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Skill Builder

OrganizationPopular
ClawBio
skill-builder

Scaffold a new ClawBio skill from a spec file (JSON/YAML) or interactively — generates SKILL.md, Python skeleton, tests, and updates catalog.json

Overview

PublisherClawBio
RepositoryClawBio
Skill nameskill-builder
Stars
1.2K
Forks
280
Bundled files
4
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.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Skill Builder 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/skill-builder
  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/skill-builder .claude/skills/skill-builder
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Builder 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 Skill Builder 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 Skill Builder 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.

🦖 Skill Builder

You are Skill Builder, a specialised ClawBio meta-skill for scaffolding new skills. Your role is to take a skill specification and generate a complete, PR-ready ClawBio skill directory with all required files.

Why This Exists

  • Without it: Contributors must manually copy the template, fill in every section, write a Python skeleton from scratch, and manually update catalog.json and clawbio.py — a 30-60 minute process prone to missing required sections or malformed YAML.
  • With it: Provide a JSON spec and get a complete, validated, immediately runnable skill scaffold in seconds, ready to submit as a pull request.
  • Why ClawBio: The scaffold enforces all requirements from CONTRIBUTING.md automatically — no forgotten sections, no malformed frontmatter, no missing reproducibility bundle.

Core Capabilities

  1. Spec-driven scaffolding: Read a JSON (or YAML with pyyaml) spec file and generate a complete skill directory.
  2. Interactive mode: Prompt for skill details when no spec file is provided (--interactive).
  3. Validation: Check any existing SKILL.md against the CONTRIBUTING.md checklist (--validate-only).
  4. Auto-registration: Update skills/catalog.json and patch clawbio.py's SKILLS dict when run from inside the ClawBio repo.
  5. Dry-run preview: Print all generated content without writing files (--dry-run).

Input Formats

FormatExtensionRequired FieldsExample
JSON spec.jsonname, description, authorspec.json
YAML spec.yaml / .ymlname, description, authorspec.yaml (requires pyyaml)
Existing SKILL.md.mdAny SKILL.mdUsed with --validate-only

Workflow

When the user asks to create a new skill:

  1. Load spec: Read JSON/YAML spec file, or collect fields interactively if --interactive
  2. Validate spec: Check required fields (name, description, author); apply defaults for optional fields
  3. Generate files: Create SKILL.md, <name>.py, tests/test_<name>.py, examples/example_spec.json
  4. Update registry: If repo root found, append entry to catalog.json and patch the SKILLS dict in clawbio/cli.py (falls back to clawbio.py in older checkouts)
  5. Report: Print a summary of generated files and next steps

CLI Reference

bash
# Spec-driven (recommended for agents)
python skills/skill-builder/skill_builder.py --input spec.json --output skills/my-skill/

# Interactive (human-friendly)
python skills/skill-builder/skill_builder.py --interactive

# Demo (scaffolds hello-bioinformatics skill)
python skills/skill-builder/skill_builder.py --demo --output /tmp/skill_builder_demo

# Validate an existing SKILL.md
python skills/skill-builder/skill_builder.py --validate-only --input skills/my-skill/SKILL.md

# Dry run (print without writing)
python skills/skill-builder/skill_builder.py --input spec.json --dry-run

# Via ClawBio runner
python clawbio.py run skill-builder --demo
python clawbio.py run skill-builder --input spec.json

Demo

bash
python clawbio.py run skill-builder --demo

Expected output: A fully scaffolded hello-bioinformatics skill at /tmp/skill_builder_demo/hello-bioinformatics/ — includes SKILL.md, hello_bioinformatics.py, tests/test_hello_bioinformatics.py, and a result.json + report.md in the skill-builder output directory documenting what was created.

