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Pacsomatic Toolkit

Free

Streamline tumor-normal workflows with ease.

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Free · Opens the source repo

What Pacsomatic Toolkit does

The Pacsomatic Toolkit provides a comprehensive solution for managing matched tumor-normal workflows using the nf-core/pacsomatic pipeline. It is specifically designed to facilitate the validation of input parameters, generate compliant samplesheets, and prepare reproducible launch artifacts for Nextflow. The primary entrypoint, scripts/run_pacsomatic.py, serves as the central hub for these operations, ensuring that users can efficiently execute their workflows with minimal manual intervention.

This skill automates several critical tasks, including the validation of required identifiers and file paths, which helps prevent common errors that can occur during pipeline execution. It generates a pacsomatic-compatible samplesheet that includes essential information such as patient and sample identifiers, BAM file paths, and statuses. Additionally, it creates a YAML configuration file and a launch script tailored for reproducible reruns, which is crucial for maintaining consistency in computational analyses.

Users can invoke this skill to run tumor-normal analyses, generate or fix samplesheets, and execute workflows locally or on various schedulers like LSF, Slurm, PBS, or SGE. The toolkit also supports dry-run validation, allowing users to check for potential issues before actual execution, thereby reducing the likelihood of runtime errors. In case of execution failures, the skill provides detailed feedback to help troubleshoot the problem effectively.

This toolkit is ideal for bioinformaticians and researchers who need to manage complex genomic workflows efficiently. By utilizing the Pacsomatic Toolkit, users can focus on their analyses rather than the intricacies of workflow management, leading to more productive research outcomes.

When to use it

Use this skill when you need to validate inputs, generate samplesheets, or run tumor-normal analyses with nf-core/pacsomatic.

When not to use it

Avoid this skill for deep biological interpretation or when manual command construction is explicitly required.

What you can build with it

Running a Tumor-Normal Analysis

Invoke the skill to execute a matched tumor-normal analysis from provided BAM files, ensuring all inputs are validated.

Generating Samplesheets

Use the toolkit to create or fix pacsomatic-compatible samplesheets required for your analysis.

Performing Dry-Run Validation

Before executing a workflow, run a dry run to validate all inputs and ensure that the setup is correct.

How to install Pacsomatic Toolkit

View source

1. Install with the skills CLI

npx skills add k-dense-ai/scientific-agent-skills/pacsomatic --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by k-dense-ai

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:

python scripts/run_pacsomatic.py \
  --tumor-bam /path/to/tumor.bam \
  --normal-bam /path/to/normal.bam \
  --patient-id P001 \
  --tumor-sample-id P001_T \
  --normal-sample-id P001_N \
  --outdir /path/to/output \
  --genome GRCh38 \
  --profile singularity,sanger \
  --dry-run

Scheduler execution example (Slurm):

python scripts/run_pacsomatic.py \
  --tumor-bam /path/to/tumor.bam \
  --normal-bam /path/to/normal.bam \
  --patient-id P001 \
  --tumor-sample-id P001_T \
  --normal-sample-id P001_N \
  --outdir /path/to/output \
  --genome GRCh38 \
  --profile singularity,sanger \
  --executor slurm \
  --queue compute \
  --project my_account \
  --cpus 16 \
  --memory-gb 64 \
  --walltime 48:00 \
  --run

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:

python -m unittest discover -s tests/pacsomatic -v

References

  • references/agent-playbook.md
  • references/config-and-output.md
  • references/pacsomatic_guide.md
  • scripts/run_pacsomatic.py

Frequently asked questions about Pacsomatic Toolkit

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