COBRApy (Claude Skill)

Claude skill that drives COBRApy for constraint-based reconstruction and analysis (COBRA) of genome-scale metabolic models — flux balance analysis, flux variability analysis, gene/reaction knockouts, flux sampling, and gapfilling on SBML / JSON / YAML models.

   
Type Claude Skill
Supplier K-Dense Inc. (community OSS); COBRApy maintained by the opencobra/cobrapy project
Availability GA — actively maintained 2025–2026
Pricing Free / OSS skill (MIT collection); COBRApy itself is GPL-2.0-licensed — review license terms for commercial use
Capabilities Read/Write — Claude executes COBRApy via Python/Bash to load models, run optimisation, and produce flux distributions / knockout screens

How to install

  • Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone jaechang-hits/SciAgent-Skills and run /plugin install sciagent-skills in Claude Code (or copy skills/systems-biology-multiomics/cobrapy-metabolic-modeling into ~/.claude/skills/).
  • Claude Code / Claude.ai — Skills CLI (recommended): 
    npx skills add K-Dense-AI/scientific-agent-skills

    Installs the K-Dense collection; enable the cobrapy skill when prompted (also works in Cursor/Codex via the Agent Skills spec; requires Node ≥ 18).

  • Claude Code / Claude Desktop — manual clone: 
    git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/cobrapy ~/.claude/skills/
pip install cobra

Project-scoped alternative: copy into .claude/skills/ instead of ~/.claude/skills/.

What it does

SKILL.md with recipes for:

  • Model I/O — load and save SBML / JSON / YAML genome-scale models; inspect reactions, metabolites, genes, gene-protein-reaction (GPR) associations
  • Flux balance analysis (FBA) — optimize(), slim_optimize(), parsimonious FBA (pFBA), geometric FBA; biomass / ATP / metabolite-production objectives
  • Flux variability analysis (FVA) — including loopless FVA when thermodynamic feasibility matters, with fraction_of_optimum control to explore suboptimal space
  • Knockout screens — single and double gene/reaction deletions, parallelised for double deletions
  • Flux sampling — Markov-chain Monte Carlo sampling of the solution polytope, with validation utilities
  • Production envelopes — trade-off curves between biomass and product flux for metabolic-engineering design
  • Gapfilling and model refinement — identify minimal reaction sets that restore feasibility against a target medium / objective
  • Context managers — apply temporary model changes (with model: …) so state reverts automatically

Primary use cases: Systems-biology phenotype prediction, metabolic-engineering strain design (yield / titre / rate optimisation), drug-target identification via essentiality analysis, host-microbiome interaction modelling, integration of expression / proteomics constraints into genome-scale models.

Notes

Solver backends are pluggable via model.solver — defaults to GLPK, with CPLEX and Gurobi recommended for large genome-scale models or heavy sampling workloads. Skill is documentation plus Python recipes — Claude calls COBRApy locally via Bash/Python. Pairs naturally with the scanpy, pydeseq2, and gget entries when integrating omics data into constraint-based models, and with chembl / pubchem when mapping flux solutions to chemistry-side targets.

Sources

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