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 statistical-experimental-evaluationgit 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/statistical-experimental-evaluation)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/statistical-experimental-evaluation"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/statistical-experimental-evaluation.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00540 |
| Opus 5 | $0.00015 | $0.00270 |
| Sonnet 5 | $0.00006 | $0.00108 |
| Haiku 4.5 | $0.00003 | $0.00054 |
Grade A, and why
statistical-experimental-evaluation 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 8d 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
Statistical Experimental Evaluation
Overview
Use this skill after formulation, method proposal, and theory. Experiments should test specific claims and theoretical predictions.
Experiment Plan
Define:
- Conditions or data-generating processes
- Real data source or synthetic data generator
- Sample sizes, folds, repetitions, seeds, or resamples
- Proposed method
- Baselines
- Ablations
- Diagnostics
- Metrics
- Failure accounting
Required Artifacts
experiments/<TOPIC_ID>/config.yaml
experiments/<TOPIC_ID>/src/
experiments/<TOPIC_ID>/results/metrics.json
experiments/<TOPIC_ID>/results/run_manifest.json
experiments/<TOPIC_ID>/results/comparison_summary.md
experiments/<TOPIC_ID>/results/claim_verdicts.json
experiments/<TOPIC_ID>/report/paper.md
experiments/<TOPIC_ID>/README.md
Evidence Schema
Use a row-oriented metric format:
{
"topic_id": "TXX",
"metric_rows": [
{
"claim_id": "C1",
"method": "proposed_method",
"baseline": "standard_method",
"condition": "stress_condition",
"metric": "risk",
"value": 0.12,
"status": "ok"
}
]
}
Claim verdicts should connect theory and experiments:
[
{
"claim_id": "C1",
"verdict": "supported",
"theory_support": "Proposition 1 under A1-A3",
"experimental_support": "Proposed method has lower risk in conditions X-Y",
"comparison": "Outperforms baseline B on metric M",
"limitations": "Finite sample only; assumption A2 not tested"
}
]
Evidence Rules
- A metric must map to a formulated claim.
- A comparison must use the same data conditions across methods.
- Failed runs must be counted.
- Runtime reductions must be recorded.
- Results must be interpreted against theoretical predictions.
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.
- 8d ago First seen · 90 lines · 31 tokens per session scan A 365cf7b3b753
statistical-experimental-evaluation is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,361 stars, last pushed 20d ago), licensed MIT. It adds 31 tokens to every session and 540 once invoked, about $0.0002 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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proof-derivation-forensics
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research-writing
A collection of 30 prompt templates for writing and reviewing scientific papers. It covers tasks such as translating, editing, summarizing research, writing sections, creating figure captions, and preparing reviewer replies.