autoresearch

autoresearch is a skill for Claude Code from proyecto26/autoresearch-ai-plugin. It costs 209 tokens per session (5,490 once invoked), scanned A, original, MIT.

An autonomous experiment loop that edits code, runs a benchmark, measures a chosen result, and keeps or reverts each change.

In plain words
What is it for?
Use it for measurable optimization jobs such as reducing test time, build time, bundle size, or training loss. A benchmark is the repeatable command used to measure the target.
Why use it?
It lets you compare many improvements automatically while preserving the best measured result and recording the experiment history.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter.

Part of the autoresearch-ai-plugin plugin — 2 skills, 1 command, 1 agent, 1 hook shipped together

Good fit Use it for measurable optimization jobs such as reducing test time, build time, bundle size, or training loss. A benchmark is the repeatable command used to measure the target.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/proyecto26/autoresearch-ai-plugin/autoresearch
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add proyecto26/autoresearch-ai-plugin --skill autoresearch
Clone the repo
git clone --depth 1 https://github.com/proyecto26/autoresearch-ai-plugin

Made for: Claude Code.

Or install autoresearch-ai-plugin, the plugin that ships this one along with the rest of its 2 skills, 1 command, 1 agent, 1 hook.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for autoresearch

README.md
[![agentmods](https://agentmods.dev/badge/skills/proyecto26/autoresearch-ai-plugin/autoresearch/github.svg)](https://agentmods.dev/skills/proyecto26/autoresearch-ai-plugin/autoresearch)
Your own site
<a href="https://agentmods.dev/skills/proyecto26/autoresearch-ai-plugin/autoresearch"><img src="https://agentmods.dev/badge/skills/proyecto26/autoresearch-ai-plugin/autoresearch/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for autoresearch

Your own site · 80×15
<a href="https://agentmods.dev/skills/proyecto26/autoresearch-ai-plugin/autoresearch"><img src="https://agentmods.dev/badge/skills/proyecto26/autoresearch-ai-plugin/autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 209 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,490 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00209 $0.05490
Opus 5 $0.00105 $0.02745
Sonnet 5 $0.00042 $0.01098
Haiku 4.5 $0.00021 $0.00549

Measured 10d ago against content hash 4e2b77718a92, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

autoresearch scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 5 executable files (examples/autoresearch.checks.sh, examples/autoresearch.sh, scripts/log-experiment.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/autoresearch/SKILL.md · 382 lines

How it starts

The opening of the file, as written. The whole thing — 382 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Autoresearch: Autonomous Experiment Loop

An autonomous optimization loop where Claude edits code, runs a benchmark, measures a metric, and keeps improvements or reverts — repeating forever until stopped.

Core Concept

The loop is simple: edit → commit → run → measure → keep or discard → repeat.

  • Primary metric is king. Lower (or higher, depending on direction) is better. Improved → keep the commit. Equal or worse → git revert.
  • State survives context resets via autoresearch.jsonl (append-only log) and autoresearch.md (living session document).
  • Domain-agnostic. Works for any measurable target: test speed, bundle size, LLM training loss, Lighthouse scores, build times, etc.
  • Be careful not to overfit to the benchmarks and do not cheat on the benchmarks. Optimize the real workload, not the measurement harness.

Setup Phase

When the user triggers autoresearch, gather the following (ask if not provided). If $ARGUMENTS is provided, use it as the optimization goal:

  1. Goal — what to optimize (e.g., "reduce unit test runtime") — use $ARGUMENTS if provided
  2. Command — the benchmark to run (e.g., pnpm test, uv run train.py)
  3. Primary metric — name, unit, and direction (lower or higher is better)
  4. Secondary metrics — optional additional metrics to track for tradeoff monitoring (e.g., memory, compile time)
  5. Files in scope — which files can be modified
  6. Constraints — time budget, off-limits files, correctness requirements

Optionally check for .claude/autoresearch-ai-plugin.local.md in the project root for persistent configuration:

---
enabled: true
max_iterations: 50
working_dir: "/path/to/project"
benchmark_timeout: 600
checks_timeout: 300
---

# Autoresearch Configuration

Additional context or notes for this project's autoresearch setup.
  • enabled — whether autoresearch is active (default: true)
  • max_iterations — stop after N experiments (default: 0 = unlimited)
  • working_dir — override directory for experiment files (default: current directory)
  • benchmark_timeout — benchmark timeout in seconds (default: 600)
  • checks_timeout — correctness checks timeout in seconds (default: 300)

Read the full file on GitHub · 382 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 382 lines · 209 tokens per session scan A 4e2b77718a92

Subscribe to this mod's changes

autoresearch is a skill published in the GitHub repository proyecto26/autoresearch-ai-plugin (12 stars, last pushed 1mo ago), licensed MIT. It adds 209 tokens to every session and 5,490 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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