Scikit Learn
zLanqing/codex-claude-academic-skills
Machine learning in Python with scikit-learn. An agent skill from zLanqing/codex-claude-academic-skills.
Plan, execute, monitor, verify, compare, and document reproducible machine-learning experiments.
Install Auto Exp
$ npx skills add haibarazz/awesome-codex-research --skill auto-exp -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install haibarazz/awesome-codex-research auto-exp --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/haibarazz/awesome-codex-research.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/auto-exp .claude/skills/auto-exp && 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 "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .claude/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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/haibarazz/awesome-codex-research/tree/main/skills/auto-expType 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 haibarazz/awesome-codex-research --skill auto-exp -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install haibarazz/awesome-codex-research auto-exp --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haibarazz/awesome-codex-research.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/auto-exp .agents/skills/auto-exp && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .agents/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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 haibarazz/awesome-codex-research --skill auto-exp -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install haibarazz/awesome-codex-research auto-exp --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haibarazz/awesome-codex-research.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/auto-exp .cursor/skills/auto-exp && 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 "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .cursor/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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/haibarazz/awesome-codex-research.git --path skills/auto-exp--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 haibarazz/awesome-codex-research --skill auto-exp -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install haibarazz/awesome-codex-research auto-exp --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haibarazz/awesome-codex-research.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/auto-exp .gemini/skills/auto-exp && 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 "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .gemini/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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 haibarazz/awesome-codex-research auto-expInstalls 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 haibarazz/awesome-codex-research --skill auto-exp -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/haibarazz/awesome-codex-research.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/auto-exp .github/skills/auto-exp && 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 "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .github/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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 haibarazz/awesome-codex-research --skill auto-exp -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install haibarazz/awesome-codex-research auto-exp --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/haibarazz/awesome-codex-research.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/auto-exp .opencode/skills/auto-exp && 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 "auto-exp" agent skill from https://github.com/haibarazz/awesome-codex-research/tree/main/skills/auto-exp into .opencode/skills/auto-exp/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "auto-exp", 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
auto-exp- GitHub stars
- 100
- Token cost
- ~1.4k tokens
- SKILL.md length
- 629 words
- Files
- 4 (incl. references)
- Skills in repo
- 9
- Repo updated
- First seen
- Licence
- None found
At a glance
Plan, execute, monitor, verify, compare, and document reproducible machine-learning experiments.
- Works in 8 steps: Inspect context. Read the closest… → Lock the contract. State the hypothesis,… → Run the authorized preflight. Check… → …
- An AI agent is asked to design an experiment contract
- SKILL.md covers Independence, Scope Gate, Reference Routing and Core Workflow, plus 2 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Auto Exp is an agent skill from haibarazz/awesome-codex-research. Plan, execute, monitor, verify, compare, and document reproducible machine-learning experiments. Use when an AI agent is asked to design an experiment contract, implement or launch a training/evaluation run, monitor local or remote jobs, audit artifacts and metrics, maintain experiment logs or leaderboards, diagnose a failed run, or decide whether to continue, stop, retry, or archive an experiment.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `references/experiment_log_template.md`, `references/ml_experiment_playbook.md` and `references/pre_run_checklist.md`).
It sits in Data & Analytics, covering Machine learning.
When your agent uses it
- An AI agent is asked to design an experiment contract
- Launch a training/evaluation run
- Audit artifacts and metrics
- Maintain experiment logs
Example prompts
- “/auto-exp”
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- Inspect context. Read the closest project instructions, existing experiment logs, active configs, current baseline, data release, and…
- Lock the contract. State the hypothesis, change layer, single intended variable, dataset and split semantics, baseline, training protocol…
- Run the authorized preflight. Check paths, dependencies, sample counts, labels, leakage, truncation or shape limits, resource estimates…
- Execute without drift when requested. Make reusable code and configuration traceable in the project before execution. Launch with explicit…
- Monitor proportionately. Inspect progress, loss, learning rate, resource use, NaN/Inf, out-of-memory errors, tracebacks, disk pressure…
- Verify completion. Confirm process exit, expected row counts, metric files, predictions, histories, configs, audits, fingerprints, finite…
- Record the decision. Write settings, complete results, comparison to the fixed baseline, caveats, failure causes, and the next decision…
- Close safely. Sync only required artifacts, preserve provenance, and perform destructive cleanup or infrastructure shutdown only with the…
What it can do on your machine
Read from SKILL.md and the folder at commit e3ca125. 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
Auto Exp loads about 1.4k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 629 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
Without a licence we can't republish the file, so here is its outline and opening line. It has 629 words (~1,361 tokens).
