Agent skill

Autoresearch Create

by gianfrancopiana in gianfrancopiana/openclaw-autoresearch

Set up and run an autonomous experiment loop for any optimization target.

MITAuto-check: warningsAgent Workflows

Install Autoresearch Create

The automated check flagged lines worth reading first. See the safety section below.

skills CLI
$ npx skills add gianfrancopiana/openclaw-autoresearch --skill autoresearch-create -a claude-code

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

GitHub CLI
$ gh skill install gianfrancopiana/openclaw-autoresearch autoresearch-create --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/gianfrancopiana/openclaw-autoresearch.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/autoresearch-create .claude/skills/autoresearch-create && 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-create
GitHub stars
176
Token cost
~1.5k tokens
SKILL.md length
717 words
Files
1
Skills in repo
1
Repo updated
First seen
Licence
MIT

At a glance

Set up and run an autonomous experiment loop for any optimization target.

  • Works in 5 steps: Ask (or infer): Goal, Command, Metric (+… → If autoresearch.checkpoint.json already… → Read the source files. Understand the… → …
  • Asked to run autoresearch
  • SKILL.md covers Tools, Setup, Loop Rules and Ideas Backlog, plus 1 more section
  • Calls git

What it does

Autoresearch Create is an agent skill from gianfrancopiana/openclaw-autoresearch. Set up and run an autonomous experiment loop for any optimization target. Gathers what to optimize, then starts the loop immediately. Use when asked to "run autoresearch", "optimize X in a loop", "set up autoresearch for X", or "start experiments".

Its SKILL.md is about 1.5k 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: Autonomous experiment loop plugin for OpenClaw. The licence is MIT.

When your agent uses it

  • Asked to run autoresearch
  • Optimize X in a loop
  • Set up autoresearch for X
  • Start experiments

Example prompts

  • “run autoresearch”
  • “optimize X in a loop”
  • “set up autoresearch for X”
  • “/autoresearch-create”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Ask (or infer): Goal, Command, Metric (+ direction), Files in scope, Constraints.
  2. If autoresearch.checkpoint.json already names a canonical branch, reuse it. Otherwise create one with git checkout -b autoresearch/- and…
  3. Read the source files. Understand the workload deeply before writing anything.
  4. Write autoresearch.md and autoresearch.sh (see below). Commit both.
  5. init_experiment → run_experiment baseline → log_experiment → start looping immediately.

What it can do on your machine

Read from SKILL.md and the folder at commit 08bfcaf. 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

    Shell commands in SKILL.md call:

    • git

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

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    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 Create loads about 1.5k tokens when it runs. Until then it costs about 67 tokens; SKILL.md has 717 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~67
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

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: warnings

The automated check found patterns that need a careful read before installing.

  • WarningTells the agent its actions are pre-authorized / not to stop for confirmationSKILL.md:95
    the user's idea in the next experiment. Don't stop mid-experiment or ask for confirmation unless the user explicitly int

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 gianfrancopiana/openclaw-autoresearch at commit 08bfcaf, republished under its MIT licence (© gianfrancopiana). 717 words, ~1,487 tokens.

Download SKILL.mdSave it as .claude/skills/autoresearch-create/SKILL.md (or your agent's skills folder).
name
autoresearch-create
description
Set up and run an autonomous experiment loop for any optimization target. Gathers what to optimize, then starts the loop immediately. Use when asked to "run autoresearch", "optimize X in a loop", "set up autoresearch for X", or "start experiments".

Autoresearch

Autonomous experiment loop: try ideas, keep what works, discard what doesn't, never stop.

Tools

  • init_experiment — configure session (name, metric, unit, direction). Once runs exist, calling it again requires reset: true to start a new segment explicitly.
  • run_experiment — runs the benchmark command, times it, captures output, parses METRIC name=number lines, and opens a pending run that must be logged before another run can start.
  • log_experiment — records the pending run. The first logged run in a segment becomes the baseline automatically. keep auto-commits. discard/crash → git checkout -- . to revert. discard also requires an idea note so the failed path gets appended to autoresearch.ideas.md. If the previous run_experiment captured the primary metric, commit and metric can be omitted and will default from the pending run.

Setup

  1. Ask (or infer): Goal, Command, Metric (+ direction), Files in scope, Constraints.
  2. If autoresearch.checkpoint.json already names a canonical branch, reuse it. Otherwise create one with git checkout -b autoresearch/<goal>-<date> and keep using that branch for the whole loop.
  3. Read the source files. Understand the workload deeply before writing anything.
  4. Write autoresearch.md and autoresearch.sh (see below). Commit both.
  5. init_experiment → run_experiment baseline → log_experiment → start looping immediately.
autoresearch.md

This is the heart of the session. A fresh agent with no context should be able to read this file and run the loop effectively. Invest time making it excellent.

markdown
# Autoresearch: <goal>

## Objective
<Specific description of what we're optimizing and the workload.>

## Metrics
- **Primary**: <name> (<unit>, lower/higher is better)
- **Secondary**: <name>, <name>, ...

## How to Run
`./autoresearch.sh` — outputs `METRIC name=number` lines.

## Files in Scope
<Every file the agent may modify, with a brief note on what it does.>

## Off Limits
<What must NOT be touched.>

## Constraints
<Hard rules: tests must pass, no new deps, etc.>

## What's Been Tried
<Update this section as experiments accumulate. Note key wins, dead ends,
and architectural insights so the agent doesn't repeat failed approaches.>

The plugin rewrites the Metrics, How to Run, What's Been Tried, and Plugin Checkpoint sections after init/log transitions. You may add context elsewhere in the file, but do not fight the plugin-managed sections.

autoresearch.sh

Bash script (set -euo pipefail) that: pre-checks fast (syntax errors in <1s), runs the benchmark, outputs METRIC name=number lines. Keep it fast — every second is multiplied by hundreds of runs. Update it during the loop as needed.

