Agent skill

Bio Metagenomics Amr Detection

by GPTomics in GPTomics/bioSkills

Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling…

MITAuto-check passedResearch & Science

Install Bio Metagenomics Amr Detection

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/metagenomics/amr-detection .claude/skills/bio-metagenomics-amr-detection && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-metagenomics-amr-detection
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.6k tokens
SKILL.md length
1,570 words
Files
4
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling…

  • Works in 3 steps: match -> gene - defeated by partial hits… → gene -> resistance - defeated by… → gene -> who carries it / is it mobile -…
  • Quantifying a community resistome
  • SKILL.md covers Version Compatibility, The Single Most Important…, Read-Based vs Assembly-Based:… and Tool Taxonomy, plus 8 more sections
  • Runs Python and Shell scripts from its folder; calls pip

What it does

Bio Metagenomics Amr Detection is an agent skill from GPTomics/bioSkills. Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling with AMRFinderPlus/ABRicate on assembled contigs or MAGs. Covers why an ARG hit is a sequence match not a phenotype, why a metagenomic ARG has no host and no genomic context until assembly (and assembly breaks at ARGs), per-gene curated thresholds vs a flat 80/80, gene-fraction false-positive control, and cross-study…

Its SKILL.md is about 3.6k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files (for example `examples/batch_amr_screening.py`, `examples/run_amrfinder.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Quantifying a community resistome
  • Normalizing ARG abundance
  • Calling ARGs from metagenome contigs

Example prompts

  • “Use the bio-metagenomics-amr-detection skill to profile the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based…”
  • “/bio-metagenomics-amr-detection”

Requirements

  • Python 3
  • A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. match -> gene - defeated by partial hits (a 30 bp conserved-domain hit to a 1 kb ARG is not a gene); guarded by gene-fraction /…
  2. gene -> resistance - defeated by expression and regulation: silent sul2 below ECOFF, ampC/blaOXA driven only when an IS lands in the…
  3. gene -> who carries it / is it mobile - defeated by read shortness: a short read cannot see its neighbors, so host and plasmid context…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Metagenomics Amr Detection loads about 3.6k tokens when it runs. Until then it costs about 202 tokens; SKILL.md has 1,570 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~202
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,570 words, ~3,646 tokens.

Download SKILL.mdSave it as .claude/skills/bio-metagenomics-amr-detection/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
bio-metagenomics-amr-detection
description
Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling with AMRFinderPlus/ABRicate on assembled contigs or MAGs. Covers why an ARG hit is a sequence match not a phenotype, why a metagenomic ARG has no host and no genomic context until assembly (and assembly breaks at ARGs), per-gene curated thresholds vs a flat 80/80, gene-fraction false-positive control, and cross-study normalization pitfalls. Use when quantifying a community resistome, normalizing ARG abundance, or calling ARGs from metagenome contigs. For pure-culture isolate AMR, point mutations, and phenotype/MIC prediction see epidemiological-genomics/amr-surveillance.
tool_type
cli
primary_tool
AMRFinderPlus

Version Compatibility

Reference examples tested with: AMRFinderPlus 3.12+, RGI 6+ (CARD 3.2+), ABRicate 1.0+, pandas 2.2+.

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: amrfinder -V (reports software AND database version), rgi main --version, abricate --list to confirm DB snapshots
  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The AMR reference DATABASE is versioned and updated roughly monthly; amrfinder -V reports both software and database version, and ABRicate ships pinned database snapshots so two labs on different versions get different calls. Record the tool version, the database version, and (for read-based work) the normalization unit and sequencing depth - none are recoverable later.

AMR Detection (Community Resistome)

"What resistance genes are in my community, and how abundant?" -> Match reads or contigs to a curated ARG database - reporting that ARG sequences are present at some relative abundance, never that the sample is resistant, because a metagenomic hit has no host and no expression.

  • CLI (reads): rgi bwt -1 R1.fq.gz -2 R2.fq.gz -a kma -n 16 -o sample --local
  • CLI (contigs/MAGs): amrfinder -n contigs.fasta --plus -o amr.tsv

Scope: community/metagenomic resistome - read-based quantification and contig/MAG presence calling. Pure-culture isolate AMR, point-mutation resistance, in-silico antibiogram, MLST/clone/outbreak context, and GLASS reporting -> epidemiological-genomics/amr-surveillance. Assembly/binning and ARG-host linkage mechanics -> genome-assembly/metagenome-assembly. General gene-family/pathway abundance -> functional-profiling.

