القائمة
ClawBio wrapper around nf-core/sarek 3.8.1 covering mapping through annotation for germline, tumor-only, and somatic paired analyses.
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استنادا إلى تصنيف SOC المهني
| name | nfcore-sarek-wrapper |
| description | ClawBio wrapper around nf-core/sarek 3.8.1 covering mapping through annotation for germline, tumor-only, and somatic paired analyses. |
| license | MIT |
| metadata | {"version":"0.1.0","author":"ClawBio","domain":"genomics","tags":["sarek","variant-calling","germline","somatic","tumor-only","wes","wgs","nextflow","nf-core"],"inputs":[{"name":"samplesheet","type":"file","format":["csv","tsv","yaml","yml","json"],"description":"nf-core/sarek samplesheet. Accepted formats: .csv, .tsv, .yaml, .yml, .json. Always-required columns: patient, sample. Step-dependent columns: for mapping, lane plus one input mode — fastq_1 + fastq_2, OR spring_1 (+ optional spring_2), OR bam (uBAM); for markduplicates/ prepare_recalibration/variant_calling, bam+bai or cram+crai; for recalibrate, additionally table; for annotate, vcf (+ optional variantcaller). Optional metadata columns: sex (XX/XY/NA), status (0=normal, 1=tumor), contamination (varlociraptor). A row may not mix FASTQ, BAM/CRAM, and VCF input modes.\n","required":true}],"outputs":[{"name":"report","type":"file","format":["md"],"description":"Wrapper run summary, per-tool VCF inventory, and downstream handoff recommendations"},{"name":"result","type":"file","format":["json"],"description":"Structured result payload with detected CRAMs, per-tool VCFs, MultiQC HTML, and provenance"}],"dependencies":{"python":">=3.11","packages":null},"demo_data":[{"path":"demo/README.md","description":"Demo mode uses the upstream nf-core/sarek -profile test dataset rather than bundled FASTQs"}],"endpoints":{"cli":"python clawbio.py run sarek-pipeline --input {samplesheet} --output {output_dir}"},"openclaw":{"requires":{"bins":["python3","nextflow","java"],"env":null,"config":null},"always":false,"emoji":"🧬","homepage":"https://github.com/ClawBio/ClawBio","os":["darwin","linux"],"install":null,"trigger_keywords":["sarek","germline variant calling","somatic variant calling","tumor-normal pair","mutect2","strelka","haplotypecaller","ascat","WES variant calling","WGS variant calling","VEP annotation","SnpEff","GATK pipeline","nf-core","Nextflow"]}} |
You are nfcore-sarek-wrapper, a specialised ClawBio agent for germline, tumor-only, and somatic paired variant calling and annotation using nf-core/sarek 3.8.1.
Fire when:
nf-core/sarekDo NOT fire when:
nfcore-rnaseq-wrappernfcore-scrnaseq-wrapperclinical-variant-reporterwes-clinical-report-en or wes-clinical-report-esOne skill, one task: orchestrate nf-core/sarek 3.8.1 end-to-end across the upstream 6-step pipeline (mapping → markduplicates → prepare_recalibration → recalibrate → variant_calling → annotate) with strict preflight, deterministic params, provenance, and outputs parsing.
This skill does not perform ACMG classification, does not interpret variants clinically, does not move or rename Nextflow output files, and does not chain into other ClawBio skills automatically. Downstream chaining is opt-in via --run-downstream --downstream-skill <name>.
