AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.
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 aiming-lab/AutoResearchClaw --skill experimental-designgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote 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/aiming-lab/autoresearchclaw/experimental-design)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/experimental-design"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/experimental-design/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/aiming-lab/autoresearchclaw/experimental-design"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/experimental-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00025 | $0.00276 |
| Opus 5 | $0.00013 | $0.00138 |
| Sonnet 5 | $0.00005 | $0.00055 |
| Haiku 4.5 | $0.00003 | $0.00028 |
Grade A, and why
experimental-design 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 10d 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.
What it actually says
Experimental Design Best Practice
- ALWAYS include meaningful baselines (not just random):
- At least one classical method baseline
- At least one recent SOTA method baseline
- A simple-but-strong baseline (e.g., linear probe, k-NN)
- Use MULTIPLE random seeds (minimum 3, ideally 5)
- Report mean +/- std across seeds
- Design ablations that isolate EACH key component:
- Remove one component at a time
- Each ablation must be meaningfully different from baseline
- Control variables: change only ONE thing per comparison
- Use standard splits (train/val/test) — never test on training data
- Report wall-clock time and memory usage alongside accuracy
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.
- 10d ago First seen · 27 lines · 25 tokens per session scan A a7a531dc023e
experimental-design is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,375 stars, last pushed 21d ago), licensed MIT. It adds 25 tokens to every session and 276 once invoked, about $0.0001 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 2026-08-30.
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Align speech, text, image, or video signals for multimodal benchmarks.