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Codex Autoresearch

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leo-lilinxiao
codex-autoresearch

Run repeated, measured Git experiments toward a numeric target; keep improvements and revert failures. Use for autonomous optimization or managing an autoresearch run, not one-shot edits.

Overview

Publisherleo-lilinxiao
Repositorycodex-autoresearch
Skill namecodex-autoresearch
Stars
3.1K
Forks
129
Bundled files
32
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.

  • 32 bundled files

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

  • Open source

    Published by leo-lilinxiao on GitHub. Read the source before you install it.

Installation

Install the Codex Autoresearch 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/leo-lilinxiao/codex-autoresearch/tree/main
  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/leo-lilinxiao/codex-autoresearch.git \
  .claude/skills/codex-autoresearch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Codex Autoresearch 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 Codex Autoresearch 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 Codex Autoresearch 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.

Codex Autoresearch

Improve a repository through repeated, reversible experiments:

hypothesize -> change -> measure -> learn -> keep or revert -> repeat

Codex chooses hypotheses and makes code changes. The control script owns measurement, commits, rollback, and the event history.

Respond To The Request

Resolve <control> to this skill's own scripts/autoresearch.py; do not assume it is installed in the target repository.

For a status or results request, run the corresponding command directly:

RequestCommand
Statuspython3 <control> status --repo <repo>
Historypython3 <control> history --repo <repo>
TSV exportpython3 <control> history --repo <repo> --format tsv
HTML reportpython3 <control> report --repo <repo>

Report the requested result without starting or resuming experiments. Return the generated path for an HTML report.

Before starting or resuming an experiment, read the applicable workflow unless it is already in context:

  • Workflow: starting, continuing, resuming, or changing an experiment.
  • Background: launching or controlling a detached run.

Experiment Boundary

  • One run owns one Git repository, one numeric metric, one confirmed target, and approved path scopes.
  • For a new foreground run, initialize successfully before creating its Goal; the Goal identifies the returned run id. If initialization reports complete, no Goal is needed.
  • Use finish to finalize each coherent experiment. Do not manually commit, revert, or edit run artifacts during an active run.
  • Only a verified target can mean complete; an iteration limit, error, or external blocker has its own status.
  • Validate state through the control script on entry or resume. Use recorded experiments rather than guessing from conversation.
  • Surface command and state errors with their diagnostic paths. Never bypass validation, fabricate metrics, or reconstruct missing events.

Foreground continuation belongs to the official Codex Goal. Background continuation belongs to the detached controller. An already launched background worker follows its supplied experiment contract, without starting a new launch flow.

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 Codex Autoresearch AI skill do?

Run repeated, measured Git experiments toward a numeric target; keep improvements and revert failures. Use for autonomous optimization or managing an autoresearch run, not one-shot edits.

Why use Codex Autoresearch on TypingMind?

Because you install it once and use it with any model. Codex Autoresearch 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 Codex Autoresearch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/leo-lilinxiao/codex-autoresearch/tree/main. 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 Codex Autoresearch?

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 Codex Autoresearch?

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

Is the Codex Autoresearch AI skill free?

Yes. It is published on GitHub by leo-lilinxiao 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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