params.yaml, commands.sh, manifest.json, and a checksums bundle.nf-core/sarek 3.8.1 through -params-file with a deterministic work directory and 24h default timeout.commands.sh, params.yaml, manifest.json, checksums, environment.yml, and provenance JSON under reproducibility/.clinical-variant-reporter, wes-clinical-report-en, wes-clinical-report-es, omics-target-evidence-mapper, or clinical-trial-finder.--step | Inputs required | Best for |
|---|---|---|
mapping (default) | lane plus one of: fastq_1+fastq_2, spring_1(+ optional spring_2), or bam (uBAM) | Standard FASTQ/Spring/uBAM-to-VCF runs |
markduplicates | Aligned bam+bai or cram+crai | Restart from alignment |
prepare_recalibration | Deduplicated BAM/CRAM | Pre-BQSR restart |
recalibrate | BAM/CRAM + table | Restart at BQSR apply |
variant_calling | Recalibrated BAM/CRAM | Tool re-run without realignment |
annotate | vcf (+ optional variantcaller) | Annotate existing variant calls |
The samplesheet may be .csv, .tsv, .yaml, .yml, or .json (CSV/TSV are
delimited; YAML/JSON are a top-level list of row records). File-column values
must be local paths by default (local-first): remote URLs (https://, s3://,
gs://, ftp://, …) — and remote reference paths — are rejected at preflight
(REMOTE_INPUT_NOT_ALLOWED) unless you pass --allow-remote-inputs, which also
logs a runtime warning naming every path fetched over the network. (The public
iGenomes mirror base and the object-store --work-dir are not gated.)
| Mode | Required Fields | Example |
|---|---|---|
| Mapping (FASTQ) | patient, sample, lane, fastq_1, fastq_2 | samplesheet.csv |
| Mapping (Spring) | patient, sample, lane, spring_1 (+ optional spring_2) | samplesheet_spring.csv |
| Mapping (uBAM) | patient, sample, lane, bam | samplesheet_ubam.csv |
| BAM/CRAM restart | patient, sample, plus bam+bai or cram+crai | samplesheet_bam.csv |
| Recalibrate restart | above plus table | samplesheet_recal.csv |
| Annotate | patient, sample, vcf (+ optional variantcaller) | samplesheet_vcf.csv |
| Demo mode | none | python clawbio.py run sarek-pipeline --demo |
Optional columns (any step): sex (XX/XY/NA), status (0=normal, 1=tumor),
contamination (float 0–1; required by varlociraptor for tumor/somatic).
Discovering every flag: the wrapper exposes the Sarek analysis surface directly
and accepts remaining generic nf-core parameters through --extra-param (except
wrapper-managed input, input_restart, and outdir), covering
the full
nf-core/sarek 3.8.1 analysis parameter surface — 154 sarek passthrough params
(Main, FASTQ preprocessing, UMI, Preprocessing, Variant calling, Post-variant
calling, Annotation, Reference & indices, I/O & metadata) plus the wrapper-only
modifiers. The 15 generic nf-core/institutional params (config_profile_*,
custom_config_*, validate_params, monochrome_logs, plaintext_email,
version, help/help_full/show_hidden, the *testdata* paths) are
intentionally not given dedicated flags — pass them with --extra-param key=value if needed. python clawbio.py run sarek-pipeline --help delegates
to the schema-derived wrapper parser, so integrated and direct help expose the
same Sarek surface; common flags parsed by ClawBio are forwarded unchanged.
--input for mapping unless --demo or native input-free --build-only-index mode; for later steps validate an explicit sheet or Sarek's prior CSV handoff; validate --run-downstream requires --downstream-skill; merge --extra-param key=value pairs.--arm, --gpu, --spark-profile, --mutect-profile, and --demo (test) tokens.--step, check tool/skip_tools tokens, resolve reference paths (iGenomes or explicit FASTA+indices), probe Java/Nextflow/backend, detect resume drift if --resume is set.params.yaml from CLI flags + extras + step-dependent defaults; clear all reference flags when --demo is set.-params-file params.yaml, deterministic -work-dir, streamed stdout/stderr.reproducibility/, emit report.md, result.json, commands.sh, params.yaml, the normalized samplesheet, environment.yml, checksums.sha256, and seven JSON files (manifest.json, parameters.json, samplesheet.json, pipeline_source.json, tool_versions.json, outputs.json, compatibility_policy.json).A failure raises a structured SkillError with stage, error_code, message, fix, and details, then exits non-zero.
