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Agent skill

Autoresearch Mlx

by autonomous-ai in autonomous-ai/openharness

Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration.

MITAuto-check passedAgent Workflows

Install Autoresearch Mlx

skills CLI
$ npx skills add autonomous-ai/openharness --skill autoresearch-mlx -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install autonomous-ai/openharness autoresearch-mlx --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/autonomous-ai/openharness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/store/agents/autoresearch-mlx/skills/autoresearch-mlx .claude/skills/autoresearch-mlx && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
autoresearch-mlx
GitHub stars
1.2k
Token cost
~526 tokens
SKILL.md length
254 words
Files
1
Skills in repo
100
Repo updated
First seen
Licence
MIT

At a glance

Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration.

  • Tasks that involve Autonomous loops
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Autoresearch Mlx is an agent skill from autonomous-ai/openharness. Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration.

Its SKILL.md is about 530 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Autonomous loops. The repository describes itself as: The ultimate harness for coding agents and beyond. All your agents. All your machines. One command center. Start with code, then follow your curiosity and build across… The licence is MIT.

When your agent uses it

  • Tasks that involve Autonomous loops

Example prompts

  • “/autoresearch-mlx”

What it can do on your machine

Read from SKILL.md and the folder at commit cc4983e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Autoresearch Mlx loads about 526 tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 254 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~526

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from autonomous-ai/openharness at commit cc4983e, republished under its MIT licence (© autonomous-ai). 254 words, ~526 tokens.

Download SKILL.mdSave it as .claude/skills/autoresearch-mlx/SKILL.md (or your agent's skills folder).
name
autoresearch-mlx
description
Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration.

Research notebook

Read studio.json to understand the current controls; "$STUDIO_TOOLCHAIN/../studio.config.json" describes their ranges. Run "$STUDIO_TOOLCHAIN/run.sh" train to make a new result. Successful artifacts and their measurements are in out/runs/<id>/; out/latest.json names the current result. A failed run preserves the last success and records the error in the verdict.

The local starter trains a small character transition model with NumPy on a bundled, original text corpus. It uses real training and held-out cross-entropy, not generated metrics. It is a CPU baseline, distinct from upstream MLX transformer training, which requires Apple Silicon and its prepared dataset.

Use "$STUDIO_TOOLCHAIN/../README.md" for the integration contract and commands. Read the relevant files under $STUDIO_UPSTREAM before using an upstream API. Keep controls within their documented ranges, preserve the data needed to reproduce a comparison, and distinguish preview results from native service or hardware output. The viewer supports history and artifact downloads; tell the user which run contains the result, and what was actually measured.

Run a controlled experiment

Read train.py, train.txt, and holdout.txt. Save a baseline. Change one training choice or the editable training code, then run train. Inspect evaluation.json, the saved model, and learning.csv. Keep the holdout unchanged; the runner independently reopens the model with pickle disabled and computes its score. Data hashes define which earlier runs compare. Repeat promising changes with several seeds before presenting a conclusion.

For Apple Silicon, read $STUDIO_UPSTREAM/program.md and README.md. Prepare a separate workspace mlx/ checkout and its environment/data before running mlx. The CPU starter's bits-per-character score and the upstream bits-per-byte metric are different experiments.

© autonomous-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in store/agents/autoresearch-mlx/skills/autoresearch-mlx of autonomous-ai/openharness.

Open the folder on GitHubat commit cc4983e

Compare with similar skills

Autoresearch Mlx next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Autoresearch Mlx compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Autoresearch Mlx this skillautonomous-ai/openharness1.2k—~526Automated safety check: PassMIT
Show Me Your Work Decision Logcursor/plugins11k8 repos~1.6kAutomated safety check: PassNone
Autoresearch Iteration Loopuditgoenka/autoresearch6.5k1 repos~2kAutomated safety check: PassMIT
Install Loop Engineeringcobusgreyling/loop-engineering11k1 repos~648Automated safety check: PassMIT
LoopyForward-Future/loopy3.2k—~3.9kAutomated safety check: PassMIT
AI Performance Improvement Plantanweai/pua20k2 repos~6.9kAutomated safety check: PassMIT

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Categories

Questions about Autoresearch Mlx

What does Autoresearch Mlx do?

Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration. Autoresearch Mlx is an agent skill from autonomous-ai/openharness. Create and inspect science projects in the autoresearch-mlx Harness workspace, including its local starter and optional upstream integration.

When should I use Autoresearch Mlx?

Autoresearch Mlx fits situations like: tasks that involve Autonomous loops.

How do I install Autoresearch Mlx in Claude Code?

Run `npx skills add autonomous-ai/openharness --skill autoresearch-mlx -a claude-code`. Or copy the skill folder (store/agents/autoresearch-mlx/skills/autoresearch-mlx in autonomous-ai/openharness) into .claude/skills/autoresearch-mlx in your project. Claude Code loads it when a task matches its description.

How do I install Autoresearch Mlx in Codex?

Run `npx skills add autonomous-ai/openharness --skill autoresearch-mlx -a codex`. Or copy the skill folder (store/agents/autoresearch-mlx/skills/autoresearch-mlx in autonomous-ai/openharness) into .agents/skills/autoresearch-mlx in your project. Codex loads it when a task matches its description.

Can I use Autoresearch Mlx in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add autonomous-ai/openharness --skill autoresearch-mlx -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/autoresearch-mlx, .gemini/skills/autoresearch-mlx, .github/skills/autoresearch-mlx and .opencode/skills/autoresearch-mlx in your project.

What does Autoresearch Mlx need to run?

SKILL.md names no scripts, command-line tools or credentials: Autoresearch Mlx is instructions for the agent only.

Does Autoresearch Mlx access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Autoresearch Mlx safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Autoresearch Mlx use?

Autoresearch Mlx is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Autoresearch Mlx use?

About 526 tokens (SKILL.md is roughly 2.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Autoresearch Mlx?

Skills that share tags, products or a category with Autoresearch Mlx: Show Me Your Work Decision Log (cursor/plugins, 11k stars), Autoresearch Iteration Loop (uditgoenka/autoresearch, 6.5k stars), Install Loop Engineering (cobusgreyling/loop-engineering, 11k stars) and Loopy (Forward-Future/loopy, 3.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Autoresearch Mlx?

autonomous-ai (a GitHub organization) maintains it in autonomous-ai/openharness, which has 1,210 GitHub stars. The repository holds 100 skills in this directory. The repository was last updated on October 10, 2026.

Source: autonomous-ai/openharness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.