Scientific Critical Thinking
weapp-tailwindcss/weapp-tailwindcss
Evaluate research rigor. An agent skill from weapp-tailwindcss/weapp-tailwindcss.
Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.
Install Experimental Design
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install aiming-lab/AutoResearchClaw experimental-design --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .claude/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .claude/skills/experimental-design && rm -rf skills-srcUse ~/.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/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .claude/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-designType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install aiming-lab/AutoResearchClaw experimental-design --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .agents/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .agents/skills/experimental-design && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .agents/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install aiming-lab/AutoResearchClaw experimental-design --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .cursor/skills/experimental-design && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .cursor/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/aiming-lab/AutoResearchClaw.git --path researchclaw/skills/builtin/experiment/experimental-design--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install aiming-lab/AutoResearchClaw experimental-design --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .gemini/skills/experimental-design && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .gemini/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install aiming-lab/AutoResearchClaw experimental-designInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .github/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .github/skills/experimental-design && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .github/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install aiming-lab/AutoResearchClaw experimental-design --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/researchclaw/skills/builtin/experiment/experimental-design .opencode/skills/experimental-design && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "experimental-design" agent skill from https://github.com/aiming-lab/AutoResearchClaw/tree/main/researchclaw/skills/builtin/experiment/experimental-design into .opencode/skills/experimental-design/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "experimental-design", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
experimental-design- GitHub stars
- 15k
- Token cost
- ~286 tokens
- SKILL.md length
- 97 words
- Files
- 1
- Skills in repo
- 34
- Repo updated
- First seen
- Licence
- MIT
At a glance
Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw.
- Works in 7 steps: ALWAYS include meaningful baselines (not… → Use MULTIPLE random seeds (minimum 3,… → Report mean +/- std across seeds → …
- Planning ablations
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Controlled experiments
What it does
Experimental Design is an agent skill from aiming-lab/AutoResearchClaw. Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
Its SKILL.md is about 290 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Research & Science, covering Experimental design. The repository describes itself as: Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞. The licence is MIT.
When your agent uses it
- Planning ablations
- Controlled experiments
Example prompts
- “/experimental-design”
Workflow steps
7 steps, taken from the first numbered list in SKILL.md.
- ALWAYS include meaningful baselines (not just random)
- Use MULTIPLE random seeds (minimum 3, ideally 5)
- Report mean +/- std across seeds
- Design ablations that isolate EACH key component
- 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 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.
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
Experimental Design loads about 286 tokens when it runs. Until then it costs about 36 tokens; SKILL.md has 97 words of instructions outside code blocks.
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). 97 words, ~286 tokens.
Download SKILL.mdSave it as
.claude/skills/experimental-design/SKILL.md (or your agent's skills folder).- name
- experimental-design
- description
- Best practices for designing reproducible ML experiments. Use when planning ablations, baselines, or controlled experiments.
- metadata.category
- experiment
- metadata.trigger-keywords
- experiment,ablation,baseline,control,hypothesis,reproducib
- metadata.applicable-stages
- 9,10,12
- metadata.priority
- 2
- metadata.version
- 1.0
- metadata.author
- researchclaw
- metadata.references
- Bouthillier et al., Accounting for Variance in ML Benchmarks, MLSys 2021
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
© 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 researchclaw/skills/builtin/experiment/experimental-design of aiming-lab/AutoResearchClaw.
Open the folder on GitHubat commit be4ba47
Compare with similar skills
Experimental Design 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Experimental Design this skillaiming-lab/AutoResearchClaw | 15k | — | ~286 | Automated safety check: Pass | MIT | |
| Scientific Critical Thinkingweapp-tailwindcss/weapp-tailwindcss | 1.9k | 23 repos | ~5.9k | Automated safety check: Notes | MIT | |
| Claim-Driven Experiment PlannerzjYao36/Auto-Research-Refine | 128 | 7 repos | ~2.3k | Automated safety check: Notes | None | |
| Benchmark Paper TemplateHKUSTDial/Supervisor-Skills | 8.5k | — | ~2.8k | Automated safety check: Pass | CC-BY-4.0 | |
| Research Refine PipelinezjYao36/Auto-Research-Refine | 128 | 6 repos | ~1.4k | Automated safety check: Notes | None | |
| Experimental DesignOleafly/Oleafly | 206 | 4 repos | ~3.5k | Automated safety check: Notes | MIT |
Similar skills
Claim-Driven Experiment Planner
zjYao36/Auto-Research-Refine
Turns a refined research proposal into a claim-to-evidence-to-run-order roadmap instead of a sprawling benchmark wishlist.