# Preflight only; no Nextflow execution
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_check --check \
--genome GATK.GRCh38 --tools haplotypecaller
# Demo mode using upstream -profile test
python clawbio.py run sarek-pipeline --demo --output /tmp/sarek_demo
# Germline WES with custom targeted reference resources
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_run \
--tools haplotypecaller,strelka \
--genome null --igenomes-ignore --fasta /refs/genome.fa \
--known-indels /refs/known_indels.vcf.gz \
--wes --intervals exome_targets.bed
# Somatic paired (tumor + normal in same patient) with Mutect2 + Strelka + Manta
python clawbio.py run sarek-pipeline \
--input samplesheet_paired.csv --output ./sarek_somatic \
--tools mutect2,strelka,manta,vep \
--genome GATK.GRCh38
# Tumor-only with Mutect2 + PON
python clawbio.py run sarek-pipeline \
--input samplesheet_tumor_only.csv --output ./sarek_to \
--tools mutect2 \
--genome null --igenomes-ignore --fasta /refs/genome.fa \
--known-indels /refs/known_indels.vcf.gz \
--pon /refs/pon.vcf.gz --pon-tbi /refs/pon.vcf.gz.tbi \
--germline-resource /refs/af-only.vcf.gz --germline-resource-tbi /refs/af-only.vcf.gz.tbi
# Explicit FASTA reference (non-default genome build)
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_run \
--genome null --igenomes-ignore \
--fasta /refs/genome.fa --fasta-fai /refs/genome.fa.fai --dict /refs/genome.dict \
--bwa /refs/bwa/
# ARM (Apple M-series, AWS Graviton) — composes -profile docker,arm64
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_arm \
--profile docker --arm --genome GATK.GRCh38
# Opt-in downstream handoff to clinical-variant-reporter
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_run \
--tools haplotypecaller,vep \
--genome GATK.GRCh38 \
--run-downstream --downstream-skill clinical-variant-reporter
# Wrapper runtime controls (parity with scrnaseq/rnaseq):
# --timeout-hours N wall-clock cap (default 24h; 0 disables for HPC/cloud)
# --work-dir PATH Nextflow work dir (local path or object-store URI; default <output>/upstream/work)
# --nextflow-config / -c / --config extra Nextflow config file(s), repeatable
# --allow-pipeline-version-override run a non-3.8.1 --pipeline-version at your own risk
# --allow-remote-inputs opt in to remote inputs/refs (default local-first)
python clawbio.py run sarek-pipeline \
--input samplesheet.csv --output ./sarek_run \
--genome GATK.GRCh38 --tools haplotypecaller \
--timeout-hours 0 --work-dir s3://my-bucket/sarek/work
python clawbio.py run sarek-pipeline --demo --output /tmp/sarek_demo
Expected output: upstream nf-core/sarek -profile test outputs (synthetic small dataset) under upstream/results/, plus the ClawBio reproducibility/ bundle (report.md, result.json, params/commands/samplesheet snapshots, provenance JSON, environment.yml, checksums.sha256).
Key methods:
--input samplesheet URI is first staged through nextflow fs cp
(the same URI backends used by Sarek), then validated and normalized locally.--input schema accepts it (the schema accepts .csv, .tsv, .yaml, .yml, .json).--genome <iGenomes> and optionally override individual reference files (or pass false for a resource that should not be used), or use --genome null --igenomes-ignore --fasta <reference> when no iGenomes reference files should be loaded. Optional FASTA indices and tool resources may be supplied in either mode when appropriate.--build-only-index mode, Sarek intentionally supplies an empty samplesheet channel: the wrapper does not require sample pairing or per-sample caller outputs, while it preserves upstream global resource guards (including BQSR guards on preprocessing start steps) and captures published reference outputs.ascat, msisensorpro, muse) still need at least one patient with both status=0 and status=1.GATK.GRCh38 PON still emits a recommendation to use a project-specific PON.--no-intervals; the upstream schema explicitly marks that combination unsupported.--snv-consensus-calling requires --normalize-vcfs, as enforced by the upstream workflow before post-variant processing.--use-gatk-spark markduplicates is incompatible with header/positional UMI dedup (--umi-in-read-header or --umi-location); it is fine with --umi-read-structure (fgbio consensus runs upstream).--ascat-genome, --ascat-alleles, and --ascat-loci (which supported iGenomes bundles can provide). With --wes, custom --ascat-alleles, --ascat-loci, --ascat-loci-gc, and --ascat-loci-rt resources are recommended; the wrapper warns because Sarek documents its iGenomes ASCAT resources as unsuitable for WES.output/ # the --output directory
