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Agent skill

Statistical Experimental Evaluation

by aiming-lab in aiming-lab/AutoResearchClaw

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

MITAuto-check passed

Install Statistical Experimental Evaluation

skills CLI
$ npx skills add aiming-lab/AutoResearchClaw --skill statistical-experimental-evaluation -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install aiming-lab/AutoResearchClaw statistical-experimental-evaluation --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/external/agents/stat_research_agent/skills/statistical-experimental-evaluation .claude/skills/statistical-experimental-evaluation && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
statistical-experimental-evaluation
GitHub stars
15k
Token cost
~553 tokens
SKILL.md length
103 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

  • SKILL.md covers Overview, Experiment Plan, Required Artifacts and Evidence Schema, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Statistical Experimental Evaluation is an agent skill from aiming-lab/AutoResearchClaw. Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

Its SKILL.md is about 550 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.

Example prompts

  • “/statistical-experimental-evaluation”

What it can do on your machine

Read from SKILL.md and the folder at commit be4ba47. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are json).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Statistical Experimental Evaluation loads about 553 tokens when it runs. Until then it costs about 43 tokens; SKILL.md has 103 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~43
When it runs · the whole SKILL.md, loaded when a task matches
~553

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from aiming-lab/AutoResearchClaw at commit be4ba47, republished under its MIT licence (© aiming-lab). 103 words, ~553 tokens.

Download SKILL.mdSave it as .claude/skills/statistical-experimental-evaluation/SKILL.md (or your agent's skills folder).
name
statistical-experimental-evaluation
description
Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.
metadata.category
domain
metadata.trigger-keywords
experiment,simulation,evaluation,comparison,baseline,ablation,metrics,diagnostics,statistical evidence
metadata.applicable-stages
7,8,9,10,11,12,13,14
metadata.priority
1

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

text
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:

json
{
  "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:

json
[
  {
    "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.

© aiming-lab, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in external/agents/stat_research_agent/skills/statistical-experimental-evaluation of aiming-lab/AutoResearchClaw.

Open the folder on GitHubat commit be4ba47

Compare with similar skills

Statistical Experimental Evaluation next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Statistical Experimental Evaluation compared with similar skills
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Statistical Experimental Evaluation this skillaiming-lab/AutoResearchClaw15k—~553Automated safety check: PassMIT
Statistical Analystalirezarezvani/claude-skills28k1 repos~2.5kAutomated safety check: PassMIT
Statistical PowerK-Dense-AI/scientific-agent-skills48k1 repos~4.4kAutomated safety check: NotesMIT
Arize Evaluatorgithub/awesome-copilot40k2 repos~8.1kAutomated safety check: NotesMIT
Manuscript Statistics AuditYuan1z0825/nature-skills46k2 repos~2.1kAutomated safety check: PassApache-2.0
Experiment Designeralirezarezvani/claude-skills28k1 repos~783Automated safety check: PassMIT

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Questions about Statistical Experimental Evaluation

What does Statistical Experimental Evaluation do?

Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations. Statistical Experimental Evaluation is an agent skill from aiming-lab/AutoResearchClaw. Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

How do I install Statistical Experimental Evaluation in Claude Code?

Run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-experimental-evaluation -a claude-code`. Or copy the skill folder (external/agents/stat_research_agent/skills/statistical-experimental-evaluation in aiming-lab/AutoResearchClaw) into .claude/skills/statistical-experimental-evaluation in your project. Claude Code loads it when a task matches its description.

How do I install Statistical Experimental Evaluation in Codex?

Run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-experimental-evaluation -a codex`. Or copy the skill folder (external/agents/stat_research_agent/skills/statistical-experimental-evaluation in aiming-lab/AutoResearchClaw) into .agents/skills/statistical-experimental-evaluation in your project. Codex loads it when a task matches its description.

Can I use Statistical Experimental Evaluation in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add aiming-lab/AutoResearchClaw --skill statistical-experimental-evaluation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/statistical-experimental-evaluation, .gemini/skills/statistical-experimental-evaluation, .github/skills/statistical-experimental-evaluation and .opencode/skills/statistical-experimental-evaluation in your project.

What does Statistical Experimental Evaluation need to run?

SKILL.md names no scripts, command-line tools or credentials: Statistical Experimental Evaluation is instructions for the agent only.

Does Statistical Experimental Evaluation access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Statistical Experimental Evaluation safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Statistical Experimental Evaluation use?

Statistical Experimental Evaluation is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Statistical Experimental Evaluation use?

About 553 tokens (SKILL.md is roughly 2.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Statistical Experimental Evaluation?

Skills that share tags, products or a category with Statistical Experimental Evaluation: Statistical Analyst (alirezarezvani/claude-skills, 28k stars), Statistical Power (K-Dense-AI/scientific-agent-skills, 48k stars), Arize Evaluator (github/awesome-copilot, 40k stars) and Manuscript Statistics Audit (Yuan1z0825/nature-skills, 46k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Statistical Experimental Evaluation?

aiming-lab (a GitHub organization) maintains it in aiming-lab/AutoResearchClaw, which has 14,595 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on August 19, 2026.

Source: aiming-lab/AutoResearchClaw on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.