Loop Rules

LOOP FOREVER. Never ask "should I continue?" — the user expects autonomous work.

  • Primary metric is king. Improved → keep. Worse/equal → discard. Secondary metrics rarely affect this.
  • Simpler is better. Removing code for equal perf = keep. Ugly complexity for tiny gain = probably discard.
  • Don't thrash. Repeatedly reverting the same idea? Try something structurally different.
  • Crashes: fix if trivial, otherwise log and move on. Don't over-invest.
  • Think longer when stuck. Re-read source files, study the profiling data, reason about what the CPU is actually doing. The best ideas come from deep understanding, not from trying random variations.
  • Resuming: if autoresearch.md exists, read it plus autoresearch.checkpoint.json, then continue looping.
  • Respect the canonical branch. If the checkpoint names a canonical branch, switch back to it before resuming. Do not create a fresh autoresearch branch for every session.
  • Respect the lock file. If autoresearch.lock exists and the PID is alive, another loop is active. Resume that loop instead of forking a second session.
  • No raw benchmark exec: during active autoresearch mode, benchmark/test commands should go through run_experiment, not raw exec/bash.
  • Reset explicitly. If you need a new segment, call init_experiment with reset: true; do not silently wipe the current baseline.

NEVER STOP. The user may be away for hours. Keep going until interrupted.

Show full SKILL.md (215 more words)Show less

Ideas Backlog

When you discover complex but promising optimizations that you decide not to pursue right now, append them as bullet points to autoresearch.ideas.md. Don't let good ideas get lost.

If the loop stops (context limit, crash, etc.) and autoresearch.ideas.md exists, you'll be asked to:

  1. Read the ideas file and use it as inspiration for new experiment paths
  2. Prune ideas that are duplicated, already tried, or clearly bad
  3. Create experiments based on the remaining ideas
  4. If nothing is left, try to come up with your own new ideas
  5. If all paths are exhausted, delete autoresearch.ideas.md and write a final summary report

When there is no autoresearch.ideas.md file and the loop ends, the research is complete.

User Steers

If the host exposes the OpenClaw message hooks, user steers that arrive while an experiment is running are captured and surfaced after your next log_experiment call. OpenClaw may also preserve the same steer in the normal followup backlog, so if the next turn repeats a steer you already saw in log_experiment, treat it as the same request rather than a brand new branch of work.

Finish the current experiment first, then incorporate the user's idea in the next experiment. Don't stop mid-experiment or ask for confirmation unless the user explicitly interrupts the loop.

© gianfrancopiana, 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 skills/autoresearch-create of gianfrancopiana/openclaw-autoresearch.

Open the folder on GitHubat commit 08bfcaf

Compare with similar skills

Autoresearch Create 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 Create compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Autoresearch Create this skillgianfrancopiana/openclaw-autoresearch176—~1.5kAutomated safety check: WarnMIT
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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    uditgoenka/autoresearch

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Categories

Questions about Autoresearch Create

What does Autoresearch Create do?

Set up and run an autonomous experiment loop for any optimization target. Autoresearch Create is an agent skill from gianfrancopiana/openclaw-autoresearch. Set up and run an autonomous experiment loop for any optimization target.

When should I use Autoresearch Create?

Autoresearch Create fits situations like: asked to run autoresearch; optimize X in a loop; set up autoresearch for X; start experiments.

How do I install Autoresearch Create in Claude Code?

Run `npx skills add gianfrancopiana/openclaw-autoresearch --skill autoresearch-create -a claude-code`. Or copy the skill folder (skills/autoresearch-create in gianfrancopiana/openclaw-autoresearch) into .claude/skills/autoresearch-create in your project. Claude Code loads it when a task matches its description.

How do I install Autoresearch Create in Codex?

Run `npx skills add gianfrancopiana/openclaw-autoresearch --skill autoresearch-create -a codex`. Or copy the skill folder (skills/autoresearch-create in gianfrancopiana/openclaw-autoresearch) into .agents/skills/autoresearch-create in your project. Codex loads it when a task matches its description.

Can I use Autoresearch Create 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 gianfrancopiana/openclaw-autoresearch --skill autoresearch-create -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-create, .gemini/skills/autoresearch-create, .github/skills/autoresearch-create and .opencode/skills/autoresearch-create in your project.

What does Autoresearch Create need to run?

Going by SKILL.md and its folder, Autoresearch Create needs the command-line tools its instructions call (git).

Does Autoresearch Create access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Autoresearch Create safe to install?

Our automated static check of SKILL.md flagged 1 warning(s): tells the agent its actions are pre-authorized / not to stop for confirmation. Read the flagged lines before installing; the check is not a guarantee either way.

What licence does Autoresearch Create use?

Autoresearch Create 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 Create use?

About 1.5k tokens (SKILL.md is roughly 5.9k 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 Create?

Skills that share tags, products or a category with Autoresearch Create: 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 Create?

gianfrancopiana (a GitHub user) maintains it in gianfrancopiana/openclaw-autoresearch, which has 176 GitHub stars. The repository was last updated on May 4, 2026.

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