pacsomatic
Operator toolkit for nf-core/pacsomatic matched tumor-normal workflows from BAM inputs. Use this skill when the user needs to validate run inputs, generate pacsomatic-compliant samplesheets, prepare reproducible Nextflow launch artifacts, run locally or submit to schedulers (LSF/Slurm/PBS/SGE), and
By k-dense-ai · 966 installs
npx skills add k-dense-ai/scientific-agent-skills --skill pacsomatic
Source repository · Upstream listing
pacsomatic
Overview
This skill provides a reproducible execution workflow for nf core/pacsomatic, centered on a single helper entrypoint that handles validation, artifact generation, and optional execution.
Primary entrypoint:
scripts/run pacsomatic.py
The helper script:
validates required identifiers, files, reference mode, and runtime prerequisites
writes a pacsomatic compatible samplesheet ( patient,sample,status,bam,pbi )
generates a params YAML and launch script for reproducible reruns
supports dry run validation and run/submit execution paths
Use this skill as the default path for pacsomatic operations. Do not bypass it with manually assembled nextflow run nf core/pacsomatic commands unless the user explicitly asks for manual command construction.
When to Use This Skill
Invoke this skill when the user asks to:
run matched tumor normal analysis from BAM files
generate or fix pacsomatic samplesheet and launch artifacts
execute locally or submit to schedulers (LSF/Slurm/PBS/SGE)
perform dry run validation before execution
troubleshoot launch failures or summarize run outputs
Do not use this skill for:
deep biological interpretation beyond run level sanity checks
editing pipeline internals unless explicitly requested
Typical trigger phrases:
"run nf core/pacsomatic for this tumor normal pair"
"prepare pacsomatic samplesheet and launch script"
"do a dry run first and tell me what is missing"
"submit pacsomatic to slurm/lsf and return the job id"
"why did pacsomatic submission fail"
Routing and Execution Rules
1. Always collect required run inputs first.
2. Always route through scripts/run pacsomatic.py for validation and artifact generation.
3. Default to dry run when the user asks for checks/validation only.
4. Use run only when the user asks to execute/submit.
5. For scheduler modes, include executor specific resource arguments and return detected job ID when available.
6. If execution fails, report first failure point and next triage target ( .nextflow.log , pipeline info , failing task logs).
Inputs Required
Required:
tumor BAM path
normal BAM path
patient ID
tumor sample ID
normal sample ID
output directory
exactly one reference mode: fasta or genome
Optional:
profile, resources, scheduler account/queue
pipeline version ( r )
params file, resume/report/dag flags
dry run and/or run
Workflow
1. Validate identity and input constraints.
2. Validate required local paths (BAM, optional PBI, optional FASTA).
3. Resolve runtime and dependency checks.
4. Build samplesheet and generated params YAML.
5. Generate launch script for selected executor.
6. If dry run and not run , stop after artifact generation.
7. If run , execute locally or submit to scheduler.
8. Return command/script path, validation status, and job ID (if detected).
Agent Response Contract
Every response after invocation should include:
exact command used or generated script path
confirmation that validation checks ran
run type ( dry run vs run )
scheduler job ID when available
one concrete next step for validation/triage
Quick Start
Dry run:
Scheduler execution example (Slurm):
Configuration
Use config.yaml as the baseline for profile/executor/runtime defaults. Override at invocation time when user requirements differ.
Testing
Run unit tests from skill root:
References
references/agent playbook.md
references/config and output.md
references/pacsomatic guide.md
scripts/run pacsomatic.py