Skill 详情
autoresearch-agent
Engineering optimization workflow.
使用前先检查
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SKILL.md
这段内容是审核时保存的快照。外部来源才是完整且最新的版本。
---
name: "autoresearch-agent"
description: "Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo."
license: MIT
metadata:
version: 2.0.0
author: Alireza Rezvani
category: engineering
updated: 2026-03-13
---
# Autoresearch Agent
> You sleep. The agent experiments. You wake up to results.
Autonomous experiment loop inspired by [Karpathy's autoresearch](https://github.com/karpathy/autoresearch). The agent edits one file, runs a fixed evaluation, keeps improvements, discards failures, and loops indefinitely.
Not one guess — fifty measured attempts, compounding.
---
## Slash Commands
| Command | What it does |
|---------|-------------|
| `/ar:setup` | Set up a new experiment interactively |
| `/ar:run` | Run a single experiment iteration |
| `/ar:loop` | Start autonomous loop with configurable interval (10m, 1h, daily, weekly, monthly) |
| `/ar:status` | Show dashboard and results |
| `/ar:resume` | Resume a paused experiment |
---
## When This Skill Activates
Recognize these patterns from the user:
- "Make this faster / smaller / better"
- "Optimize [file] for [metric]"
- "Improve my [headlines / copy / prompts]"
- "Run experiments overnight"
- "I want to get [metric] from X to Y"
- Any request involving: optimize, benchmark, improve, experiment loop, autoresearch
If the user describes a target file + a way to measure success → this skill applies.
---
## Setup
### First Time — Create the Experiment
Run the setup script. The user decides where experiments live:
**Project-level** (inside repo, git-tracked, shareable with team):
```bash
python scripts/setup_experiment.py \
--domain engineering \
--name api-speed \
--target src/api/search.py \
--eval "pytest bench.py --tb=no -q" \
--metric p50_ms \
--direction lower \
--scope project
```
**User-level** (personal, in `~/.autoresearch/`):
```bash
python scripts/setup_experiment.py \
--domain marketing \
--name medium-ctr \
--target content/titles.md \
--eval "python evaluate.py" \
--metric ctr_score \
--direction higher \
--evaluator llm_judge_content \
--scope user
```
The `--scope` flag determines where `.autoresearch/` lives:
- `project` (default) → `.autoresearch/` in the repo root. Experiment definitions are git-tracked. Results are gitignored.
- `user` → `~/.autoresearch/` in the home directory. Everything is personal.
### What Setup Creates
```
.autoresearch/
├── config.yaml ← Global settings
├── .gitignore ← Ignores results.tsv, *.log
└── {domain}/{experiment-name}/
├── program.md ← Objectives, constraints, strategy
├── config.cfg ← Target, eval cmd, metric, direction
├── results.tsv ← Experiment log (gitignored)
└── evaluate.py ← Evaluation script (if --evaluator used)
```
**results.tsv columns:** `commit | metric | status | description`
- `commit` — short git hash
- `metric` — float value or "N/A" for crashes
- `status` — keep | discard | crash
- `description` — what changed or why it crashed
### Domains
| Domain | Use Cases |
|--------|-----------|
| `engineering` | Code speed, memory, bundle size, test pass rate, build time |
| `marketing` | Headlines, social copy, email subjects, ad copy, engagement |
| `content` | Article structure, SEO descriptions, readability, CTR |
| `prompts` | System prompts, chatbot tone, agent instructions |
| `custom` | Anything else with a measurable metric |
### If在 GitHub 阅读完整来源 (打开外部页面)