Agent skill

bio-workflows-outbreak-pipeline

End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeships scriptsNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill outbreak-pipeline --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 3
SKILL.md size: 11 KB
Bundled scripts: yes
Path: skills/bioskills/outbreak-pipeline/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

## Version Compatibility Reference examples tested with: AMRFinderPlus 3.12+, BioPython 1.83+, IQ-TREE 2.2+, Nextclade 3.3+, TreeTime 0.11+, matplotlib 3.8+, mlst 2.23+, pandas 2.2+, scanpy 1.10+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters - CLI: `<tool> --version` then `<tool> --help` to confirm flags If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Outbreak Pipeline **"Characterize a pathogen outbreak from my isolate sequences"** → Orchestrate MLST typing, SNP phylogeny, TreeTime time-scaled tree construction, TransPhylo transmission inference, AMR profiling, and variant surveillance for genomic epidemiology. Complete workflow for genomic epidemiology: from pathogen isolates to transmission networks and outbreak characterization. ## Workflow Overview ``` Pathogen Isolate Genomes (FASTA/FASTQ) | v +---------+---------+ | | v v [1a. MLST Typing] [1b. AMR Detection] <-- Parallel execut

What's inside
Steps it walks through
  1. Version Compatibility
  2. Workflow Overview
  3. Prerequisites
  4. Primary Path: Bacterial Outbreak Investigation
  5. Step 1a: MLST Typing (Parallel)
  6. Step 1b: AMR Detection (Parallel)
  7. Step 2: Core Genome Alignment
  8. Step 3: Phylodynamics with TreeTime
  9. Step 4: Transmission Inference with TransPhylo
  10. Python Alternative: TransPhylo via rpy2
  11. Visualization: Outbreak Timeline
  12. Parameter Recommendations
  13. Troubleshooting
  14. Output Files
Ships with 2 files
  • examples/outbreak_workflow.sh
  • usage-guide.md
Commands it runs
conda install -c bioconda mlst abricate snippy iqtree fasttree
pip install treetime transphylo biopython pandas matplotlib
R packages for TransPhylo
Rscript -e "install.packages('TransPhylo')"
mkdir -p ${OUTDIR}/{mlst,amr,alignment,phylo,transmission}
Run MLST on all isolates
echo "=== MLST Typing ==="
for fasta in $ISOLATES; do
mlst $fasta > ${OUTDIR}/mlst/${sample}.mlst.txt
done
More from awesome-bio-agent-skills
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About this skill
What does the bio-workflows-outbreak-pipeline skill do?

End-to-end outbreak investigation from pathogen isolates to transmission networks. Orchestrates MLST typing, AMR surveillance, phylodynamic dating, and transmission inference with TransPhylo. Use when investigating disease outbreaks or tracking pathogen transmission chains.

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill outbreak-pipeline --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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