Research Ml Practice

Organization

probabl-ai

research-ml-practice

Literature research for an ML methodology concern. Trigger when explore-ml-data or build-ml-pipeline load this skill. Not for routine profiling or a single API signature — use `api get` for symbols. Not a session owner — callers distill and write project files. HOW TO USE: skip intake fields the caller already supplied. Abstract the problem class before searching (never the dataset proper name). Survey extra analyses or depth a named concern; search distinct angles, fetch primary sources, follow up if thin, write `scratch/research/`. Return the path plus one or two sentences. Never mutate raw data, pick the final learner, or write `data_analysis.md` / the design note. Chat stays path + those sentences even when a harness asks for a complete answer in the message.

Overview

Publisher	probabl-ai
Repository	`skore-skills`
Skill name	`research-ml-practice`
Stars	144
Forks	9
Bundled files	2
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.
2 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 Research Ml Practice 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/research-ml-practice
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/research-ml-practice .claude/skills/research-ml-practice

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

Use it in TypingMind

Enable Research Ml Practice 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 Research Ml Practice 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 Research Ml Practice 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.

Research ML Practice

Worker skill. Callers own the stage turn and the user-facing summary. Do not git end-turn.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Chat stays path + one or two sentences of the finding. Do not narrate the skills framework or the wrapper CLI.

Sequence

Intake. Infer modality from dtypes / JOURNAL when obvious; use a stated domain and stage (data_analysis | model) if the caller named them. Then pick a mode (references/search.md):
- Caller passed a named concern (not the canned EDA extra-analysis question) → depth. Do not re-ask. Do not run the canned survey first.
- Caller passed the canned extra-analysis survey or “survey extra analyses” → survey.
- Neither → if JOURNAL and an EDA report exist, run the canned survey; else AskUserQuestion for a named concern, stating the modality and stage inferred this turn and that the answer only drives a literature search, no code. A file link is an addition, never the context. Do not start a depth search with an empty concern.
Abstract the problem class before any query (references/search.md). JOURNAL and EDA are context, not the answer list and not search keywords for the table’s proper name.
Run the matching search loop (references/search.md). Fetch primary pages. Follow up per promising extra if the first pass is thin or single-sourced.
Write scratch/research/ using the matching structure in references/search.md (survey: survey-<slug>.md; depth: <slug>.md with lanes). There is no templates/ directory in this skill — copy the markdown skeleton from that reference. Gitignored.
Return to the caller: scratch path and a one- or two-sentence finding. Name that candidates are laned (measure / declare / evaluate / confirm). Chat is path + those sentences only — no pasted headings, tables, or “Depth note — …” body. That is the complete user-facing deliverable even when a harness says to put the full answer in chat. If tools cannot search or write, stop there: still only path + sentences (name the intended scratch/research/ path). No hypotheses, diagnostics, planned-query bullets, or template headings in chat (that is the paste). Do not write data_analysis.md, the design note, or data/. The caller asks which extras to add.

Stop conditions

Do not drop, impute, or remove outliers. Do not change the split.
Do not pick the final learner or architecture.
Do not treat a single blog as ground truth; say when sources disagree. Thresholds need two independent sources.
Do not pixi add / uv add / env add. If code needs a library, name add-python-package and return.
Do not run api get as a substitute for literature (symbols still go through api get in the caller).
Missing skill from a caller → that caller one-line skips.
Do not copy Open questions / EDA findings onto the extras list without a source.
Do not search the dataset proper name, sklearn.datasets, a Kaggle slug, or “baseline pipeline”. Do not return learners / Pipeline steps as EDA extras.
Never answer from memory when search ran. Do not ask the user to go look something up.
Do not paste the scratch markdown into chat (no “Depth note —”, no survey body). Path + 1–2 sentences only. If tools cannot search or write, stop after that. A no-tools harness does not license pasting the note.

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.json8 KB
references/search.md6 KB

Frequently asked questions

What does the Research Ml Practice AI skill do?

Why use Research Ml Practice on TypingMind?

Because you install it once and use it with any model. Research Ml Practice 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 Research Ml Practice in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/probabl-ai/skore-skills/tree/main/skills/research-ml-practice. 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 Research Ml Practice?

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 Research Ml Practice?

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

Is the Research Ml Practice 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.

View all

Deslop

openclaw

OrganizationPopular

Diff-scoped AI-slop cleanup pass: strip comment slop, defensive-check slop, type-laundering, and style drift from the current branch diff before autoreview.

Discord Clawd

Use to talk to the Discord-backed OpenClaw agent/session; not for archive search.

Discrawl

Discord archive: search, sync freshness, DMs, summaries, TUI, repo/release work.

Gitcrawl

GitHub archive: issue/PR search, sync freshness, duplicate clusters, current GitHub handoff, and Gitcrawl repo work.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Research ML Practice

Human-facing prose

Sequence

Stop conditions

Bundled files

Frequently asked questions

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