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效率与协作 · SKILL.md

core-runner

core-runner

【效率与协作】Operate the ClawBio installation, CLI, public Python runner, patient profiles, output contracts, and reproducibility bundles safely.

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技能说明

GitHub

name: core-runner description: "Operate the ClawBio installation, CLI, public Python runner, patient profiles, output contracts, and reproducibility bundles safely." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Core Runner

Use this sub-skill when a request concerns installing ClawBio, listing or running a registered skill, using the stable Python API, creating or reusing a patient profile, composing the full profile run, or interpreting run outputs and replay metadata. This is the execution and provenance layer, not the scientific method used by an individual domain skill.

Route by task

Operating procedure

  1. Confirm whether the caller has an installed package or a source checkout; do not assume the checkout-only skill tree is available in a wheel.
  2. For a direct single-skill run, select exactly one input mode: --demo, --input, or a patient --profile. Choose a fresh, writable output directory for durable artifacts.
  3. For personal genotype data, prefer clawbio upload --input ... once, then reuse the generated profile. Verify the profile path and checksum before a chain of analyses.
  4. Treat run_skill(...) as a structured boundary: inspect success, exit_code, stderr, output_dir, and files; then inspect the promoted result.json/report.md fields when present. Never infer scientific findings from an exit code alone.
  5. Require the output contract appropriate to the selected skill. The common contract is a report plus structured result; reproducibility files are skill-dependent rather than guaranteed for every run.
  6. On failure, preserve the returned error and fix the stated input, profile, output, dependency, or timeout issue. Do not bypass the runner's per-skill extra-flag allowlist or pass raw patient data to network services.

Stable contracts

The public package exports run_skill, list_skills, upload_profile, and __version__. list_skills() prints a human-readable registry and returns the registry mapping. upload_profile(...) creates a JSON PatientProfile with parsed genotypes, source metadata, and a SHA-256 checksum. run_skill(...) launches the selected registered skill and returns a structured result; the virtual full-profile skill runs the configured genotype-consuming chain and writes pipeline_summary.json.

Input paths are made absolute before subprocess execution. A profile can supply the original input path when it is still present; a missing stored input does not recreate the source data. Relative output paths resolve from the caller's current working directory. Existing directories are reusable, but an existing file at the output path or an uncreatable directory is a preflight failure. Use a fresh directory when comparing replayed artifacts.

Keep these boundaries clear:

  • Domain-routing skills own scientific interpretation and method choice.
  • Pipeline/integration skills own network services and Nextflow behavior.
  • Skill-authoring rules own how new domain skills are written.
  • This sub-skill owns invocation, state transfer, output/provenance inspection, and safe failure handling.
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