├── .nextflow/ # Nextflow cache/history (framework-created; excluded from checksums)
├── .nextflow.log # Nextflow launch log (framework-created; excluded from checksums)
├── upstream/
│ ├── results/ # Nextflow --outdir
│ │ ├── csv/ # handoff CSVs (mapped/markduplicates/recalibrated/variantcalled); legacy fallback: preprocessing/csv/
│ │ ├── preprocessing/
│ │ │ ├── mapped/ # §1 aligned CRAMs (one per sample/lane)
│ │ │ ├── markduplicates/ # §2 deduplicated CRAMs
│ │ │ └── recalibrated/ # §3 BQSR-recalibrated CRAMs
│ │ ├── variant_calling/
│ │ │ ├── haplotypecaller/ # §4 germline VCFs
│ │ │ ├── mutect2/ # somatic / tumor-only VCFs
│ │ │ ├── strelka/ # somatic + germline VCFs
│ │ │ ├── manta/ # SV VCFs
│ │ │ ├── ascat/ # CNV / purity / ploidy
│ │ │ ├── controlfreec/ # CNV
│ │ │ ├── tiddit/ # SV
│ │ │ ├── bcftools/ # mpileup caller output
│ │ │ ├── msisensor2/ # MSI
│ │ │ └── msisensorpro/ # MSI (paired MSIsensorPro)
│ │ ├── annotation/<variantcaller>/<sample_or_pair>/ # §5 SnpEff/VEP/merge/bcftools/SnpSift annotated VCFs
│ │ ├── multiqc/ # §6 MultiQC HTML + data
│ │ ├── pipeline_info/
│ │ └── reports/
│ └── work/ # Nextflow work directory
└── reproducibility/ # all ClawBio artifacts land here
├── report.md # human-readable run summary
├── result.json # machine-readable run summary
├── samplesheet.valid.csv # or samplesheet.demo.csv in --demo mode
├── params.yaml
├── commands.sh
├── remap_paths.py
├── environment.yml
├── checksums.sha256
├── compatibility_policy.json
├── parameters.json
├── samplesheet.json
├── pipeline_source.json
├── tool_versions.json
├── outputs.json # omitted if outputs parsing was skipped
├── manifest.json
├── logs/ # Nextflow stdout.txt / stderr.txt (real runs only)
├── macos_docker.config # written only on macOS + docker backend
└── sarek_downstream_handoff.{sh,json} # written only when --run-downstream is set
The wrapper writes exactly two child directories under the output/ root: upstream/ (the Nextflow results/ tree plus its work/ directory) and reproducibility/ (every ClawBio artifact — report.md, result.json, the params/commands/samplesheet snapshots, the seven JSON provenance files, environment.yml, and checksums.sha256). Nextflow itself additionally writes its own hidden bookkeeping in the launch directory — .nextflow/ (cache/history) and .nextflow.log — because the wrapper runs Nextflow with cwd = output_dir so the relative input/outdir paths resolve; both are excluded from checksums.sha256 (.nextflow directory and any .log file are skipped). There is no separate top-level provenance/ or logs/ directory; all ClawBio bundle files are co-located in reproducibility/, including execution logs under reproducibility/logs/ and the macOS Docker compatibility config (reproducibility/macos_docker.config, macOS + docker only). The whole reproducibility/ tree is excluded from checksums.sha256, so execution logs never enter the manifest. The §1–§6 layout in outputs_parser.py corresponds to: §1 mapped, §2 markduplicates, §3 recalibrated, §4 per-tool variant calls, §5 annotation, §6 MultiQC.
Cross-machine / cross-OS portability. The bundle stores absolute data/reference paths (required by Nextflow) but ships a stdlib-only remap_paths.py to rebase them on any host: --old/--new rewrites samplesheet data paths, --refs-old/--refs-new rewrites reference/index paths in params.yaml (and commands.sh if any were added there), and --verify confirms every path resolves before replay. URIs (s3://, https://, …) and the false disable sentinel are preserved. All bundle files use POSIX paths and utf-8/\n, so macOS↔Linux replay is byte-stable. The recommended replay path is a self-contained bash commands.sh (no environment variable required): it self-anchors via BASH_SOURCE, pins the Nextflow engine with NXF_VER, and applies the macOS-only Docker config through a uname-gated -c reproducibility/macos_docker.config, so the same bundle replays identically on Linux and macOS.
