Agent skill · Data & Analytics

bio-metagenomics-amr-detection

Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.

FreedomIntelligencegithub.com/FreedomIntelligenceGitHub ↗
claude-codeships scripts
Install
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-amr-detection --agent claude-code

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

Facts
Files in the skill folder: 4
SKILL.md size: 7 KB
Bundled scripts: yes
Path: skills/bio-metagenomics-amr-detection/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 2,909
Language: Python
Read our review of the source →

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+, pandas 2.2+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures - 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. # AMR Detection **"Screen my isolates for antibiotic resistance genes"** → Identify antimicrobial resistance determinants in bacterial genomes or metagenomes by searching against curated resistance gene databases. - CLI: `amrfinder -n assembly.fasta --organism Escherichia` (AMRFinderPlus) - CLI: ResFinder, CARD/RGI for alternative database searches Identify antimicrobial resistance (AMR) genes in bacterial genomes and metagenomes. ## Tool Comparison | Tool | Database | Best For | |------|----------|----------| | AMRFinderPlus | NCBI | Comprehensive, curated | | ResFinder | CGE | Clinical isolates | | CARD/RGI | CARD | Detailed resistance mechanisms | | ABRicate | Multiple | Quick screening | ## AMRFinderPlus (NCBI) #

What's inside
Steps it walks through
  1. Version Compatibility
  2. Tool Comparison
  3. AMRFinderPlus (NCBI)
  4. Installation
  5. From Nucleotide Sequences
  6. From Protein Sequences
  7. Output Fields
  8. Batch Processing
  9. ResFinder
  10. Run ResFinder
  11. From Raw Reads (KMA)
  12. CARD/RGI
  13. Run RGI
  14. RGI Output
Ships with 3 files
  • examples/batch_amr_screening.py
  • examples/run_amrfinder.sh
  • usage-guide.md
Commands it runs
conda install -c bioconda ncbi-amrfinderplus
amrfinder -u  # Update database
Assembled contigs
amrfinder -n contigs.fasta -o amr_results.tsv --threads 8
With organism for point mutations
amrfinder -n contigs.fasta -O Escherichia -o amr_results.tsv
Include stress/virulence genes
amrfinder -n contigs.fasta -O Salmonella --plus -o amr_results.tsv
If you have predicted proteins
amrfinder -p proteins.faa -o amr_results.tsv
More from OpenClaw-Medical-Skills
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About this skill
What does the bio-metagenomics-amr-detection skill do?

Detect antimicrobial resistance genes using AMRFinderPlus, ResFinder, and CARD. Screen isolates and metagenomes for resistance determinants. Use when characterizing resistance profiles in clinical isolates, surveillance samples, or metagenomic data.

How do I install it?

Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-metagenomics-amr-detection --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 FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 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.

Keep going