Agent skill · Testing & QA

bio-ecological-genomics-biodiversity-metrics

Calculates species richness, diversity, and turnover using the Hill number framework with iNEXT coverage-based rarefaction/extrapolation, asymptotic diversity estimation, and beta diversity partitioning (betapart turnover vs nestedness). Compares assemblages using coverage-standardized rather than size-standardized rarefaction. Use when quantifying biodiversity from species abundance or incidence data, comparing diversity across sites, or constructing rarefaction curves. Not for clinical 16S microbiome alpha/beta diversity (see microbiome/diversity-analysis).

BioTender-maxgithub.com/BioTender-maxGitHub ↗
claude-codeNOASSERTION
Install
npx skills add BioTender-max/awesome-bio-agent-skills --skill biodiversity-metrics --agent claude-code

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

Facts
Files in the skill folder: 4
SKILL.md size: 9 KB
Bundled scripts: none
Path: skills/bioskills/biodiversity-metrics/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 135
Language: Python

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: ggplot2 3.5+, vegan 2.6+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Biodiversity Metrics **"Calculate species diversity for my ecological samples"** → Compute Hill number diversity (richness, Shannon, Simpson) with coverage-based rarefaction/extrapolation using iNEXT, and partition beta diversity into turnover and nestedness components with betapart. - R: `iNEXT::iNEXT()` for coverage-based rarefaction and extrapolation - R: `betapart::beta.multi()` for beta diversity partitioning Calculates alpha diversity using Hill numbers, coverage-based rarefaction/extrapolation, and beta diversity partitioning into turnover and nestedness components. ## Hill Numbers Framework Hill numbers unify diversity indices into a single parametric family controlled by order q: | Order (q) | Name | Sensitivity | Equivalent Index | |-----------|------|-------------|----------------

What's inside
Steps it walks through
  1. Version Compatibility
  2. Hill Numbers Framework
  3. iNEXT Coverage-Based Rarefaction
  4. Point Estimates at Standardized Coverage
  5. Asymptotic Diversity Estimation
  6. iNEXT.3D: Taxonomic, Phylogenetic, and Functional Diversity
  7. Classic Diversity with vegan
  8. Beta Diversity Partitioning with betapart
  9. Interpreting Beta Diversity Components
  10. Visualization
  11. Related Skills
Ships with 3 files
  • examples/beta_partitioning.R
  • examples/inext_diversity.R
  • usage-guide.md
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About this skill
What does the bio-ecological-genomics-biodiversity-metrics skill do?

Calculates species richness, diversity, and turnover using the Hill number framework with iNEXT coverage-based rarefaction/extrapolation, asymptotic diversity estimation, and beta diversity partitioning (betapart turnover vs nestedness). Compares assemblages using coverage-standardized rather than size-standardized rarefaction. Use when quantifying biodiversity from species abundance or incidence data, comparing diversity across sites, or constructing rarefaction curves. Not for clinical 16S microbiome alpha/beta diversity (see microbiome/diversity-analysis).

How do I install it?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill biodiversity-metrics --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 BioTender-max/awesome-bio-agent-skills, a repository with 135 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.

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