The Single Most Important Modern Insight -- An ARG Hit Is a Sequence Match, Not a Phenotype

An ARG hit is a match against a reference database, not a measured resistance phenotype - and in a metagenome it is a match with no host and no genomic context until assembly. Three inferences a naive pipeline silently makes, all wrong:

  1. match -> gene - defeated by partial hits (a 30 bp conserved-domain hit to a 1 kb ARG is not a gene); guarded by gene-fraction / breadth-of-coverage.
  2. gene -> resistance - defeated by expression and regulation: silent sul2 below ECOFF, ampC/blaOXA driven only when an IS lands in the promoter, efflux that needs overexpression, truncations that still score a partial hit, point mutations where only the SNP matters.
  3. gene -> who carries it / is it mobile - defeated by read shortness: a short read cannot see its neighbors, so host and plasmid context need assembly, long reads, or Hi-C.

The honest deliverable is "these ARG sequences are present at this relative abundance in this community," never "this sample is resistant to drug X." The moment a report says resistant, it has smuggled in a host, an expression assumption, and a clinical breakpoint the data never contained. On a pure culture the organism can be grown and an MIC measured - that is a different skill (epidemiological-genomics/amr-surveillance).

Read-Based vs Assembly-Based: the Core Metagenomic Tradeoff

AxisRead-based (RGI bwt, AMR++, ARGs-OAP, deepARG, GROOT)Assembly-based (AMRFinderPlus/RGI main/ABRicate on contigs/MAGs)
Low-abundance sensitivityhigh - every read counts, below assembly coveragelow - ARGs at low coverage do not assemble
Quantificationyes - abundance + normalizationpresence/absence per contig
Host / MGE contextnone without binningpossible via contig taxonomy / MAG
Point-mutation resistanceweak/unreliable (RGI bwt cannot screen the SNP)yes, with organism/model
False positivespartial hits unless gene-fraction filteredchimeric contigs, but vettable

The assembly paradox: metagenomic assemblies preferentially break exactly at ARG/MGE boundaries, recovering only a small fraction of true ARG genomic contexts and underestimating the resistome (Abramova 2024 BMC Genomics 25:959). So "assemble to get host" is necessary but not sufficient - long reads (Nanopore/PacBio) and Hi-C metagenomics are the real remedy for ARG-host/MGE linkage.

Tool Taxonomy

ToolCitationRoleWhen
AMRFinderPlusFeldgarden 2021 Sci Rep 11:12728NCBI Reference Gene Catalog; per-gene curated cutoffs + HMMscontig/MAG presence calling; the default contig caller
RGI bwtAlcock 2023 Nucleic Acids Res 51:D690CARD homolog-model read mapping (KMA/bowtie2/bwa)read-based resistome with coverage/depth per allele
AMR++ / MEGARes 3.0Bonin & Doster 2023 Nucleic Acids Res 51:D744BWA-MEM + gene-fraction filter + rarefactionquantitative resistome with built-in partial-hit control
ARGs-OAP / SARGYin 2023 Engineering 27:234two-stage read annotation + 16S/cell normalizationcopies-ARG-per-16S / per-cell units
deepARGArango-Argoty 2018 Microbiome 6:23deep-NN over dissimilarity featurescatches divergent ARGs best-hit BLAST misses
GROOTRowe & Winn 2018 Bioinformatics 34:3601variation-graph alignmenttypes SNP-bearing alleles that flat references conflate
ABRicateSeemann (no paper)flat 80/80 BLASTn, bundled DB snapshotsquick contig screen; acquired genes only, no point mutations

Decision Tree by Scenario

ScenarioRecommendedWhy
Quantitative resistome from readsRGI bwt or AMR++ or ARGs-OAPabundance + normalization; no host/context
Divergent / novel ARGs from readsdeepARG (confirm surprising calls)dissimilarity features beat top-hit BLAST
Type a specific high-similarity alleleGROOTgraph carries SNP-bearing variants
Presence per contig / MAGAMRFinderPlus (--plus) on contigscurated per-gene cutoffs; possible host via binning
Quick multi-DB contig screenABRicatefast; but flat 80/80, no point mutations
Is the ARG mobile / in a pathogen?assemble+bin, long read, or Hi-Cshort reads cannot link ARG to host
Pure culture / phenotype / MIC-> epidemiological-genomics/amr-surveillanceisolate AMR is a different skill
Cross-study abundance comparisonwithin-study only, same DB+normalization+depth"total ARG abundance" is rarely comparable

Read-Based Resistome Quantification

bash
# CARD read mapping (homolog models). RGI bwt CANNOT screen point-mutation SNPs, so this is for
# acquired/homolog ARGs only - never report a gyrA read hit as fluoroquinolone resistance.
rgi bwt -1 reads_R1.fq.gz -2 reads_R2.fq.gz \
    -a kma -n 16 \
    -o sample_resistome --local
# Outputs *.gene_mapping_data.txt with percent coverage and depth per gene.

