Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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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.
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See how to installReview the instructions the agent will receive before you install.
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.
clawbio list/run/upload, Python APIs, profiles, outputs, or
replay metadata, read core-runner.INTENTS.json, read skill-authoring.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.
SKILL.md contracts; do not
invent a runnable alias for a spec-only skill.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.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.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.
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.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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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.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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