Required
--wes --tools ascat with iGenomes but warns that its default ASCAT resources are not suited for WES. The wrapper preserves that warning; for production WES runs, use --genome null --igenomes-ignore and provide target-appropriate --ascat-genome, --ascat-alleles, --ascat-loci, --ascat-loci-gc, and --ascat-loci-rt.snpeff and merge require an effective snpeff_db: either pass --snpeff-db or use an iGenomes genome that supplies it. This is still required when downloading/building annotation caches because Sarek uses the database identifier to select/download the cache.GATK.GRCh38 PON is used; pass a technically matched --pon/--pon-tbi and appropriate --germline-resource for production runs.--use-gatk-spark markduplicates alongside header/positional UMIs (--umi-in-read-header or --umi-location) to "speed up dedup". Do not — MarkDuplicatesSpark cannot perform UMI-aware deduplication, so the wrapper rejects that combination (matching sarek). Note: --use-gatk-spark markduplicates IS compatible with --umi-read-structure, because fgbio collapses UMIs into consensus reads before deduplication.--genome null --igenomes-ignore --fasta <reference>; Sarek documents FASTA as the only required custom-reference file and can build missing indices. For a partial override, keep --genome <iGenomes> and pass only the replacement resource files, as described in the official usage guide.status column — there is no --tumor-only flag. The model will want to write an all-status=1 samplesheet and still ask for paired tools. Do not. The wrapper's preflight enforces each tool's supported modes against the samplesheet:
status=0 normal and a status=1 tumor under the same patient): ascat, msisensorpro, muse.lofreq, msisensor2.mutect2, sentieon_tnscope, controlfreec.manta, tiddit, cnvkit, freebayes, varlociraptor.mpileup. Germline or paired: strelka. WGS germline or paired only: indexcov (--wes does not execute it upstream).haplotypecaller, deepvariant, sentieon_haplotyper, sentieon_dnascope. These run on the normal sample of a tumor/normal pair as well as on standalone normals.haplotypecaller runs on the normal while mutect2 runs on the tumor/normal pair.--demo clears all reference flags. The model will want to combine --demo with --genome GATK.GRCh38 or --fasta. Do not. The --demo flag forces the upstream -profile test dataset, which ships its own tiny reference; the wrapper clears every reference-path flag (genome, igenomes_base, fasta, intervals, dbsnp, known_indels, known_snps, germline_resource, pon, ...) before they reach params.yaml and ignores --input.--resume is rejected when the manifest drifts. Changes to pipeline source, profile composition, step, aligner, tools, skip_tools, analysis_mode, wes, joint_germline, joint_mutect2, the complete emitted parameter checksum, reference fingerprints, or samplesheet checksum invalidate the Nextflow work directory. The wrapper refuses to resume in those cases — re-run with a fresh --output directory.arm64 profile implicitly enables Wave. The model will want to use --arm on offline / air-gapped machines. Do not without preparation. Sarek's arm64 profile sets wave.enabled=true + wave.freeze=true + wave.strategy='conda,container' so containers are rebuilt for arm via the upstream Wave service. If outbound traffic to wave.seqera.io is blocked, the run stalls. On offline Apple Silicon, instead build images once via Wave on a connected host, mirror to a local registry, and add a custom -c config pointing process.container to the mirror.--aligner sentieon-bwamem or --tools sentieon_haplotyper,sentieon_dnascope,sentieon_tnscope without setting SENTIEON_LICENSE (or SENTIEON_LICENSE_BASE64) first. Do not. Sarek does not vendor a Sentieon license; the upstream task fails late with a cryptic Sentieon binary error. Export SENTIEON_LICENSE=host:port (server) or SENTIEON_LICENSE_BASE64=... (offline) before invoking clawbio.py run sarek-pipeline, and document the license source in your run notes.-profile test data.REMOTE_INPUT_NOT_ALLOWED) unless --allow-remote-inputs is explicitly passed, which also logs a runtime warning naming every path fetched over the network. The iGenomes mirror base and the object-store --work-dir are not gated.--extra-param are passed through but tracked in provenance, while input, input_restart, and outdir remain wrapper-managed so normalized input and output provenance cannot be bypassed.--resume is rejected on manifest drift (pipeline source, profile, step, aligner, tools, skip_tools, analysis_mode, wes, joint_germline, joint_mutect2, effective params checksum, reference fingerprints, samplesheet checksum).ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.
