Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/bestagentkits/agency-skillsnpx agentmods add skills/bestagentkits/agency-skills/autoresearch-agentWrote 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.
[](https://agentmods.dev/skills/bestagentkits/agency-skills/autoresearch-agent)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/autoresearch-agent"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/autoresearch-agent/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.
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/autoresearch-agent"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/autoresearch-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.02904 |
| Opus 5 | $0.00057 | $0.01452 |
| Sonnet 5 | $0.00023 | $0.00581 |
| Haiku 4.5 | $0.00012 | $0.00290 |
Grade A, and why
autoresearch-agent scanned grade A with 1 finding 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 12d ago.
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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(["my-benchmark", "--json"], capture_output=True, text=True) How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 309 lines · 115 tokens per session scan A 0c26d36d71fc
autoresearch-agent is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 115 tokens to every session and 2,904 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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