Alphagenome Single Variant Analysis
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
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…
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .claude/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detectionType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/metagenomics/amr-detection .agents/skills/bio-metagenomics-amr-detection && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .agents/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/metagenomics/amr-detection .cursor/skills/bio-metagenomics-amr-detection && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .cursor/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path metagenomics/amr-detection--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/metagenomics/amr-detection .gemini/skills/bio-metagenomics-amr-detection && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .gemini/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detectionInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/metagenomics/amr-detection .github/skills/bio-metagenomics-amr-detection && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .github/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-metagenomics-amr-detection -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-metagenomics-amr-detection --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/metagenomics/amr-detection .opencode/skills/bio-metagenomics-amr-detection && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-metagenomics-amr-detection" agent skill from https://github.com/GPTomics/bioSkills/tree/main/metagenomics/amr-detection into .opencode/skills/bio-metagenomics-amr-detection/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-metagenomics-amr-detection", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-metagenomics-amr-detectionProfiles 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. 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
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.
Ships script files (Python and Shell), which the agent can run.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
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.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,570 words, ~3,646 tokens.
.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.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:
amrfinder -V (reports software AND database version), rgi main --version, abricate --list to confirm DB snapshotspip show <package> then help(module.function) to check signaturesIf 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.
"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.
rgi bwt -1 R1.fq.gz -2 R2.fq.gz -a kma -n 16 -o sample --localamrfinder -n contigs.fasta --plus -o amr.tsvScope: 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.
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:
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).
| Axis | Read-based (RGI bwt, AMR++, ARGs-OAP, deepARG, GROOT) | Assembly-based (AMRFinderPlus/RGI main/ABRicate on contigs/MAGs) |
|---|---|---|
| Low-abundance sensitivity | high - every read counts, below assembly coverage | low - ARGs at low coverage do not assemble |
| Quantification | yes - abundance + normalization | presence/absence per contig |
| Host / MGE context | none without binning | possible via contig taxonomy / MAG |
| Point-mutation resistance | weak/unreliable (RGI bwt cannot screen the SNP) | yes, with organism/model |
| False positives | partial hits unless gene-fraction filtered | chimeric 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 | Citation | Role | When |
|---|---|---|---|
| AMRFinderPlus | Feldgarden 2021 Sci Rep 11:12728 | NCBI Reference Gene Catalog; per-gene curated cutoffs + HMMs | contig/MAG presence calling; the default contig caller |
| RGI bwt | Alcock 2023 Nucleic Acids Res 51:D690 | CARD homolog-model read mapping (KMA/bowtie2/bwa) | read-based resistome with coverage/depth per allele |
| AMR++ / MEGARes 3.0 | Bonin & Doster 2023 Nucleic Acids Res 51:D744 | BWA-MEM + gene-fraction filter + rarefaction | quantitative resistome with built-in partial-hit control |
| ARGs-OAP / SARG | Yin 2023 Engineering 27:234 | two-stage read annotation + 16S/cell normalization | copies-ARG-per-16S / per-cell units |
| deepARG | Arango-Argoty 2018 Microbiome 6:23 | deep-NN over dissimilarity features | catches divergent ARGs best-hit BLAST misses |
| GROOT | Rowe & Winn 2018 Bioinformatics 34:3601 | variation-graph alignment | types SNP-bearing alleles that flat references conflate |
| ABRicate | Seemann (no paper) | flat 80/80 BLASTn, bundled DB snapshots | quick contig screen; acquired genes only, no point mutations |
| Scenario | Recommended | Why |
|---|---|---|
| Quantitative resistome from reads | RGI bwt or AMR++ or ARGs-OAP | abundance + normalization; no host/context |
| Divergent / novel ARGs from reads | deepARG (confirm surprising calls) | dissimilarity features beat top-hit BLAST |
| Type a specific high-similarity allele | GROOT | graph carries SNP-bearing variants |
| Presence per contig / MAG | AMRFinderPlus (--plus) on contigs | curated per-gene cutoffs; possible host via binning |
| Quick multi-DB contig screen | ABRicate | fast; but flat 80/80, no point mutations |
| Is the ARG mobile / in a pathogen? | assemble+bin, long read, or Hi-C | short reads cannot link ARG to host |
| Pure culture / phenotype / MIC | -> epidemiological-genomics/amr-surveillance | isolate AMR is a different skill |
| Cross-study abundance comparison | within-study only, same DB+normalization+depth | "total ARG abundance" is rarely comparable |
# 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.
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.
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).
--organism on a mixed communityTrigger: 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.
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.
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.
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.
| Threshold | Source | Rationale |
|---|---|---|
AMRFinderPlus --ident_min -1 (use curated) | Feldgarden 2021 Sci Rep 11:12728 | per-gene curated cutoffs beat a global 80/80; overriding is usually wrong |
AMRFinderPlus --coverage_min 0.5 | AMRFinderPlus docs | minimum reference coverage for a call |
| AMR++ gene fraction 80% | Bonin & Doster 2023 Nucleic Acids Res 51:D744 | breadth guard against partial-hit false positives |
| ResFinder acquired 0.80 id / 0.60 cov | Bortolaia 2020 J Antimicrob Chemother 75:3491 | the documented default (not 0.90) |
deepARG --min-prob 0.8 | Arango-Argoty 2018 Microbiome 6:23 | category probability cutoff; confirm surprising calls |
| CARD Loose tier = discovery only | Alcock 2023 Nucleic Acids Res 51:D690 | below curated bit-score; not for surveillance |
| Error / symptom | Cause | Solution |
|---|---|---|
| No point mutations reported | ABRicate or read-based homolog mapper used | those cannot call SNPs; use AMRFinderPlus --organism on a MAG / defer to amr-surveillance |
| AMRFinderPlus calls changed silently | stale reference database | amrfinder -u; record amrfinder -V software + DB version |
| ABRicate results differ between labs | pinned DB snapshot version differs | record abricate --list versions; update with abricate-get_db |
| Inflated ARG abundance | no gene-fraction/breadth filter | apply breadth-of-coverage; inspect coverage |
| gyrA "hit" from read-based RGI | --include_other_models reports the gene, not the SNP | do not call resistance; SNP screening needs an isolate/organism |
© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 3 other files in metagenomics/amr-detection of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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.
Bio Metagenomics Amr Detection next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Metagenomics Amr Detection this skillGPTomics/bioSkills | 1.2k | 1 repos | ~3.6k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
google-deepmind/science-skills
Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.
K-Dense-AI/scientific-agent-skills
Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.
google-deepmind/science-skills
A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…
aiming-lab/AutoResearchClaw
Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.
google-deepmind/science-skills
A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.
aiming-lab/AutoResearchClaw
Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
GPTomics/bioSkills
Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
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.
Bio Metagenomics Amr Detection fits situations like: quantifying a community resistome; normalizing ARG abundance; calling ARGs from metagenome contigs.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.