Agent dispatches and explains; skill executes Nextflow. The agent must not invent variant-calling thresholds, fabricate reference paths, or override --resume drift errors. If a flag combination is rejected by preflight, the agent should explain the failure and suggest the corrected invocation — never bypass the check.
This skill emits a downstream handoff template (under reproducibility/sarek_downstream_handoff.sh) when invoked with --run-downstream --downstream-skill <name>. Supported partners:
clinical-variant-reporter: ACMG/AMP classification from VEP-annotated VCFs.wes-clinical-report-en: render an English-language WES clinical PDF from this run's markdown report.wes-clinical-report-es: render a Spanish-language WES clinical PDF from this run's markdown report.omics-target-evidence-mapper: aggregate target-level evidence across multi-omic sources using the gene list from annotated VCFs.clinical-trial-finder: match variants/genes to ClinicalTrials.gov and EUCTR trials.The handoff is opt-in. Without --run-downstream, no template is written and no follow-on skill is launched.
nf-core/sarek ships a new release._SAREK_PASSTHROUGH_PARAMS, regenerated preflight rules, and full test re-baselining. Before bumping the pinned version, audit nextflow.config, assets/schema_input.json, nextflow_schema.json, docs/output.md, and changed module configs.12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題.
End-to-end Proteina-Complexa design pipeline driver. Reach for this skill whenever the user wants to "design a binder", "design binders for X", "run complexa design", "de novo binder", "PDL1 binder", "TrkA binder", "design proteins for target", "protein binder design", "ligand binder", "design a small-molecule binder", "ATP-binding protein", "AME motif scaffolding", "scaffold a motif near a ligand", "motif + ligand design", "enzyme scaffolding", "flow matching protein design", "beam-search binder", "FK steering", "MCTS protein design", "refold with AF2", "refold with RF3", "refold with ESMFold", or wants success rates, interface pAE, scRMSD, or FoldSeek diversity from a single command. This is the scientific anchor of the skill set: it drives `complexa design <pipeline>` from target picking to manifest emission and tells the user how many designs passed.
Standalone evaluation of an existing PDB directory with Proteina-Complexa. Use this skill whenever the user wants to "evaluate PDB files", "re-fold these designs", "compute interface pAE", "compute i_pLDDT for a folder", "run AF2 / RF3 / ESMFold on my designs", "score binder candidates", "designability of this folder", "scRMSD for designs", "motif RMSD for these PDBs", "complexa analysis", "complexa evaluate from a PDB directory", "evaluate from pdb dir", or score third-party outputs (BindCraft, AlphaProteo, RFdiffusion, hand-curated decoys). It picks the correct `evaluate_*.yaml` config, wires `++dataset.pdb_dir` and the folding backend, runs `complexa analysis` (the evaluate → analyze chain), parses the result CSV, reports pass-rates against the right `result_type` thresholds, and emits a replayable `eval_manifest.json`. Reach for this skill before hand-rolling refolding scripts.
First-time setup, environment configuration, and model-weight installation for Proteina-Complexa. Reach for this skill whenever the user says "set up complexa", "install complexa", "configure my .env", "first-time setup", "what models do I have installed", "what's in my .env", "download model weights", "download Complexa / AF2 / RF3 / ProteinMPNN / LigandMPNN / ESM2 / ESMFold checkpoints", "preflight my GPU", "verify environment", "complexa init", "complexa download", "complexa download --status", "complexa validate env", or any time a fresh checkout needs to be made runnable. This is the first skill to run on a new clone — it drives `complexa init`, `complexa download`, and `complexa validate env` end-to-end, edits the required `.env` keys, picks the right runtime (UV vs Docker), and emits a replayable setup artifact.