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Agent skill

Bio Systems Biology Community Metabolic Modeling

by GPTomics in GPTomics/bioSkills

Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for…

MITAuto-check passedResearch & Science

Install Bio Systems Biology Community Metabolic Modeling

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-systems-biology-community-metabolic-modeling -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-systems-biology-community-metabolic-modeling --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/systems-biology/community-metabolic-modeling .claude/skills/bio-systems-biology-community-metabolic-modeling && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-systems-biology-community-metabolic-modeling
GitHub stars
1.2k
Used in
1 other repo
Token cost
~2.4k tokens
SKILL.md length
916 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for…

  • Modeling a microbiome
  • SKILL.md covers Version Compatibility, The governing principle: a…, Decision: which community method and Build and Simulate a Community…, plus 5 more sections
  • Runs Python scripts from its folder; calls pip
  • Predicting cross-feeding and competition

What it does

Bio Systems Biology Community Metabolic Modeling is an agent skill from GPTomics/bioSkills. Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for cross-feeding and competition scoring, and SteadyCom/COMETS for common-growth-rate and dynamic simulation. Use when modeling a microbiome or co-culture, predicting cross-feeding and competition, abundance-weighting members from metagenomics, choosing steady-state vs dynamic community modeling, avoiding the…

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/community_modeling.py` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. It works with Python. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Modeling a microbiome
  • Predicting cross-feeding and competition
  • Abundance-weighting members from metagenomics
  • Choosing steady-state vs dynamic community modeling

Example prompts

  • “Use the bio-systems-biology-community-metabolic-modeling skill to build and simulates multi-species metabolic community models from member…”
  • “/bio-systems-biology-community-metabolic-modeling”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use pip, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Systems Biology Community Metabolic Modeling loads about 2.4k tokens when it runs. Until then it costs about 167 tokens; SKILL.md has 916 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~167
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 916 words, ~2,399 tokens.

Download SKILL.mdSave it as .claude/skills/bio-systems-biology-community-metabolic-modeling/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-systems-biology-community-metabolic-modeling
description
Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for cross-feeding and competition scoring, and SteadyCom/COMETS for common-growth-rate and dynamic simulation. Use when modeling a microbiome or co-culture, predicting cross-feeding and competition, abundance-weighting members from metagenomics, choosing steady-state vs dynamic community modeling, avoiding the compartment-pooling artifact, or judging how member-model quality and namespace propagate into community predictions.
tool_type
python
primary_tool
micom

Version Compatibility

Reference examples tested with: MICOM 0.33+, COBRApy 0.29+, Python 3.10+ (SMETANA and COMETS are separate installs)

Before using code patterns, verify installed versions match. If versions differ:

  • Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Note: community FBA needs a QP solver for MICOM's cooperative tradeoff (HiGHS/CPLEX/Gurobi). Member models must share a namespace (BiGG vs ModelSEED), reconciled via MetaNetX before combining. SMETANA is a separate CLI (github.com/cdanielmachado/smetana); COMETS uses the cometspy toolbox.

Community Metabolic Modeling

"Model the metabolism of my microbial community" -> Combine member genome-scale models into a community, then predict community growth, individual growth rates, and metabolite exchange (cross-feeding and competition) under a shared medium.

  • Python: micom.Community(taxonomy).cooperative_tradeoff() (steady-state, abundance-weighted); SMETANA (cross-feeding scores); COMETS (dynamic)

The governing principle: a community model inherits every member's errors, and the shared space is a modeling choice

Two things dominate whether a community prediction means anything:

  • Reference-model quality propagates. A community model is only as good as its member reconstructions - a wrong biomass, a missing pathway, or an energy-generating cycle in one member distorts the whole community's exchange predictions. Curate members (systems-biology/model-curation) before combining, and confirm they share a namespace (BiGG vs ModelSEED); a namespace mismatch silently breaks metabolite sharing.
  • How the shared space is modeled is the central design decision, and the classic trap is compartment pooling. Modeling a community as one giant "bag" with a single shared metabolite pool is fast but biologically wrong: it lets any member use any other member's INTERNAL metabolites directly, inventing cross-feeding that requires no secretion. The correct structure gives each member its own compartments and connects them only through a shared EXTRACELLULAR medium with explicit exchange. Pooling artifacts are a recurring reviewer catch. MICOM and SteadyCom implement the compartmentalized structure correctly; hand-merging models by prefix usually does not.

A further modeling fork: steady-state community FBA (SteadyCom, MICOM) assumes a stable coexistence with a common community growth rate, while dynamic simulation (COMETS, BacArena) resolves the time course and spatial structure but is expensive and parameter-hungry. Neither predicts the other's regime.

