autoresearch-agent

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":"2026-03-13T00:00:00.000Z"}

Autoresearch Agent

You sleep. The agent experiments. You wake up to results.

Autonomous experiment loop inspired by Karpathy's 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):

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/):

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 program.md Already Exists

The user may have written their own program.md. If found in the experiment directory, read it. It overrides the template. Only ask for what's missing.

---

Agent Protocol

You are the loop. The scripts handle setup and evaluation — you handle the creative work.

Before Starting

1. Read .autoresearch/{domain}/{name}/config.cfg to get:
   • target — the file you edit
   • evaluate_cmd — the command that measures your changes
   • metric — the metric name to look for in eval output
   • metric_direction — "lower" or "higher" is better
   • time_budget_minutes — max time per evaluation
2. Read program.md for strategy, constraints, and what you can/cannot change
3. Read results.tsv for experiment history (columns: commit, metric, status, description)
4. Checkout the experiment branch: git checkout autoresearch/{domain}/{name}

Each Iteration

1. Review results.tsv — what worked? What failed? What hasn't been tried?
2. Decide ONE change to the target file. One variable per experiment.
3. Edit the target file
4. Commit: git add {target} && git commit -m "experiment: {description}"
5. Evaluate: python scripts/run_experiment.py --experiment {domain}/{name} --single
6. Read the output — it prints KEEP, DISCARD, or CRASH with the metric value
7. Go to step 1

What the Script Handles (you don't)

• Running the eval command with timeout
• Parsing the metric from eval output
• Comparing to previous best
• Reverting the commit on failure (git reset --hard HEAD~1)
• Logging the result to results.tsv

Starting an Experiment

# Single iteration (the agent calls this repeatedly)
python scripts/run_experiment.py --experiment engineering/api-speed --single

# Dry run (test setup before starting)
python scripts/run_experiment.py --experiment engineering/api-speed --dry-run

Strategy Escalation

• Runs 1-5: Low-hanging fruit (obvious improvements, simple optimizations)
• Runs 6-15: Systematic exploration (vary one parameter at a time)
• Runs 16-30: Structural changes (algorithm swaps, architecture shifts)
• Runs 30+: Radical experiments (completely different approaches)
• If no improvement in 20+ runs: update program.md Strategy section

Self-Improvement

After every 10 experiments, review results.tsv for patterns. Update the Strategy section of program.md with what you learned (e.g., "caching changes consistently improve by 5-10%", "refactoring attempts never improve the metric"). Future iterations benefit from this accumulated knowledge.

Stopping

• Run until interrupted by the user, context limit reached, or goal in program.md is met
• Before stopping: ensure results.tsv is up to date
• On context limit: the next session can resume — results.tsv and git log persist

Rules

• One change per experiment. Don't change 5 things at once. You won't know what worked.
• Simplicity criterion. A small improvement that adds ugly complexity is not worth it. Equal performance with simpler code is a win. Removing code that gets same results is the best outcome.
• Never modify the evaluator. evaluate.py is the ground truth. Modifying it invalidates all comparisons. Hard stop if you catch yourself doing this.
• Timeout. If a run exceeds 2.5× the time budget, kill it and treat as crash.
• Crash handling. If it's a typo or missing import, fix and re-run. If the idea is fundamentally broken, revert, log "crash", move on. 5 consecutive crashes → pause and alert.
• No new dependencies. Only use what's already available in the project.

---

Evaluators

Ready-to-use evaluation scripts. Copied into the experiment directory during setup with --evaluator.

Free Evaluators (no API cost)

Evaluator	Metric	Use Case
benchmark_speed	p50_ms (lower)	Function/API execution time
benchmark_size	size_bytes (lower)	File, bundle, Docker image size
test_pass_rate	pass_rate (higher)	Test suite pass percentage
build_speed	build_seconds (lower)	Build/compile/Docker build time
memory_usage	peak_mb (lower)	Peak memory during execution

LLM Judge Evaluators (uses your subscription)

Evaluator	Metric	Use Case
llm_judge_content	ctr_score 0-10 (higher)	Headlines, titles, descriptions
llm_judge_prompt	quality_score 0-100 (higher)	System prompts, agent instructions
llm_judge_copy	engagement_score 0-10 (higher)	Social posts, ad copy, emails

LLM judges call the CLI tool the user is already running (Claude, Codex, Gemini). The evaluation prompt is locked inside evaluate.py — the agent cannot modify it. This prevents the agent from gaming its own evaluator.

The user's existing subscription covers the cost:

• Claude Code Max → unlimited Claude calls for evaluation
• Codex CLI (ChatGPT Pro) → unlimited Codex calls
• Gemini CLI (free tier) → free evaluation calls

Custom Evaluators

If no built-in evaluator fits, the user writes their own evaluate.py. Only requirement: it must print metric_name: value to stdout.

#!/usr/bin/env python3
# My custom evaluator — DO NOT MODIFY after experiment starts
import subprocess
result = subprocess.run(["my-benchmark", "--json"], capture_output=True, text=True)
# Parse and output
print(f"my_metric: {parse_score(result.stdout)}")

---

Viewing Results

# Single experiment
python scripts/log_results.py --experiment engineering/api-speed

# All experiments in a domain
python scripts/log_results.py --domain engineering

# Cross-experiment dashboard
python scripts/log_results.py --dashboard

# Export formats
python scripts/log_results.py --experiment engineering/api-speed --format csv --output results.csv
python scripts/log_results.py --experiment engineering/api-speed --format markdown --output results.md
python scripts/log_results.py --dashboard --format markdown --output dashboard.md

Dashboard Output

DOMAIN          EXPERIMENT          RUNS  KEPT  BEST         Δ FROM START  STATUS
engineering     api-speed            47    14   185ms        -76.9%        active
engineering     bundle-size          23     8   412KB        -58.3%        paused
marketing       medium-ctr           31    11   8.4/10       +68.0%        active
prompts         support-tone         15     6   82/100       +46.4%        done

Export Formats

• TSV — default, tab-separated (compatible with spreadsheets)
• CSV — comma-separated, with proper quoting
• Markdown — formatted table, readable in GitHub/docs

---

Proactive Triggers

Flag these without being asked:

• No evaluation command works → Test it before starting the loop. Run once, verify output.
• Target file not in git → git init && git add . && git commit -m 'initial' first.
• Metric direction unclear → Ask: is lower or higher better? Must know before starting.
• Time budget too short → If eval takes longer than budget, every run crashes.
• Agent modifying evaluate.py → Hard stop. This invalidates all comparisons.
• 5 consecutive crashes → Pause the loop. Alert the user. Don't keep burning cycles.
• No improvement in 20+ runs → Suggest changing strategy in program.md or trying a different approach.

---

Installation

One-liner (any tool)

git clone https://github.com/alirezarezvani/claude-skills.git
cp -r claude-skills/engineering/autoresearch-agent ~/.claude/skills/

Multi-tool install

./scripts/convert.sh --skill autoresearch-agent --tool codex|gemini|cursor|windsurf|openclaw

OpenClaw

clawhub install cs-autoresearch-agent

---

Related Skills

• self-improving-agent — improves an agent's own memory/rules over time. NOT for structured experiment loops.
• senior-ml-engineer — ML architecture decisions. Complementary — use for initial design, then autoresearch for optimization.
• tdd-guide — test-driven development. Complementary — tests can be the evaluation function.
• skill-security-auditor — audit skills before publishing. NOT for optimization loops.