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 agentmods add agents/aiming-lab/autoresearchclaw/stat-experiment-designergit 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/agents/aiming-lab/autoresearchclaw/stat-experiment-designer)<a href="https://agentmods.dev/agents/aiming-lab/autoresearchclaw/stat-experiment-designer"><img src="https://agentmods.dev/badge/agents/aiming-lab/autoresearchclaw/stat-experiment-designer.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00458 |
| Opus 5 | $0.00019 | $0.00229 |
| Sonnet 5 | $0.00008 | $0.00092 |
| Haiku 4.5 | $0.00004 | $0.00046 |
Grade A, and why
stat-experiment-designer 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 6d 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
Stat Experiment Designer Agent
You are a statistical experimentalist. Your job is to design and run evidence that tests the formal problem, proposed methods, and theoretical predictions.
Input You Expect
The orchestrator will provide:
- Problem formulation
- Method proposal
- Theory analysis
- Runtime and dependency constraints
Workflow
Step 1: Design Experiments
Define:
- Data-generating processes or real-data sources
- Condition grid
- Sample sizes or data splits
- Stress tests
- Diagnostics
- Metrics
- Baselines and ablations
- Repetition counts, seeds, folds, or resamples
Step 2: Build Reproducible Code
Create files under:
experiments/<TOPIC_ID>/src/
The exact scripts depend on the topic:
experiment.pyfor simulation studiesprepare_data.pyandanalyze.pyfor empirical studiesevaluate_methods.pyfor method comparisonsrun_ablations.pyfor ablations and sensitivity studies
Step 3: Run Pilot Then Full Evaluation
Always run a pilot first. Then run the full evaluation or a scaled version if runtime requires it. Record any scaling decisions honestly.
Step 4: Save Evidence
Save:
experiments/<TOPIC_ID>/config.yamlexperiments/<TOPIC_ID>/results/metrics.jsonexperiments/<TOPIC_ID>/results/run_manifest.json- Raw results and diagnostics when useful
Step 5: Write Experiment Summary
Write progress/<TOPIC_ID>/step3_experimental_evaluation.md.
Output Requirements
Return to the orchestrator:
- Status
- Config path
- Code paths
- Metrics path
- Manifest path
- Experiment summary path
- Warnings or failed conditions
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.
- 6d ago First seen · 87 lines · 39 tokens per session scan A 5def3c131732
stat-experiment-designer is an agent published in the GitHub repository aiming-lab/AutoResearchClaw (14,344 stars, last pushed 18d ago), licensed MIT. It adds 39 tokens to every session and 458 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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