# AMR++/MEGARes applies the gene-fraction filter (default 80%): the minimum proportion of a
# reference covered by >=1 read for "present" - the read-based analog of breadth-of-coverage.

ARGs-OAP/SARG normalizes to copies-of-ARG-per-16S or per-cell; report the unit. Gene fraction (breadth) is the single most important false-positive guard - without it a conserved-domain fragment counts as a present gene.

Contig / MAG Presence Calling

bash
amrfinder -n contigs.fasta \
    --plus \                  # also report biocide/metal (STRESS) and virulence elements
    --threads 8 -o amr.tsv
# --ident_min default -1 = use the per-gene CURATED cutoffs; overriding with a global value is usually a mistake.
# Point mutations require --organism (a single known species) - inappropriate for a mixed community;
# use it only on a taxonomically resolved MAG, and defer isolate point-mutation work to amr-surveillance.

AMRFinderPlus uses manually curated per-gene BLAST cutoffs (plus HMM cutoffs with protein), not a flat 80/80 - catching divergent real variants while rejecting partial housekeeping homologs. ABRicate, by contrast, is flat 80/80 and acquired-genes-only; it will never report a point mutation.

Per-Method Failure Modes

ARG presence reported as resistance

Trigger: an output column or summary that says "resistant." Mechanism: presence is not expression and not a host-linked MIC. Symptom: a sewage metagenome described as "resistant to carbapenems." Fix: report "ARG detected at abundance X"; reserve phenotype claims for isolates (amr-surveillance).

Show full SKILL.md (618 more words)Show less
--organism on a mixed community

Trigger: amrfinder --organism Escherichia on community contigs. Mechanism: organism mode assumes a single known species and calls organism-specific point mutations/intrinsic genes. Symptom: spurious point-mutation calls; filtered "intrinsic" genes wrong for the community. Fix: run organism mode only on a taxonomically resolved MAG; otherwise omit it.

Partial hit counted as a gene

Trigger: read mapping or BLAST with no breadth filter. Mechanism: a short conserved-domain match to a long ARG passes an identity threshold. Symptom: inflated ARG counts dominated by fragments. Fix: require gene-fraction / breadth-of-coverage (AMR++ default 80%); inspect coverage, not just identity.

Cross-study abundance comparison

Trigger: comparing "total ARG abundance" across papers. Mechanism: different databases (CARD/MEGARes/SARG/ResFinder), normalization units, aligners, and depth all change the number. Symptom: apparent resistome differences that are pipeline artifacts. Fix: compare only within a study with one pipeline; report DB version, tool version, normalization unit, and depth; hAMRonization harmonizes format, not the metric.

Loose/Discovery hits reported as ARGs

Trigger: CARD-RGI --include_loose in a surveillance report. Mechanism: Loose is below the curated bit-score cutoff - discovery only. Symptom: a flood of low-similarity false positives. Fix: report Perfect/Strict; reserve Loose for novel-variant discovery with manual curation.

Quantitative Thresholds

ThresholdSourceRationale
AMRFinderPlus --ident_min -1 (use curated)Feldgarden 2021 Sci Rep 11:12728per-gene curated cutoffs beat a global 80/80; overriding is usually wrong
AMRFinderPlus --coverage_min 0.5AMRFinderPlus docsminimum reference coverage for a call
AMR++ gene fraction 80%Bonin & Doster 2023 Nucleic Acids Res 51:D744breadth guard against partial-hit false positives
ResFinder acquired 0.80 id / 0.60 covBortolaia 2020 J Antimicrob Chemother 75:3491the documented default (not 0.90)
deepARG --min-prob 0.8Arango-Argoty 2018 Microbiome 6:23category probability cutoff; confirm surprising calls
CARD Loose tier = discovery onlyAlcock 2023 Nucleic Acids Res 51:D690below curated bit-score; not for surveillance

