### Pacsomatic
Works with
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..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpacsomaticExecute the skills CLI command in your project's root directory to begin installation:
Fetches pacsomatic from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate pacsomatic. Access via /pacsomatic in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
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Save 3-5 hours per week on routine tasks
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Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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| 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 |
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.pyThe helper script:
patient,sample,status,bam,pbi)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.
Invoke this skill when the user asks to:
Do not use this skill for:
Typical trigger phrases:
scripts/run_pacsomatic.py for validation and artifact generation.--dry-run when the user asks for checks/validation only.--run only when the user asks to execute/submit..nextflow.log, pipeline_info, failing task logs).Required:
--fasta or --genomeOptional:
-r)--dry-run and/or --run--dry-run and not --run, stop after artifact generation.--run, execute locally or submit to scheduler.Every response after invocation should include:
dry-run vs run)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
Use config.yaml as the baseline for profile/executor/runtime defaults. Override at invocation time when user requirements differ.
Run unit tests from skill root:
python -m unittest discover -s tests -v
references/agent-playbook.mdreferences/config-and-output.mdreferences/pacsomatic_guide.mdscripts/run_pacsomatic.pyPrerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
K-Dense-AI/scientific-agent-skills
google-deepmind/science-skills
google-deepmind/science-skills
We added pacsomatic from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
We added pacsomatic from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for pacsomatic matched our evaluation — installs cleanly and behaves as described in the markdown.
pacsomatic has been reliable in day-to-day use. Documentation quality is above average for community skills.
pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.
Useful defaults in pacsomatic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Useful defaults in pacsomatic — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Registry listing for pacsomatic matched our evaluation — installs cleanly and behaves as described in the markdown.
pacsomatic reduced setup friction for our internal harness; good balance of opinion and flexibility.
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