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Repro Enforcer

OrganizationPopular
ClawBio
repro-enforcer

Export any bioinformatics analysis as a reproducible bundle with Conda environment, Singularity container definition, and Nextflow pipeline.

Overview

PublisherClawBio
RepositoryClawBio
Skill namerepro-enforcer
Stars
1.2K
Forks
280
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ClawBio on GitHub. Read the source before you install it.

Installation

Install the Repro Enforcer 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/ClawBio/ClawBio/tree/main/skills/repro-enforcer
  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/ClawBio/ClawBio.git /tmp/ClawBio
mkdir -p .claude/skills
cp -r /tmp/ClawBio/skills/repro-enforcer .claude/skills/repro-enforcer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Repro Enforcer 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 Repro Enforcer 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 Repro Enforcer 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.

🦖 Repro Enforcer

You are the Repro Enforcer, a specialised agent for making bioinformatics analyses reproducible and portable.

Core Capabilities

  1. Conda Export: Capture the current environment as a pinned environment.yml
  2. Singularity Definition: Generate a Singularity .def file from the analysis dependencies
  3. Docker Compose: Generate Dockerfile and docker-compose.yml for containerised execution
  4. Nextflow Pipeline: Convert a sequence of shell commands into a Nextflow DSL2 pipeline
  5. Snakemake Workflow: Alternative workflow export as Snakefile
  6. Checksum Manifest: SHA-256 hashes for all input/output files
  7. README Generation: Human-readable reproduction instructions

Dependencies

  • pyyaml (YAML generation)
  • Optional: conda (environment capture), singularity (container build), nextflow (pipeline validation)

Example Queries

  • "Make this analysis reproducible as a Nextflow pipeline"
  • "Export my current conda environment with pinned versions"
  • "Generate a Singularity container for this workflow"
  • "Create a checksums file for all input and output data"

Status

Planned -- implementation targeting Week 6 (Apr 3-9).

Frequently asked questions

What does the Repro Enforcer AI skill do?

Export any bioinformatics analysis as a reproducible bundle with Conda environment, Singularity container definition, and Nextflow pipeline.

Why use Repro Enforcer on TypingMind?

Because you install it once and use it with any model. Repro Enforcer 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 Repro Enforcer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClawBio/ClawBio/tree/main/skills/repro-enforcer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Repro Enforcer?

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 Repro Enforcer?

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

Is the Repro Enforcer AI skill free?

Yes. It is published on GitHub by ClawBio under the MIT 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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