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Check whether public NGS tools and packages already exist before downloading, installing, or running a sequencing pipeline.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | ngs-runtime-env |
| description | Check whether public NGS tools and packages already exist before downloading, installing, or running a sequencing pipeline. |
Use this skill whenever an NGS workflow needs package checks, install planning, or runtime validation.
PATH with command -v or shutil.which.conda list, mamba list, micromamba list, or pip show.Do not modify system Python. Prefer isolated conda/mamba environments or containers.
From the repo root:
python plugins/ngs-analysis/scripts/ngs_preflight.py --list
python plugins/ngs-analysis/scripts/ngs_preflight.py --tool fastqc --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --profile local_light --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline dna_variant_calling --network-checks --emit-install-plan
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline shotgun_metagenomics --manager micromamba --install-plan-outdir runtime_readiness/shotgun_install
Use --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.
Check reference and database bundle readiness separately from executable readiness:
python plugins/ngs-analysis/scripts/ngs_reference_manager.py list
python plugins/ngs-analysis/scripts/ngs_reference_manager.py check --kind reference --bundle grch38_core --root /refs/GRCh38
python plugins/ngs-analysis/scripts/ngs_reference_manager.py explain-missing --kind database --bundle kraken2_standard --root /db/kraken2/standard
python plugins/ngs-analysis/scripts/ngs_reference_manager.py plan --pipeline shotgun_metagenomics --include-optional --outdir resource_readiness/shotgun
python plugins/ngs-analysis/scripts/ngs_reference_manager.py setup-plan --pipeline shotgun_metagenomics --include-optional --outdir resource_readiness/shotgun_setup
python plugins/ngs-analysis/scripts/ngs_reference_manager.py plan --pipeline atacseq --genome-build GRCh38 --bundle-root grch38_core=/refs/GRCh38 --outdir resource_readiness/atac
python plugins/ngs-analysis/scripts/ngs_reference_manager.py inventory --outdir resource_readiness/inventory
python plugins/ngs-analysis/scripts/ngs_reference_manager.py lock --outdir resource_readiness/lock --include-checksums
python 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
python plugins/ngs-analysis/scripts/ngs_reference_manager.py check-all --kind database --output resource_readiness/database_audit.json
Use 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.
Use 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.
Use 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.
Use 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.
The nf-core adapter performs the same resource gate automatically unless --skip-resource-plan is supplied:
python 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
The 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.
Use --install-missing --yes only after explicit user approval:
python plugins/ngs-analysis/scripts/ngs_preflight.py --pipeline fastq_qc --manager mamba --install-missing --yes
Prefer these patterns:
nextflow; use Docker/Singularity/Apptainer profiles for process tools.snakemake; use mamba or micromamba environments and avoid containers by default.mamba or micromamba from conda-forge and bioconda.--bundle-root bundle=/path or the registry root_env variables so downstream runs can cite the exact local bundle roots.Summarize:
Use this skill when implementing a Figma design as code (design → code) — the read-FROM-Figma direction. Triggers: 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'code this up from Figma', 'design to code', or a figma.com design URL provided alongside a codebase. Encodes the workflow for reading a node out of Figma with get_design_context and adapting its reference output to the target project — reusing existing components, tokens, and conventions, and honoring Code Connect mappings, component docs, design annotations, and design tokens by priority. Complements figma-code-connect (component mapping) and figma-generate-design / figma-use (the reverse, code → design direction).
Use when Codex is already in the attack-path-analysis phase of a security scan or the user explicitly asks to trace a security finding from source to sink and calibrate severity. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.
Use when the user asks for a deep, exhaustive, multi-pass, or variance-reducing repository-wide or scoped-path Codex Security scan. Run repeated independent discovery passes over one resolved scope with worker-specific threat models, semantically merge candidates, synthesize one canonical validation threat model, then run validation, attack-path analysis, canonical JSON completion, and generated reporting once. Do not use for PRs, commits, branch diffs, or working-tree diffs.
Use when Codex is already in the finding-discovery phase of a security scan or the user explicitly asks to discover candidate security findings in a repository or code change. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.
Use when the user explicitly asks to fix and verify a validated or plausible security finding. Do not use as the primary trigger for full PR, commit, branch, patch, or repository scans.
Develop evidence-backed structural and architectural security hardening proposals from vulnerability disclosures, supplied findings, incident or assessment documents, source code, or a completed Codex Security scan. Use when a user asks for systemic improvements, alternatives beyond per-finding patches, before-and-after security architecture views, engineering tradeoff analysis, or an implementation-ready plan for a selected hardening option. Also use automatically after a Codex Security scan with reportable findings when the top-level scan workflow requests final-report hardening guidance.