“Treat every experiment as a scientific contract followed by an auditable execution, not as an isolated training command.”
- name
- auto-exp
Files
SKILL.md and 3 other files (references) in skills/auto-exp of haibarazz/awesome-codex-research.
- SKILL.md
- references/experiment_log_template.md
- references/ml_experiment_playbook.md
- references/pre_run_checklist.md
Open the folder on GitHubat commit e3ca125
Compare with similar skills
Auto Exp 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 |
|---|---|---|---|---|---|---|
| Auto Exp this skillhaibarazz/awesome-codex-research | 100 | — | ~1.4k | Automated safety check: Pass | None | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.6k | 17 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 6 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Agentic Kaggle WorkflowFrankS-IntelLab/agentic-kaggle-skill | 188 | — | ~4k | Automated safety check: Pass | MIT | |
| Retention Analysisliangdabiao/claude-data-analysis-ultra-main | 290 | 1 repos | ~1.3k | Automated safety check: Notes | None | |
| Geomlitalo-goncalves/geoML | 109 | — | ~4.2k | Automated safety check: Pass | GPL-3.0 |
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Related
Categories
Questions about Auto Exp
What does Auto Exp do?
Plan, execute, monitor, verify, compare, and document reproducible machine-learning experiments. Auto Exp is an agent skill from haibarazz/awesome-codex-research. Plan, execute, monitor, verify, compare, and document reproducible machine-learning experiments.
When should I use Auto Exp?
Auto Exp fits situations like: an AI agent is asked to design an experiment contract; launch a training/evaluation run; audit artifacts and metrics; maintain experiment logs.
How do I install Auto Exp in Claude Code?
Run `npx skills add haibarazz/awesome-codex-research --skill auto-exp -a claude-code`. Or copy the skill folder (skills/auto-exp in haibarazz/awesome-codex-research) into .claude/skills/auto-exp in your project. Claude Code loads it when a task matches its description.
How do I install Auto Exp in Codex?
Run `npx skills add haibarazz/awesome-codex-research --skill auto-exp -a codex`. Or copy the skill folder (skills/auto-exp in haibarazz/awesome-codex-research) into .agents/skills/auto-exp in your project. Codex loads it when a task matches its description.
Can I use Auto Exp 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 haibarazz/awesome-codex-research --skill auto-exp -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/auto-exp, .gemini/skills/auto-exp, .github/skills/auto-exp and .opencode/skills/auto-exp in your project.
What does Auto Exp need to run?
SKILL.md names no scripts, command-line tools or credentials: Auto Exp is instructions for the agent only.
Does Auto Exp 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 Auto Exp 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 Auto Exp use?
No licence was found for Auto Exp or its repository. Without one, default copyright applies: ask the author before reusing or redistributing it.
How many tokens does Auto Exp use?
About 1.4k tokens (SKILL.md is roughly 5.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.1k tokens, read only when the agent opens those files.
What are the alternatives to Auto Exp?
Skills that share tags, products or a category with Auto Exp: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.6k stars), Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Agentic Kaggle Workflow (FrankS-IntelLab/agentic-kaggle-skill, 188 stars) and Retention Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Auto Exp?
haibarazz (a GitHub user) maintains it in haibarazz/awesome-codex-research, which has 100 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on September 23, 2026.
Source: haibarazz/awesome-codex-research on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.