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COBRApy Metabolic Modeling

Run constraint-based metabolic models: FBA, FVA, knockouts, media design and sampling with COBRApy

Source Download skill
Repository
K-Dense-AI/scientific-agent-skills
Stars
55,780
Licence
MIT
Files
3 files

What it does

This skill teaches an AI agent to work with genome-scale metabolic models of cells, using the COBRApy Python library. Once installed, the agent can load or build a metabolic network, predict steady-state fluxes and growth with methods like FBA and FVA, simulate gene knockouts, design growth media, sample feasible flux states, and check models for errors. It also includes guidance on recording versions and settings so analyses can be reproduced.

Great for

  • Systems biology students learning flux balance analysis
  • Researchers running knockout screens or medium design on bundled E. coli or Salmonella models
  • Metabolic engineers who want reproducible constraint-based analyses with explicit solver and version settings

Skip it if

  • Predicting real measured growth or kinetic rates: the skill states FBA is a constraint-based prediction and does not establish experimental growth or kinetics
  • Clinical or safety decisions based on model output alone, since predictions depend heavily on model quality and stated constraints

Try asking

  • “Load the bundled textbook E. coli model and report its FBA growth rate and the flux through PFK”
  • “Run a single gene deletion screen and list genes whose knockout drops growth below 1% of baseline”
  • “Find a minimal growth medium that supports at least 50% of the baseline growth rate”
  • “Compare pFBA and FVA flux ranges for a set of reactions at 90% of optimal biomass”
Step-by-step guide: how to use COBRApy Metabolic ModelingInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 3 original files, the MIT licence and an attribution note.

    cobrapy.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip cobrapy.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip cobrapy.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Pin cobra==0.32.1 and record model source, solver, objective, medium and random seed with each analysis, as the skill recommends
  • Use the bundled textbook, iJO1366 or salmonella models offline; remote IDs such as e_coli_core need network access and are not aliases for bundled models
  • Review the validation workflow before trusting a model: a balanced-check pass alone does not prove chemical validity

What's inside

  • references/api_quick_reference.md
  • references/workflows.md
  • SKILL.md

Redistributed unmodified from K-Dense-AI/scientific-agent-skills under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.