Matplotlib
zLanqing/codex-claude-academic-skills
Low-level plotting library for full customization. An agent skill from zLanqing/codex-claude-academic-skills.
Post-evaluate review. An agent skill from probabl-ai/skills.
Install Review ML Experiment
$ npx skills add probabl-ai/skills --skill review-ml-experiment -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install probabl-ai/skills review-ml-experiment --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/probabl-ai/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-ml-experiment .claude/skills/review-ml-experiment && 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 "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .claude/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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/probabl-ai/skills/tree/main/skills/review-ml-experimentType 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 probabl-ai/skills --skill review-ml-experiment -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install probabl-ai/skills review-ml-experiment --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/probabl-ai/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/review-ml-experiment .agents/skills/review-ml-experiment && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .agents/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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 probabl-ai/skills --skill review-ml-experiment -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install probabl-ai/skills review-ml-experiment --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/probabl-ai/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/review-ml-experiment .cursor/skills/review-ml-experiment && 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 "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .cursor/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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/probabl-ai/skills.git --path skills/review-ml-experiment--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 probabl-ai/skills --skill review-ml-experiment -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install probabl-ai/skills review-ml-experiment --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/probabl-ai/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/review-ml-experiment .gemini/skills/review-ml-experiment && 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 "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .gemini/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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 probabl-ai/skills review-ml-experimentInstalls 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 probabl-ai/skills --skill review-ml-experiment -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/probabl-ai/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/review-ml-experiment .github/skills/review-ml-experiment && 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 "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .github/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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 probabl-ai/skills --skill review-ml-experiment -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install probabl-ai/skills review-ml-experiment --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/probabl-ai/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/review-ml-experiment .opencode/skills/review-ml-experiment && 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 "review-ml-experiment" agent skill from https://github.com/probabl-ai/skills/tree/main/skills/review-ml-experiment into .opencode/skills/review-ml-experiment/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "review-ml-experiment", 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
review-ml-experiment- GitHub stars
- 138
- Token cost
- ~933 tokens
- SKILL.md length
- 476 words
- Files
- 2
- Skills in repo
- 23
- Repo updated
- First seen
- Licence
- BSD-3-Clause
At a glance
Post-evaluate review. An agent skill from probabl-ai/skills.
- Works in 6 steps: Run python -m skore_skills status and → Missing audit skill → one-line skip.… → Read the design note, the EDA summary,… → …
- After a successful evaluate
- SKILL.md covers Human-facing prose, Procedure and Stop conditions
- Calls python
What it does
Review ML Experiment is an agent skill from probabl-ai/skills. Post-evaluate review. Read the stored report, then write one markdown idea file and one Ideas row per candidate. Trigger after a successful evaluate, on "review this stem", or when review consent is audit or proceed. Do not write History, Backlog, or a design note.
Its SKILL.md is about 930 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `evals/evals.json`).
It sits in Data & Analytics. The repository describes itself as: Tabular Data Science Skills for guardrailing AI Agents. The licence is BSD-3-Clause.
When your agent uses it
- After a successful evaluate
- On review this stem
- Review consent is audit
Example prompts
- “review this stem”
- “/review-ml-experiment”
Requirements
- Python 3
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- Run python -m skore_skills status and
- Missing audit skill → one-line skip. Return
- Read the design note, the EDA summary, the last History row,
- Write one file per candidate at
- Upsert one ## Ideas row per file in journal/JOURNAL.md.
- Return the digest, JSON finding from
What it can do on your machine
Read from SKILL.md and the folder at commit 77bb26c. 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
Shell commands in SKILL.md call:
python
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
Review ML Experiment loads about 933 tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 476 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 probabl-ai/skills at commit 77bb26c, republished under its BSD-3-Clause licence (© probabl-ai). 476 words, ~933 tokens.
Download SKILL.mdSave it as
.claude/skills/review-ml-experiment/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.- name
- review-ml-experiment
- description
- Post-evaluate review. Read the stored report, then write one markdown idea file and one Ideas row per candidate. Trigger after a successful evaluate, on "review this stem", or when review consent is audit or proceed. Do not write History, Backlog, or a design note.
- metadata.modelTier
- medium
Review ML Experiment
Optional loop step after evaluate. Record-outcome stays with the caller. This skill writes idea files and their Ideas rows. The stem is whichever experiment was reviewed. Check results were stored with the report, so the audit reads them.
Human-facing prose
Details: setup-workspace references/human_facing_prose.md.
Idea files describe this report and the follow-up — not skill
ids, cells run, or the wrapper CLI.
Procedure
- Run
python -m skore_skills statusandpython -m skore_skills review consent --stem <stem>. Treat JSONactionas authoritative.stop— noscratch/results/<stem>/report.html. Name that file and stop. Do not audit. Do not record-outcome.audit— report exists, digest does not. Loadaudit-ml-pipelinewhen installed. That skill runsmaterialize.pyonce and does not execute the audit file.proceed— digest already on disk. Do not re-runmaterialize.pyunless the user asked to re-audit. A re-audit loadsaudit-ml-pipeline; that skill runsmaterialize.pyonce. Otherwise refresh idea files from the existing digest.
