Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add aAAaqwq/AGI-Super-Team --skill claw-metagenomicsgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aaaaqwq/agi-super-team/claw-metagenomics)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/claw-metagenomics"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-metagenomics/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/claw-metagenomics"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/claw-metagenomics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00021 | $0.02380 |
| Opus 5.5 | $0.00008 | $0.00952 |
| Sonnet 5.5 | $0.00004 | $0.00476 |
| Haiku 4.5 | $0.00002 | $0.00238 |
Grade A, and why
claw-metagenomics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Shotgun Metagenomics Profiler
Comprehensive shotgun metagenomics analysis combining taxonomic classification, antimicrobial resistance gene detection, and functional pathway profiling from paired-end FASTQ files.
What it does
- Takes paired-end FASTQ files (R1, R2) or a single concatenated FASTQ as input
- Runs Kraken2 taxonomic classification against a standard database (e.g., Standard-8, PlusPF)
- Refines abundances with Bracken at species level (read re-estimation)
- Detects antimicrobial resistance genes with RGI against the CARD database
- Classifies detected ARGs by WHO critical priority pathogen association
- Optionally runs HUMAnN3 for functional pathway profiling (MetaCyc + UniRef)
- Generates three publication-quality figures:
- Figure 1: Taxonomy bar chart — top 20 species by relative abundance
- Figure 2: Resistome heatmap — ARG families by drug class with abundance
- Figure 3: WHO-critical ARG summary — priority-tier breakdown of detected resistance genes
- Produces a full reproducibility bundle (commands.sh, environment.yml, checksums.sha256)
Why this exists
If you ask a general AI to "analyse a metagenome," it will:
- Not know which Kraken2 database to use or how to set confidence thresholds
- Hallucinate Bracken parameters for read-length and taxonomic level
- Miss the connection between detected ARGs and WHO priority pathogen lists
- Skip HUMAnN3 entirely (or misconfigure its database paths)
- Produce a single bar chart with no resistance context
- Not provide a reproducibility bundle
This skill encodes the correct methodological decisions:
- Kraken2 confidence threshold of 0.2 (reduces false positives in environmental samples)
- Bracken re-estimation at species level with minimum 10 reads
- RGI MAIN with "Perfect" and "Strict" hit criteria only (no "Loose" hits)
- WHO Critical Priority Pathogen list mapped to detected ARG families
- HUMAnN3 with MetaCyc stratification for pathway-level functional context
- Thread count auto-detected from available CPUs
- Full reproducibility bundle for every run
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 239 lines · 21 tokens per session scan A 9b79fccffc1e
claw-metagenomics is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (112 stars, last pushed yesterday), licensed MIT. It adds 21 tokens to every session and 2,380 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-05.
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