Search Ml Literature logo

Search Ml Literature

Organization
probabl-ai
search-ml-literature

Search scientific and technical sources and write one idea file and one Ideas row for the direction the user confirms. Comes after review, beside the backlog. Do not call research-ml-practice. Do not write History, Backlog, or a design note. Do not pick a winner.

Overview

Publisherprobabl-ai
Repositoryskore-skills
Skill namesearch-ml-literature
Stars
144
Forks
9
Bundled files
1
LicenseBSD-3-Clause
Links
  • 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 Search Ml Literature AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/probabl-ai/skore-skills/tree/main/skills/search-ml-literature
  4. 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.
2

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/search-ml-literature .claude/skills/search-ml-literature
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Search Ml Literature 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 Search Ml Literature 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 Search Ml Literature 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.

Search ML Literature

Comes after review, beside the backlog. Source is a query, not an artifact the user already named. Show a few directions, then write one journal/ideas/<slug>.md and one Ideas row for the direction they confirm. manage-ml-backlog triages that file later.

Human-facing prose

Details: setup-workspace references/human_facing_prose.md. Directions and the pick question are scientific options for this project — not skill ids or the wrapper CLI.

Procedure

  1. Run python -m skore_skills status. Read journal/JOURNAL.md Status, the last History headline, and the EDA summary when data_analysis/data_analysis.md exists. If the query is too vague to search, ask once.
  2. Rewrite the query as a problem class before any search. Drop dataset names, loader names (sklearn.datasets, fetch_*), and Kaggle slugs. JOURNAL and EDA are context for that rewrite, not search strings.
  3. Search scientific and technical sources. Fetch the primary pages. When sources disagree, say so. Do not fill a gap from memory after a search has run. If search cannot run, say so and stop. Do not invent papers, URLs, or claims.
  4. Write scratch/research/direction-<slug>.md when that write can run. Do not paste it into chat.
  5. Show two to four directions. Each one: a one-line idea, why it might transfer here, what it would change, and the URL plus the claim. Do not rank a winner.
  6. AskUserQuestion: pick one, narrow the query, or stop. Narrow restarts at step 2. Stop writes no idea file.
  7. If this turn is not already an explicit yes, restate the picked direction in one short paragraph, then one single-choice AskUserQuestion: Yes / No. Do not also ask for a typed yes. Until Yes, that paragraph and the question are the whole message. "Maybe" is not Yes. No or Stop writes nothing.
  8. On Yes — the tool answer, or an explicit yes already given on this or a later turn — write journal/ideas/<slug>.md and upsert its Ideas row. Do not create a design note. Do not edit History, Backlog, or Status. Write journal/ideas/<slug>.md:
# <slug>
- Experiment: <last History stem, or n/a>
- Source: literature: <url>
- Triage: open
- Question:
- Why now:
- What changes:
- Open gaps:

Open gaps hold transfer risks, disagreements, and domain claims this dataset does not establish. A library named only by a source stays in Open gaps until the user asks to add it. Then upsert one ## Ideas row: Question as plain text, Status open, Experiment <last History stem, or n/a>, Source literature: <url>. If that table is missing, insert it between History and Backlog. Do not rewrite the other sections. Return the path to the caller.

Stop conditions

  • Do not load research-ml-practice.
  • Do not write History, Backlog, Status, or a design note. The Ideas table is the only JOURNAL.md edit.
  • Do not append a B<N> row.
  • Do not search a dataset proper name, a loader, or a Kaggle slug.
  • Do not add a package or run env add / pixi add.
  • Do not treat one blog as ground truth.

Bundled files

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

Frequently asked questions

What does the Search Ml Literature AI skill do?

Search scientific and technical sources and write one idea file and one Ideas row for the direction the user confirms. Comes after review, beside the backlog. Do not call research-ml-practice. Do not write History, Backlog, or a design note. Do not pick a winner.

Why use Search Ml Literature on TypingMind?

Because you install it once and use it with any model. Search Ml Literature 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 Search Ml Literature in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/probabl-ai/skore-skills/tree/main/skills/search-ml-literature. 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 Search Ml Literature?

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 Search Ml Literature?

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

Is the Search Ml Literature 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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