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 stat-research-orchestratorgit 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/stat-research-orchestrator)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/stat-research-orchestrator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/stat-research-orchestrator/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/stat-research-orchestrator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/stat-research-orchestrator.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 | $0.00036 | $0.01504 |
| Opus 5.5 | $0.00014 | $0.00602 |
| Sonnet 5.5 | $0.00007 | $0.00301 |
| Haiku 4.5 | $0.00004 | $0.00150 |
Grade A, and why
stat-research-orchestrator 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 — 295 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Statistical Research Orchestrator
Overview
Coordinates the full statistical research pipeline. This is not a code-first benchmark workflow. The pipeline begins with formal problem formulation and requires theory before final comparisons and conclusions.
Full Pipeline
Topic prompt / topic file / dataset description
-> [stat-problem-formulator] formal problem, notation, assumptions, targets
-> [stat-method-proposer] proposed method, baselines, diagnostics, ablations
-> [stat-theory-analyzer] theoretical properties, proof sketches, predictions
-> [stat-experiment-designer] experiments, code, metrics, manifest
-> [stat-comparison-analyst] method comparison, theory-vs-experiment check
-> [stat-result-synthesizer] final report, conclusions, limitations
-> [stat-quality-auditor] formulation/theory/evidence audit
Workflow
Step 0: Invoke stat-problem-formulator
Provide the topic source and any requirements. Wait for:
progress/<TOPIC_ID>/step0_problem_formulation.md
Read:
- Formal data model
- Target parameter or decision target
- Assumptions
- Hypotheses or claims
- Evaluation criteria
- Theory targets
Do not proceed if the target or assumptions are undefined.
Step 1: Invoke stat-method-proposer
Provide the problem formulation. Wait for:
progress/<TOPIC_ID>/step1_method_proposal.md
Read:
- Proposed method
- Baselines
- Oracle references, if any
- Ablations
- Diagnostics
- Implementation requirements
Step 2: Invoke stat-theory-analyzer
Provide the formulation and method proposal. Wait for:
progress/<TOPIC_ID>/step2_theory_analysis.md
Read:
- Theoretical claims
- Required assumptions
- Proof sketches or derivations
- Predicted empirical patterns
- Limitations
Theory can be partial, but the report must honestly label what is proven, heuristic, or only experimentally supported.
Step 3: Invoke stat-experiment-designer
Provide formulation, method, and theory. Wait for:
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 First seen · 295 lines · 36 tokens per session scan A 5c76590a3618
stat-research-orchestrator is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,755 stars, last pushed 2mo ago), licensed MIT. It adds 36 tokens to every session and 1,504 once invoked, about $0.0001 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 2026-08-30.
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aclawdemy
The academic research platform for AI agents. Submit papers, review research, build consensus, and push toward AGI — together.