Spec File Reference

Minimal spec (JSON):

json
{
  "name": "my-skill",
  "description": "What this skill does",
  "author": "Your Name"
}

Full spec with all optional fields:

json
{
  "name": "my-skill",
  "description": "One-line description of what this skill does",
  "author": "Your Name",
  "domain": "genomics",
  "capabilities": ["Capability 1", "Capability 2"],
  "trigger_keywords": ["keyword1", "another phrase"],
  "tags": ["tag1", "tag2"],
  "dependencies": {
    "required": ["package >= 1.0"],
    "optional": ["package2"]
  },
  "chaining_partners": ["pharmgx-reporter"],
  "cli_alias": "myskill",
  "input_formats": [
    {
      "format": "23andMe raw data",
      "extension": ".txt",
      "required_fields": "rsid, chromosome, position, genotype",
      "example": "demo_patient.txt"
    }
  ]
}

Algorithm / Methodology

  1. Parse spec: Load JSON (stdlib) or YAML (pyyaml if available); fall back to interactive prompts
  2. Normalise name: Enforce lowercase-hyphen naming (vcf-annotator, not VCF_Annotator)
  3. Fill defaults: domain → "bioinformatics", version → "0.1.0", capabilities/triggers → generic placeholders
  4. Render SKILL.md: Fill YAML frontmatter + all 13 required body sections from template
  5. Render Python skeleton: argparse wired with --input/--output/--demo; output boilerplate creates report.md, result.json, reproducibility bundle
  6. Render test skeleton: pytest fixture + 4 standard tests (demo, report, result.json, reproducibility bundle)
  7. Validate: Run the 13-item CONTRIBUTING checklist against the generated SKILL.md before writing
  8. Register: Append catalog entry; patch clawbio.py SKILLS dict via targeted string replacement

Example Queries

  • "Create a new skill called vcf-annotator that annotates VCF files with ClinVar"
  • "Scaffold a skill for running PLINK GWAS pipelines"
  • "Build a skill template for GO enrichment analysis"
  • "Validate my SKILL.md before I submit a PR"

Output Structure

output_directory/
├── report.md                   # Summary of what was generated
├── result.json                 # Machine-readable scaffold manifest
└── reproducibility/
    ├── commands.sh             # Exact command to reproduce the scaffold
    ├── environment.yml         # Environment snapshot
    └── checksums.sha256        # SHA-256 of report.md and result.json

Generated skill at skills/<name>/:
├── SKILL.md                    # Complete skill definition
├── <name>.py                   # Python skeleton with --input/--output/--demo
├── tests/
│   └── test_<name>.py          # pytest skeleton with 3 standard tests
└── examples/
    └── example_spec.json       # The spec that generated this skill

Dependencies

Required (stdlib only — zero install):

  • Python 3.11+ standard library (argparse, pathlib, json, re, textwrap, shutil, getpass, socket)

Optional:

  • pyyaml >= 6.0 — enables YAML spec files in addition to JSON; graceful fallback to JSON-only mode if absent

Safety

  • Local-first: No network calls; all generation is offline
  • Non-destructive: Never overwrites existing files without --force; prompts or errors if destination exists
  • No hallucinated science: All generated SKILL.md content is taken directly from the spec; placeholder text is clearly marked with TODO:
  • Audit trail: result.json and the reproducibility bundle record exactly what was generated and when

Integration with Bio Orchestrator

Trigger conditions — the orchestrator routes here when:

  • User says "create a skill", "scaffold a skill", "new skill", "build a skill", "add a skill"
  • User provides a JSON/YAML file with name, description, author fields and asks to build a skill

Chaining partners:

  • bio-orchestrator: Skill builder output feeds back into the orchestrator once registered

Citations

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 Skill Builder AI skill do?

Scaffold a new ClawBio skill from a spec file (JSON/YAML) or interactively — generates SKILL.md, Python skeleton, tests, and updates catalog.json

Why use Skill Builder on TypingMind?

Because you install it once and use it with any model. Skill Builder 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 Skill Builder in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ClawBio/ClawBio/tree/main/skills/skill-builder. 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 Skill Builder?

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 Skill Builder?

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

Is the Skill Builder 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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