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 biggora/claude-plugins-registry --skill autoresearchgit clone --depth 1 https://github.com/biggora/claude-plugins-registryWrote 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/biggora/claude-plugins-registry/autoresearch)<a href="https://agentmods.dev/skills/biggora/claude-plugins-registry/autoresearch"><img src="https://agentmods.dev/badge/skills/biggora/claude-plugins-registry/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/biggora/claude-plugins-registry/autoresearch"><img src="https://agentmods.dev/badge/skills/biggora/claude-plugins-registry/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 | $0.00236 | $0.03469 |
| Opus 5 | $0.00118 | $0.01734 |
| Sonnet 5 | $0.00047 | $0.00694 |
| Haiku 4.5 | $0.00024 | $0.00347 |
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 9d 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 — 386 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
The Agent-Optimizer pattern, originating from Andrej Karpathy's autoresearch project, asks a deceptively simple question: what if an AI agent ran its own experiments indefinitely?
The insight is that most optimization work follows the same shape regardless of domain — you have something to improve, a way to measure improvement, rules about what you can change, and an isolated place to make changes. The agent's job is to run that loop faster and more systematically than a human would.
This skill makes that pattern available for any measurable goal: not just neural network research, but prompt tuning, email copy optimization, config parameters, CSS performance scores, query speed, or any other domain where "better" maps to a number.
The key shift: the human defines success criteria upfront. The agent handles the trial-and-error.
The Four Pillars
Every optimization project needs exactly these four things. If any pillar is missing, establish it before the loop begins.
1. Scalar Metric — The Score
A single number that goes up (or down) when things improve. Single means single — if you have multiple metrics, pick the one that matters most, or combine them into a weighted score.
Why scalar? Because the agent needs an unambiguous signal. "Better" must mean "higher number" (or lower, consistently).
Examples:
- Code performance: execution time in milliseconds (lower = better)
- Prompt engineering: LLM judge score 0-100 for output quality
- Email copy: click-through rate % from A/B test simulation
- Config tuning: requests-per-second under load
- CSS/UI: Lighthouse performance score 0-100
- Text quality: Flesch readability score
- Test suite: pass rate % or coverage %
2. Evaluator — The Test
An automated script that takes the current state of the Sandbox and outputs the metric. The evaluator is sacred — the agent never modifies it.
Why immutable? Because if the agent can change the test, it will eventually find a way to pass the test without actually improving anything. The evaluator is the ground truth.
What ships with it
3 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.
- 9d ago First seen · 386 lines · 236 tokens per session scan A 39152c188909
autoresearch is a skill published in the GitHub repository biggora/claude-plugins-registry (2 stars, last pushed 11d ago), licensed MIT. It adds 236 tokens to every session and 3,469 once invoked, about $0.0012 per session on Opus 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.
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