Update to Life Sciences NGS Analysis

{
  "name": "ngs-runtime-env",
  "description": "Check whether public NGS tools and packages already exist before downloading, installing, or running a sequencing pipeline.",
  "included_files": [
    {
      "relative_path": "agents/openai.yaml",
      "size_in_bytes": 262
    }
  ],
  "skill_md_contents": "---\nname: ngs-runtime-env\ndescription: Check whether public NGS tools and packages already exist before downloading, installing, or running a sequencing pipeline.\n---\n\n# NGS Runtime Environment\n\nUse this skill whenever an NGS workflow needs package checks, install planning, or runtime validation.\n\n## Existence Check Order\n\n1. Check executables on `PATH` with `command -v` or `shutil.which`.\n2. Check Python imports for Python-backed tools.\n3. Check active package managers with `conda list`, `mamba list`, `micromamba list`, or `pip show`.\n4. If requested, check package indexes or container registries.\n5. Emit an install plan before installing.\n6. Install only when explicitly requested by the user.\n\nDo not modify system Python. Prefer isolated conda/mamba environments or containers.\n\n## Script\n\nFrom the repo root:\n\n```bash\npython plugins/ngs-analysis/scripts/ngs_preflight.py --list\npython plugins/ngs-analysis/scripts/ngs_preflight.py --tool fastqc --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_variant_calling --network-checks --emit-install-plan\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline shotgun_metagenomics --manager micromamba --install-plan-outdir runtime_readiness/shotgun_install\n```\n\nUse `--install-plan-outdir` when a user needs a reviewable permission handoff. It writes `install_plan.json` as the canonical machine-readable plan and `install_commands.sh` as a guarded shell companion generated from the same plan. The shell companion is review-only by default; it exits without installing unless `NGS_RUN_INSTALL_COMMANDS=1` is set after explicit user approval.\n\nCheck reference and database bundle readiness separately from executable readiness:\n\n```bash\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py list\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py check --kind reference --bundle grch38_core --root /refs/GRCh38\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py explain-missing --kind database --bundle kraken2_standard --root /db/kraken2/standard\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py plan --pipeline shotgun_metagenomics --include-optional --outdir resource_readiness/shotgun\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py setup-plan --pipeline shotgun_metagenomics --include-optional --outdir resource_readiness/shotgun_setup\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py plan --pipeline atacseq --genome-build GRCh38 --bundle-root grch38_core=/refs/GRCh38 --outdir resource_readiness/atac\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py inventory --outdir resource_readiness/inventory\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py lock --outdir resource_readiness/lock --include-checksums\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py verify-lock --lockfile resource_readiness/lock/resource_lock.json --outdir resource_readiness/lock_verify --fail-on-mismatch\npython plugins/ngs-analysis/scripts/ngs_reference_manager.py check-all --kind database --output resource_readiness/database_audit.json\n```\n\nUse `plan` before claiming that a reference- or database-heavy workflow is runnable. The plan output writes `resource_plan.json`, `resource_manifest.tsv`, `resource_env.sh`, `resource_readiness.md`, and setup-plan artifacts; missing required bundles are blocking, while optional bundles such as Bracken/HUMAnN or HOMER motif resources should stay explicit.\n\nUse `setup-plan` when the user needs an actionable resource/database setup checklist without running an assay. It writes `resource_setup_plan.json`, `resource_setup_plan.tsv`, `resource_setup_plan.md`, and `resource_setup_commands.sh`. The shell skeleton keeps setup hints commented by default, so large reference/database downloads remain deliberate and reviewable.\n\nUse `inventory` when the user needs a broader resource/database audit across the plugin. It writes `resource_inventory.json`, `resource_inventory.tsv`, `resource_env.sh`, and `resource_dashboard.md`, including missing files, env vars, setup hints, license notes, and pipeline usage for every known bundle.\n\nUse `lock` after resources are ready for a project or handoff. It snapshots the resource inventory into `resource_lock.json`, `resource_lock.tsv`, and `resource_lock.md`; `verify-lock` compares the lockfile against current local paths and writes a drift report before reruns.\n\nThe nf-core adapter performs the same resource gate automatically unless `--skip-resource-plan` is supplied:\n\n```bash\npython plugins/ngs-analysis/scripts/run_nfcore_pipeline.py --pipeline taxprofiler --sample-sheet samples.csv --profile docker --bundle-root kraken2_standard=/db/kraken2/standard --include-optional-resources\n```\n\nThe direct bulk RNA-seq counts/QC, scRNA FASTQ-to-count, generic DNA, germline DNA, somatic DNA, UMI panel, ATAC, ChIP/CUT&RUN, amplicon, and shotgun backend runners also emit run-local `resources/` readiness bundles. These direct runners use advisory resource checks by default so custom or reduced local inputs can still be planned; add `--require-resource-plan` when missing registered bundles should block readiness.\n\nUse `--install-missing --yes` only after explicit user approval:\n\n```bash\npython plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline fastq_qc --manager mamba --install-missing --yes\n```\n\n## Install Strategy\n\nPrefer these patterns:\n\n- nf-core workflows: install/check `nextflow`; use Docker/Singularity/Apptainer profiles for process tools.\n- local execution: install/check `snakemake`; use `mamba` or `micromamba` environments and avoid containers by default.\n- small QC tools: install with `mamba` or `micromamba` from `conda-forge` and `bioconda`.\n- Python analysis packages: install in a dedicated environment, not global Python.\n- large databases and references: estimate size and check existing paths before downloading.\n- pipeline resource plans: use `--bundle-root bundle=/path` or the registry `root_env` variables so downstream runs can cite the exact local bundle roots.\n\n## Report\n\nSummarize:\n\n- present tools and paths\n- missing tools\n- package-index checks, if performed\n- suggested install commands\n- tools that are proprietary, EULA-bound, cloud-bound, or database-heavy\n"
}