fmschulz/omics-skills

A skill and agent pack for omics data analysis, literature discovery, scientific writing, and data visualization. Works with Claude Code and the Codex CLI.

Documentation

Read the docs at . They cover installation, the agent set, the skill catalog, routing, and development. Release notes are published on the GitHub Releases page.

Scope

Four agent personas — omics-scientist, literature-expert, science-writer, dataviz-artist — compose a set of small, single-purpose skills (SKILL.md files) for tasks ranging from read QC through assembly, gene calling, annotation, phylogenomics, comparative genomics, structure prediction, viromics, interdomain HGT, statistics, manuscript drafting, and figure generation.

The canonical agent sources are Markdown prompts. Installation keeps those files for Claude Code and renders native TOML agent definitions for Codex. Skills are Markdown directories with defined input/output contracts. A deterministic router (scripts/skill_index.py) picks an agent and an ordered set of skills for a task.

How Analyses Are Run

Every skill can be used on its own. The bio-* skills also share a few habits that make exploratory analyses easier to audit and reproduce:

• Hypothesis register. Exploratory work starts with at least five working hypotheses (biological mechanism, technical artifact, null, sampling or batch effect, database artifact). Each is revised as supported, weakened, ruled out, or unresolved, with the evidence that changed its status.
• Reflection after each step. After each major result or QC gate, the agent records what was observed, which hypotheses gained or lost support, and the next discriminating check.
• Literature-derived analysis plan. Before deciding what is “interesting” the agent reads the literature for the inferred group and summarises which markers, comparison sets, plots, and outliers are diagnostic.
• Comparative axes against close relatives. When relatives are available, the query is run through five axes — genome-property frontier, marker-gene census, per-family copy-number, synteny and conserved neighborhoods, and non-coding RNA census — each producing a side-by-side comparison file.