Common Errors

Error / symptomCauseSolution
No point mutations reportedABRicate or read-based homolog mapper usedthose cannot call SNPs; use AMRFinderPlus --organism on a MAG / defer to amr-surveillance
AMRFinderPlus calls changed silentlystale reference databaseamrfinder -u; record amrfinder -V software + DB version
ABRicate results differ between labspinned DB snapshot version differsrecord abricate --list versions; update with abricate-get_db
Inflated ARG abundanceno gene-fraction/breadth filterapply breadth-of-coverage; inspect coverage
gyrA "hit" from read-based RGI--include_other_models reports the gene, not the SNPdo not call resistance; SNP screening needs an isolate/organism

References

  • Feldgarden M, Brover V, Gonzalez-Escalona N, et al. 2021. AMRFinderPlus and the Reference Gene Catalog facilitate examination of the genomic links among antimicrobial resistance, stress response, and virulence. Sci Rep 11:12728.
  • Alcock BP, Huynh W, Chalil R, et al. 2023. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Res 51:D690-D699.
  • Bortolaia V, Kaas RS, Ruppe E, et al. 2020. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 75:3491-3500.
  • Bonin N, Doster E, Worley H, et al. 2023. MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline. Nucleic Acids Res 51:D744-D752.
  • Yin X, Zheng X, Li L, et al. 2023. ARGs-OAP v3.0: antibiotic-resistance gene database curation and analysis pipeline optimization. Engineering 27:234-241.
  • Arango-Argoty G, Garner E, Pruden A, et al. 2018. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6:23.
  • Rowe WPM, Winn MD. 2018. Indexed variation graphs for efficient and accurate resistome profiling. Bioinformatics 34:3601-3608.
  • Abramova A, Karkman A, Bengtsson-Palme J. 2024. Metagenomic assemblies tend to break around antibiotic resistance genes. BMC Genomics 25:959.
  • epidemiological-genomics/amr-surveillance - Isolate AMR, point mutations, phenotype/MIC, typing, GLASS
  • functional-profiling - General gene-family/pathway abundance (HUMAnN can surface ARG families)
  • kraken-classification - Taxonomic context for the community
  • genome-assembly/metagenome-assembly - Assembly/binning and ARG-host linkage
  • contamination-controls - Host depletion before resistome profiling
  • workflows/metagenomics-pipeline - End-to-end shotgun analysis

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files in metagenomics/amr-detection of GPTomics/bioSkills.

  • SKILL.md
  • examples/batch_amr_screening.py
  • examples/run_amrfinder.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Questions about Bio Metagenomics Amr Detection

What does Bio Metagenomics Amr Detection do?

Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling…. Bio Metagenomics Amr Detection is an agent skill from GPTomics/bioSkills. Profiles the antimicrobial-resistance gene content (resistome) of shotgun metagenomes - read-based quantification with RGI bwt, AMR++/MEGARes, ARGs-OAP/SARG, deepARG, or GROOT, and presence calling with AMRFinderPlus/ABRicate on assembled contigs or MAGs.

When should I use Bio Metagenomics Amr Detection?

Bio Metagenomics Amr Detection fits situations like: quantifying a community resistome; normalizing ARG abundance; calling ARGs from metagenome contigs.

How do I install Bio Metagenomics Amr Detection in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a claude-code`. Or copy the skill folder (metagenomics/amr-detection in GPTomics/bioSkills) into .claude/skills/bio-metagenomics-amr-detection in your project. Claude Code loads it when a task matches its description.

How do I install Bio Metagenomics Amr Detection in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a codex`. Or copy the skill folder (metagenomics/amr-detection in GPTomics/bioSkills) into .agents/skills/bio-metagenomics-amr-detection in your project. Codex loads it when a task matches its description.

Can I use Bio Metagenomics Amr Detection in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-metagenomics-amr-detection, .gemini/skills/bio-metagenomics-amr-detection, .github/skills/bio-metagenomics-amr-detection and .opencode/skills/bio-metagenomics-amr-detection in your project.

What does Bio Metagenomics Amr Detection need to run?

Going by SKILL.md and its folder, Bio Metagenomics Amr Detection needs Python and a shell for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3; A Bash shell.

Does Bio Metagenomics Amr Detection access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Metagenomics Amr Detection safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Metagenomics Amr Detection use?

Bio Metagenomics Amr Detection is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Metagenomics Amr Detection use?

About 3.6k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Metagenomics Amr Detection?

Skills that share tags, products or a category with Bio Metagenomics Amr Detection: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Metagenomics Amr Detection?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.