Review Ml Experiment

Organization

probabl-ai

review-ml-experiment

Read the stored report, then write one 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.

Overview

Publisher	probabl-ai
Repository	`skore-skills`
Skill name	`review-ml-experiment`
Stars	144
Forks	9
Bundled files	1
License	BSD-3-Clause
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
1 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by probabl-ai on GitHub. Read the source before you install it.

Installation

Install the Review Ml Experiment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/probabl-ai/skore-skills/tree/main/skills/review-ml-experiment
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/probabl-ai/skore-skills.git /tmp/skore-skills
mkdir -p .claude/skills
cp -r /tmp/skore-skills/skills/review-ml-experiment .claude/skills/review-ml-experiment

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Ml Experiment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Review Ml Experiment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Review Ml Experiment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 status and python -m skore_skills review consent --stem <stem>. Treat JSON action as authoritative.
- stop — no scratch/results/<stem>/report.html. Name that file and stop. Do not audit. Do not record-outcome.
- audit — report exists, digest does not. Load audit-ml-pipeline when installed. That skill runs materialize.py once and does not execute the audit file.
- proceed — digest already on disk. Do not re-run materialize.py unless the user asked to re-audit. A re-audit loads audit-ml-pipeline; that skill runs materialize.py once. Otherwise refresh idea files from the existing digest.
Missing audit skill → one-line skip. Return n/a — audit not run and write no idea files. Do not open the Project or call report.* 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-idea or search-ml-literature writes that file and its Ideas row when the user asks. Load research-ml-practice only if status.skills.research-ml-practice is 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>.md with Experiment, Source (audit:<stem>:checks.<code> or design:<stem>), Triage open, 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's Triage value and the matching Ideas status. A new candidate is open.
Upsert one ## Ideas row per file in journal/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 is open, discarded, or aside, matching Triage. A promoted file has no Ideas row. Experiment is this run's stem. Source is copied verbatim. Edit only that table.
Return the digest, JSON finding from python -m skore_skills audit finding --stem <stem>, the locator from python -m skore_skills loop locator --stem <stem>, and the idea paths.

Stop conditions

On proceed, do not re-run materialize.py unless the user asked to re-audit.
Do not write History, Backlog, Status, or a design note. The Ideas table is the only JOURNAL.md edit.
Do not call skore.evaluate or project.put.
Do not pick a winning idea.
Do not invent a missing child's procedure.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

evals/evals.json11 KB

Frequently asked questions

What does the Review Ml Experiment AI skill do?

Why use Review Ml Experiment on TypingMind?

Because you install it once and use it with any model. Review Ml Experiment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Review Ml Experiment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/probabl-ai/skore-skills/tree/main/skills/review-ml-experiment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Review Ml Experiment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Review Ml Experiment?

As many as you like. As long as a model supports skills, you can use Review Ml Experiment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Review Ml Experiment AI skill free?

Yes. It is published on GitHub by probabl-ai under the BSD-3-Clause license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Review ML Experiment

Human-facing prose

Procedure

Stop conditions

Bundled files

Frequently asked questions

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