. Apply

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

File each Backlog item to your team's devex or backlog tracker without waiting. Only the Accepted list waits for approval.

Never edit a skill under plugins/cache/. Before approval, show its destination: .agents/skills/<skill>/ for the repository, or $CODEX_HOME/skills/<skill>/ (default ~/.codex/skills/<skill>/) for personal skills. After approval, copy the skill folder there and edit the copy. Never open a PR or push.

Follow each approved row's Routing 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): hand to Codex's skill-creator skill and run its draft / test / iterate loop.
  • tune description: <skill path> (the skill exists but did not trigger when it should have): hand to skill-creator and run its description-optimization loop.
  • new skill via skill-creator: <kebab-name>: hand creation to skill-creator. Do not invent the shape ad hoc.

If your environment ships a SKILL.md validator, run it on every touched skill before declaring done.

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?

Spawn three parallel review subagents over the active transcript, 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/ScriptedAlchemy/pstack-codex/tree/main/plugins/pstack/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 ScriptedAlchemy 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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