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 adriannoes/awesome-agentic-ai --skill autoresearchgit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/autoresearch)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/autoresearch"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/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/adriannoes/awesome-agentic-ai/autoresearch"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/autoresearch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 8 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Agent Snooping · line 268 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00100 | $0.04195 |
| Opus 5.5 | $0.00040 | $0.01678 |
| Sonnet 5.5 | $0.00020 | $0.00839 |
| Haiku 4.5 | $0.00010 | $0.00419 |
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 1mo 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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Autoresearch for Skills
Most skills work about 70% of the time. The other 30% you get garbage. The fix isn't to rewrite the skill from scratch. It's to let an agent run it dozens of times, score every output, and tighten the prompt until that 30% disappears.
This skill adapts Andrej Karpathy's autoresearch methodology (autonomous experimentation loops) to Claude Code skills. Instead of optimizing ML training code, we optimize skill prompts.
the core job
Take any existing skill, define what "good output" looks like as binary yes/no checks, then run an autonomous loop that:
- Generates outputs from the skill using test inputs
- Scores every output against the eval criteria
- Mutates the skill prompt to fix failures
- Keeps mutations that improve the score, discards the rest
- Repeats until the score ceiling is hit or the user stops it
Output: An improved SKILL.md + results.tsv log + changelog.md of every mutation attempted + a live HTML dashboard you can watch in your browser.
before starting: gather context
STOP. Do not run any experiments until all fields below are confirmed with the user. Ask for any missing fields before proceeding.
- Target skill — Which skill do you want to optimize? (need the exact path to SKILL.md)
- Test inputs — What 3-5 different prompts/scenarios should we test the skill with? (variety matters — pick inputs that cover different use cases so we don't overfit to one scenario)
- Eval criteria — What 3-6 binary yes/no checks define a good output? (these are your "test questions" — see references/eval-guide.md for how to write good evals)
- Runs per experiment — How many times should we run the skill per mutation? Default: 5. (more runs = more reliable scores, but slower and more expensive. 5 is the sweet spot for most skills.)
- Run interval — How often should experiments cycle? Default: every 2 minutes. (shorter = faster iteration, but costs more)
- Budget cap — Optional. Max number of experiment cycles before stopping. Default: no cap (runs until you stop it).
What ships with it
1 file 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.
- 1mo ago Changed · +10 lines 522819a247a5
- 1mo ago First seen · 332 lines · 100 tokens per session scan A 24c775cbca93
autoresearch is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (72 stars, last pushed 3d ago), licensed MIT. It adds 100 tokens to every session and 4,195 once invoked, about $0.0004 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 2026-09-03.
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