- Missing audit skill → one-line skip. Return
n/a — audit not runand write no idea files. Do not open the Project or callreport.*here. - Read the design note, the EDA summary, the last History row,
and the digest. One candidate per
Issues:/Tips:line. A methodological gap the design note named and this run did not test is another candidate. A user idea or a literature query is not a candidate here: after this skill returns,shape-user-ideaorsearch-ml-literaturewrites that file and its Ideas row when the user asks. Loadresearch-ml-practiceonly ifstatus.skills.research-ml-practiceis true and an audit or design candidate needs sources; otherwise one-line skip. Do not invent papers, metrics, or a winner. - Write one file per candidate at
journal/ideas/<stem>-<slug>.mdwith Experiment, Source (audit:<stem>:checks.<code>ordesign:<stem>), Triageopen, Question, Why now, What changes, Open gaps. No acceptance criteria. For a check candidate, read the documentation URL on that line and apply that page's recommendation in What changes. On a refresh, keep an existing file'sTriagevalue and the matching Ideas status. A new candidate isopen. - Upsert one
## Ideasrow per file injournal/JOURNAL.md. If that table is missing, insert it between History and Backlog. Columns: Question, Status, Experiment, Source. Question is the file's Question as plain text, not a link. Status isopen,discarded, oraside, matchingTriage. Apromotedfile has no Ideas row. Experiment is this run's stem. Source is copied verbatim. Edit only that table. - Return the digest, JSON
findingfrompython -m skore_skills audit finding --stem <stem>, the locator frompython -m skore_skills loop locator --stem <stem>, and the idea paths.
Show full SKILL.md (51 more words)Show less
Stop conditions
- On
proceed, do not re-runmaterialize.pyunless the user asked to re-audit. - Do not write History, Backlog, Status, or a design note.
The Ideas table is the only
JOURNAL.mdedit. - Do not call
skore.evaluateorproject.put. - Do not pick a winning idea.
- Do not invent a missing child's procedure.
© probabl-ai, BSD-3-Clause. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 1 other file in skills/review-ml-experiment of probabl-ai/skills.
- SKILL.md
- evals/evals.json
Open the folder on GitHubat commit 77bb26c
Compare with similar skills
Review ML Experiment 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 |
|---|---|---|---|---|---|---|
| Review ML Experiment this skillprobabl-ai/skills | 138 | — | ~933 | Automated safety check: Pass | BSD-3-Clause | |
| MatplotlibzLanqing/codex-claude-academic-skills | 4.7k | 17 repos | ~2.9k | Automated safety check: Pass | MIT | |
| Exploratory Data Analysisspacering-net/codeg | 3.9k | 14 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Scikit LearnzLanqing/codex-claude-academic-skills | 4.7k | 16 repos | ~3.9k | Automated safety check: Pass | BSD-3-Clause | |
| Chart Visualizationbytedance/deer-flow | 84k | 1 repos | ~840 | Automated safety check: Pass | MIT | |
| TimesFM Forecastinggoogle-research/timesfm | 34k | — | ~4.7k | Automated safety check: Pass | Apache-2.0 |
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Questions about Review ML Experiment
What does Review ML Experiment do?
Post-evaluate review. An agent skill from probabl-ai/skills. Review ML Experiment is an agent skill from probabl-ai/skills. Post-evaluate review.
When should I use Review ML Experiment?
Review ML Experiment fits situations like: after a successful evaluate; on review this stem; review consent is audit.
How do I install Review ML Experiment in Claude Code?
Run `npx skills add probabl-ai/skills --skill review-ml-experiment -a claude-code`. Or copy the skill folder (skills/review-ml-experiment in probabl-ai/skills) into .claude/skills/review-ml-experiment in your project. Claude Code loads it when a task matches its description.
How do I install Review ML Experiment in Codex?
Run `npx skills add probabl-ai/skills --skill review-ml-experiment -a codex`. Or copy the skill folder (skills/review-ml-experiment in probabl-ai/skills) into .agents/skills/review-ml-experiment in your project. Codex loads it when a task matches its description.
Can I use Review ML Experiment 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 probabl-ai/skills --skill review-ml-experiment -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-ml-experiment, .gemini/skills/review-ml-experiment, .github/skills/review-ml-experiment and .opencode/skills/review-ml-experiment in your project.
What does Review ML Experiment need to run?
Going by SKILL.md and its folder, Review ML Experiment needs the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Review ML Experiment 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 Review ML Experiment 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 Review ML Experiment use?
Review ML Experiment is published under the BSD-3-Clause licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Review ML Experiment use?
About 933 tokens (SKILL.md is roughly 3.7k 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 Review ML Experiment?
Skills that share tags, products or a category with Review ML Experiment: Matplotlib (zLanqing/codex-claude-academic-skills, 4.7k stars), Exploratory Data Analysis (spacering-net/codeg, 3.9k stars), Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars) and Chart Visualization (bytedance/deer-flow, 84k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Review ML Experiment?
probabl-ai (a GitHub organization) maintains it in probabl-ai/skills, which has 138 GitHub stars. The repository holds 23 skills in this directory. The repository was last updated on October 9, 2026.
Source: probabl-ai/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.