pacsomatic

K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026

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$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill pacsomatic
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summary

### Pacsomatic

  • name: "pacsomatic"
  • description: "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 r..."
skill.md
name
pacsomatic
description
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 triage execution failures. Triggers on requests to run pacsomatic, prepare launch commands/scripts, perform dry-run checks, or troubleshoot pipeline startup and scheduler submission errors.
license
MIT
metadata
version: "1.0" skill-author: Beifang Niu contributors: Haidong, Wenchao upstream-pipeline: https://github.com/nf-core/pacsomatic

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 -v

References

  • references/agent-playbook.md
  • references/config-and-output.md
  • references/pacsomatic_guide.md
  • scripts/run_pacsomatic.py
how to use pacsomatic

How to use pacsomatic on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add pacsomatic
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill pacsomatic

The skills CLI fetches pacsomatic from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/pacsomatic

Reload or restart Cursor to activate pacsomatic. Access the skill through slash commands (e.g., /pacsomatic) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.574 reviews
  • Li Reddy· Dec 28, 2024

    We added pacsomatic from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Chaitanya Patil· Dec 24, 2024

    We added pacsomatic from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Harper Lopez· Dec 24, 2024

    pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Li Sethi· Dec 20, 2024

    Registry listing for pacsomatic matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Li Garcia· Dec 16, 2024

    pacsomatic has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Alexander Garcia· Dec 12, 2024

    pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Li Harris· Nov 19, 2024

    Useful defaults in pacsomatic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Piyush G· Nov 15, 2024

    Useful defaults in pacsomatic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Harper Thomas· Nov 15, 2024

    Registry listing for pacsomatic matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Mei Robinson· Nov 11, 2024

    pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.

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