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 GPTomics/bioSkills --skill edna-metabarcodinggit clone --depth 1 https://github.com/GPTomics/bioSkillsWrote 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/gptomics/bioskills/edna-metabarcoding)<a href="https://agentmods.dev/skills/gptomics/bioskills/edna-metabarcoding"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/edna-metabarcoding/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/gptomics/bioskills/edna-metabarcoding"><img src="https://agentmods.dev/badge/skills/gptomics/bioskills/edna-metabarcoding.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00276 | $0.07231 |
| Opus 5 | $0.00138 | $0.03615 |
| Sonnet 5 | $0.00055 | $0.01446 |
| Haiku 4.5 | $0.00028 | $0.00723 |
Grade A, and why
bio-ecological-genomics-edna-metabarcoding 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 8d 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 — 396 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Version Compatibility
Reference examples tested with: DADA2 1.30+, cutadapt 4.7+, OBITools3 (Python 3), decontam 1.20+, microDecon 1.0+, occumb 1.0+, vsearch 2.27+, swarm 3.1+
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures - R:
packageVersion('<pkg>')then?function_nameto verify parameters - CLI:
<tool> --versionthen<tool> --helpto 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.
eDNA Metabarcoding
"Process eDNA samples to identify species present" -> Trim primers, denoise to ASVs (or cluster to OTUs), detect chimeras, assign taxonomy, filter contamination with negative controls AND DNA concentration, decompose tag-jumping artifacts, and quantify detection uncertainty via site-occupancy modeling. For the foundational eDNA-for-wildlife review, see Bohmann et al. 2014 Trends Ecol Evol 29:358-367.
- CLI:
cutadaptfor primer removal (linked-adapter mode) - R:
dada2::filterAndTrim()->dada()->assignTaxonomy()for ASV pipeline - CLI:
obi stats/obi clean/obi ecotagfor OBITools3 (NOTE: v3 plural commands) - R:
decontam::isContaminant()for contamination screening - R:
occumb::occumb()for detection-corrected occurrence
The Single Most Important Modern Insight -- Read Counts Are NOT Abundance
Elbrecht & Leese 2015 PLoS One 10:e0130324 and Lamb et al. 2019 Mol Ecol 28:420-430 (meta-analysis) established that metabarcoding read counts have weak-to-moderate, taxon-specific, NONLINEAR correlation with biomass or DNA input. Primer-binding bias dominates; PCR replicates introduce stochasticity. Reporting read counts as abundance without mock-community calibration is malpractice. Modern practice: report PRESENCE/ABSENCE or relative abundance with explicit calibration; use multiple PCR replicates; apply site-occupancy models for detection correction.
What ships with it
3 files 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.
- 8d ago First seen · 396 lines · 276 tokens per session scan A 6a3f55df86b4
bio-ecological-genomics-edna-metabarcoding is a skill published in the GitHub repository GPTomics/bioSkills (1,201 stars, last pushed 27d ago), licensed MIT. It adds 276 tokens to every session and 7,231 once invoked, about $0.0014 per session on Opus 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 2026-09-03.
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