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.
npx skills add a-tokyo/agent-skills-harness --skill autoresearchgit clone --depth 1 https://github.com/a-tokyo/agent-skills-harnessWrote 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/a-tokyo/agent-skills-harness/autoresearch)<a href="https://agentmods.dev/skills/a-tokyo/agent-skills-harness/autoresearch"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills-harness/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.
<a href="https://agentmods.dev/skills/a-tokyo/agent-skills-harness/autoresearch"><img src="https://agentmods.dev/badge/skills/a-tokyo/agent-skills-harness/autoresearch.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 | $0.00116 | $0.04744 |
| Opus 5.5 | $0.00046 | $0.01898 |
| Sonnet 5.5 | $0.00023 | $0.00949 |
| Haiku 4.5 | $0.00012 | $0.00474 |
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 2mo 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.
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.
How it starts
The opening of the file, as written. The whole thing — 464 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch: Autonomous Iterative Experimentation
An autonomous experimentation loop for any programming task. You define the goal and how to measure it; the agent iterates autonomously -- modifying code, running experiments, measuring results, and keeping or discarding changes -- until interrupted.
Inspired by Karpathy's autoresearch, enhanced with battle-tested patterns from pi-autoresearch.
Agent Behavior Rules
- DO guide the user through the Setup phase interactively before starting the loop.
- DO establish a baseline measurement before making any changes.
- DO use the METRIC protocol for all measurements.
- DO keep structured logs:
autoresearch.jsonl(machine) +results.tsv(human). - DO record ASI fields (hypothesis, learned, rollback_reason) on every experiment.
- DO commit before running, revert on failure -- only kept commits remain on the branch.
- DO run autonomously once the loop starts -- never pause to ask "should I continue?".
- DO run checks (if
autoresearch.checks.shexists) before measuring. - DO NOT modify files the user marked as out-of-scope.
- DO NOT skip the measurement step -- every experiment must be measured.
- DO NOT keep changes that regress the metric unless the user explicitly allowed trade-offs.
- DO NOT install new dependencies or make environment changes unless the user approved it.
Phase 1: Setup (Interactive)
Before any experimentation begins, work with the user to establish these parameters. Ask the user directly for each item. Do not assume or skip any.
1.1 Define the Goal
Ask the user:
What are you trying to improve or optimize?
Examples: execution time, memory usage, binary size, test pass rate, code coverage, API response latency, throughput, error rate, benchmark score, build time, bundle size, LLM output quality, prompt effectiveness, skill accuracy, etc.
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.
- 2mo ago First seen · 464 lines · 116 tokens per session scan A 67f6c4df3345
autoresearch is a skill published in the GitHub repository a-tokyo/agent-skills-harness (11 stars, last pushed 5d ago), licensed MIT. It adds 116 tokens to every session and 4,744 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-08-31.
autoresearch compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| autoresearcha-tokyo/agent-skills-harness | Skill | 11 | 5d ago | MIT | A | 116 | Claude Code, Codex |
| refactor-cleandzhng/skills | Skill | 1.4k | today | MIT | A | 80 | Claude Code, Codex |
| arbor-agent-ideateRUC-NLPIR/Arbor | Skill | 1.1k | 4d ago | Apache-2.0 | A | 66 | Claude Code, Codex |
| create-notesHuman-Agent-Society/CORAL | Skill | 1.1k | 19d ago | Apache-2.0 | A | 217 | Claude Code, Codex |
| explore-unknownsdzhng/skills | Skill | 1.4k | today | MIT | A | 162 | Claude Code, Codex |
| grid-ctf-opsgreyhaven-ai/autocontext | Skill | 1.3k | today | Apache-2.0 | A | 42 | Claude Code, Codex |
| design-with-imagesdzhng/skills | Skill | 1.4k | today | MIT | A | 55 | Claude Code, Codex |