Skip to content
cccskills
FreePublished on GitHub

clawbio

Use ClawBio for local-first bioinformatics agent workflows: install and run its skill library, route genomic or omics inputs, create reproducible reports, connect optional MCP or Nextflow integrations, and author or validate skills.

The English summary is being prepared. Showing the original description.

See how to install

Files included (32)

  • SKILL.md4.3 KB
  • references/repo-provenance.md1.6 KB
  • references/repo-routing-metadata.json513 B
  • references/troubleshooting.md3.4 KB
  • scripts/check_environment.py3.7 KB
  • sub-skills/core-runner/references/api-reference.md5.5 KB
  • sub-skills/core-runner/references/cli-reference.md5.5 KB
  • sub-skills/core-runner/references/output-and-reproducibility.md5.2 KB
  • sub-skills/core-runner/references/troubleshooting.md5.7 KB
  • sub-skills/core-runner/scripts/check_output_contract.py6.4 KB
  • sub-skills/core-runner/SKILL.md3.9 KB
  • sub-skills/domain-routing/references/catalog-and-routing.md7.1 KB
  • sub-skills/domain-routing/references/domain-families.md8.7 KB
  • sub-skills/domain-routing/references/troubleshooting.md5.3 KB
  • sub-skills/domain-routing/scripts/catalog_query.py8.2 KB
  • sub-skills/domain-routing/SKILL.md5.9 KB
  • sub-skills/pipelines-integrations/references/integrations.md7.9 KB
  • sub-skills/pipelines-integrations/references/pipeline-preflight.md9.1 KB
  • sub-skills/pipelines-integrations/references/troubleshooting.md8.4 KB
  • sub-skills/pipelines-integrations/scripts/check_integrations.py5.2 KB
  • sub-skills/pipelines-integrations/SKILL.md4.9 KB
  • sub-skills/skill-authoring/references/authoring.md7.3 KB
  • sub-skills/skill-authoring/references/intent-descriptors.md5.0 KB
  • sub-skills/skill-authoring/references/troubleshooting.md5.8 KB
  • sub-skills/skill-authoring/references/validation-and-packaging.md5.4 KB
  • sub-skills/skill-authoring/scripts/check_skill_assets.py29.8 KB
  • sub-skills/skill-authoring/SKILL.md3.6 KB
  • sub-skills/validation-safety/references/safety-and-privacy.md6.5 KB
  • sub-skills/validation-safety/references/troubleshooting.md7.8 KB
  • sub-skills/validation-safety/references/validation-contracts.md9.3 KB
  • sub-skills/validation-safety/scripts/validate_runtime.py7.8 KB
  • sub-skills/validation-safety/SKILL.md2.6 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

ClawBio

ClawBio is a Python package and skill library for reproducible, local-first bioinformatics workflows. Use this repo skill to operate the platform and choose its specialist skills; do not improvise scientific thresholds or treat an output as a clinical diagnosis.

Start here

  • For install, clawbio list/run/upload, Python APIs, profiles, outputs, or replay metadata, read core-runner.
  • For a natural-language biological request, an input file, or a multi-step chain, read domain-routing before choosing an alias.
  • For MCP, messaging/web adapters, provider credentials, or nf-core/Nextflow, read pipelines-integrations.
  • For creating or changing a skill, catalog registration, tests, demos, or INTENTS.json, read skill-authoring.
  • For replay/checksum review, benchmark status, action contracts, privacy, or security filtering, read validation-safety.
  • Read troubleshooting when the failure spans more than one route. Use provenance before deciding whether this graph is stale for a repository checkout. For a read-only package/API/CLI probe, run check_environment.py after reading its help.

Public setup

For ordinary users, install the public distribution with Python 3.11 or newer:

python -m pip install clawbio
python -c "import clawbio; print(clawbio.__version__)"
clawbio list

The optional MCP extra is clawbio[mcp]. It is deliberately separate from the base install. A source checkout may use the repository's documented uv sync workflow, but a published skill must not assume that checkout exists.

Platform operating rules

  1. Identify the goal, input format/build, desired artifact, and whether the request is demo, local-file, or profile based. Inspect small headers locally; do not upload raw genomic data just to determine its type.
  2. Route to the narrowest catalog entry. Catalog metadata distinguishes the registered CLI aliases from agent-readable-only SKILL.md contracts; do not invent a runnable alias for a spec-only skill.
  3. Execute registered skills through clawbio run or the stable Python runner, not by bypassing the runner's per-skill flag allowlist. Use a fresh writable output directory and inspect structured results as well as exit codes.
  4. Preserve the local-first boundary. External APIs, cloud datasets, MCP local file access, bots, credentials, containers, Nextflow, and large references are explicit opt-ins with separate failure and provenance handling.
  5. Keep PASS, SKIP, FAIL, stale/expired action, and unavailable-backend states distinct. A successful import or demo does not prove a scientific method, external service, GPU, or clinical interpretation.
  6. Reports must retain the package's research/education disclaimer. Do not give medical diagnoses or treatment advice from a ClawBio result.

Common entry points

clawbio list
clawbio run <registered-alias> --demo
clawbio run <registered-alias> --input sample.ext --output results
clawbio upload --input genotype.txt --patient-id PT001
clawbio run profile --profile profiles/PT001.json --output profile-results
clawbio mcp

The exact flags and virtual full-profile behavior belong to core-runner. Use domain-routing for aliases and chains, not this short list as a substitute for input inspection.

Freshness and scope

This graph is versioned against the source snapshot in repo-provenance.md. If the package version, public entry points, catalog, or major evidence paths changed, refresh the repo skill before relying on detailed claims. Review-only test cases and reports are kept outside this runtime directory; they are not inputs to ordinary ClawBio runs.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

3ddfa

Free

Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

acme

Free

Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.

The English summary is being prepared. Showing the original description.

VectorSpaceLab/AREX-Skill339Updated Sep 3, 2026

See all skills by VectorSpaceLab

Report a problem with this skill