Benchmark Paper Template
HKUSTDial/Supervisor-Skills
Structures benchmark and evaluation papers around five pillars, with a completeness audit, an Introduction logic chain, a section skeleton and a pre-submission checklist.
Research Refine Pipeline
zjYao36/Auto-Research-Refine
Chains research-refine and experiment-plan to turn a vague research direction into a focused proposal and a claim-driven experiment roadmap.
Experimental Design
Oleafly/Oleafly
Design experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so results are interpretable.
Research Refine
zjYao36/Auto-Research-Refine
Turns a vague research direction into a focused, problem-anchored method plan through up to five review rounds with a second model.
More from aiming-lab/AutoResearchClaw
All 34 skills in this repoA-Evolve Agent Improvement
aiming-lab/AutoResearchClaw
Diagnoses where an agent failed across runs and turns the findings into new skills, system prompt patches and knowledge entries, using the A-Evolve loop.
Metabolic Study Planner
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
MFA Pipeline Orchestrator
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
Qiskit 2.x Quantum ML Reference
aiming-lab/AutoResearchClaw
Reference patterns for writing qiskit 2.x code for variational quantum machine learning: feature maps, VQC training, VQE for chemistry, MPS circuits and noise models.
Genome-Scale Metabolic Model Builder
aiming-lab/AutoResearchClaw
Builds or loads a genome-scale metabolic model in COBRApy, sets its growth medium and objective, and exports it as a validated JSON file for flux analysis.
Biopython Bioinformatics
aiming-lab/AutoResearchClaw
Quick reference for Biopython work: sequence operations, SeqIO file parsing, BLAST searches, Entrez queries, phylogenetic trees and PDB structure analysis.
Related
Categories
Questions about Experimental Design
What does Experimental Design do?
Best practices for designing reproducible ML experiments. An agent skill from aiming-lab/AutoResearchClaw. Experimental Design is an agent skill from aiming-lab/AutoResearchClaw. Best practices for designing reproducible ML experiments.
When should I use Experimental Design?
Experimental Design fits situations like: planning ablations; controlled experiments.
How do I install Experimental Design in Claude Code?
Run `npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a claude-code`. Or copy the skill folder (researchclaw/skills/builtin/experiment/experimental-design in aiming-lab/AutoResearchClaw) into .claude/skills/experimental-design in your project. Claude Code loads it when a task matches its description.
How do I install Experimental Design in Codex?
Run `npx skills add aiming-lab/AutoResearchClaw --skill experimental-design -a codex`. Or copy the skill folder (researchclaw/skills/builtin/experiment/experimental-design in aiming-lab/AutoResearchClaw) into .agents/skills/experimental-design in your project. Codex loads it when a task matches its description.
Can I use Experimental Design 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 experimental-design -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/experimental-design, .gemini/skills/experimental-design, .github/skills/experimental-design and .opencode/skills/experimental-design in your project.
What does Experimental Design need to run?
SKILL.md names no scripts, command-line tools or credentials: Experimental Design is instructions for the agent only.
Does Experimental Design 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 Experimental Design 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 Experimental Design use?
Experimental Design 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 Experimental Design use?
About 286 tokens (SKILL.md is roughly 1.1k 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 Experimental Design?
Skills that share tags, products or a category with Experimental Design: Scientific Critical Thinking (weapp-tailwindcss/weapp-tailwindcss, 1.9k stars), Claim-Driven Experiment Planner (zjYao36/Auto-Research-Refine, 128 stars), Benchmark Paper Template (HKUSTDial/Supervisor-Skills, 8.5k stars) and Research Refine Pipeline (zjYao36/Auto-Research-Refine, 128 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Experimental Design?
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.