Decision: which community method

GoalToolApproach / trade-off
Metagenome-scale gut community, abundance-weighted, steady stateMICOM (Python)community FBA with cooperative tradeoff (community vs individual growth); scales to many taxa from abundances
Cross-feeding / competition SCORES between membersSMETANA (CLI)MRO (resource overlap = competition), MIP (interaction potential = cooperation), per-metabolite scores; pairs with CarveMe
Coexistence at a common community growth rateSteadyComenforces one shared growth rate; elegant steady-state coexistence model
Time course / spatial dynamics, diffusionCOMETS / BacArenadynamic (COMETS) or individual-based spatial (BacArena) FBA; realistic but expensive/parameter-hungry

Do not model a community as one pooled "bag" model; use a tool that keeps members compartmentalized and connects them through a shared extracellular medium.

Build and Simulate a Community with MICOM

Goal: Combine member models (weighted by their metagenomic abundance) and predict community and per-member growth under a medium.

Approach: Assemble a taxonomy table (one row per taxon with an id, a model file, and an abundance), build the Community (which compartmentalizes members correctly), and solve with cooperative tradeoff - which finds a community growth optimum while spreading growth across members rather than letting one taxon dominate. Reserve the fraction argument to trade community optimum against individual growth.

python
from micom import Community
from micom.data import test_taxonomy

# taxonomy: columns id, file (per-taxon SBML), and abundance (from metagenomics). test_taxonomy()
# ships a ready E. coli example community.
taxonomy = test_taxonomy()

community = Community(taxonomy)            # builds the compartmentalized multi-species model
solution = community.cooperative_tradeoff(fraction=1.0)   # QP; needs HiGHS/CPLEX/Gurobi
print('community growth rate:', solution.growth_rate)
print(solution.members[['growth_rate']])  # per-taxon growth; NaN row is the shared medium
Show full SKILL.md (364 more words)Show less

Cross-Feeding and Competition (SMETANA)

bash
# SMETANA (separate install) scores interactions between member models built by CarveMe:
#   pip install smetana   # then:
# smetana model1.xml model2.xml -o community --flavor bigg
# Outputs: MRO (metabolic resource overlap = competition for shared nutrients),
#          MIP (metabolic interaction potential = potential cooperation/cross-feeding),
#          and per-metabolite SMETANA scores (who feeds whom). A high MIP with low MRO
#          suggests cooperative cross-feeding; high MRO suggests competition.

Dynamic and Spatial Simulation (COMETS)

python
# For the time course rather than a steady state, COMETS (cometspy) runs dynamic FBA on a lattice
# with metabolite diffusion. Use when the QUESTION is temporal (succession, diauxie, spatial
# structure), not a coexistence steady state. It is far more expensive and needs kinetic parameters
# (uptake Vmax/Km, initial biomass, diffusion constants) that a steady-state model does not.

Common Errors

SymptomCauseFix
Cross-feeding predicted that needs no secretioncompartment pooling (single shared internal pool)use MICOM/SteadyCom (compartmentalized); connect members only via a shared extracellular medium
Members will not exchange metabolitesnamespace mismatch (BiGG vs ModelSEED IDs)reconcile member models via MetaNetX before combining
Community growth nonsensicala member model is broken (bad biomass, energy cycle)curate each member first; a bad member poisons the community
cooperative_tradeoff errors on solverit is a QP and GLPK cannot solve ituse HiGHS (bundled), CPLEX, or Gurobi
One taxon takes all the growthplain community-max FBA has alternate optimause cooperative tradeoff (spreads growth) and set abundances from data
Dynamic run is impossibly slowCOMETS/BacArena are expensive and parameter-hungryuse a steady-state method unless the question is genuinely temporal/spatial
  • systems-biology/metabolic-reconstruction - Build the member models (CarveMe pairs with SMETANA)
  • systems-biology/model-curation - Curate members before combining; errors propagate to the community
  • systems-biology/flux-balance-analysis - Single-organism FBA underlying each member
  • metagenomics/abundance-estimation - Member abundances to weight the community
  • metagenomics/functional-profiling - Community-level metabolic potential from metagenomes

References

  • Diener C, Gibbons SM, Resendis-Antonio O. 2020. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems 5(1):e00606-19.
  • Zelezniak A, Andrejev S, Ponomarova O, et al. 2015. Metabolic dependencies drive species co-occurrence in diverse microbial communities. PNAS 112(20):6449-6454. (SMETANA)
  • Chan SHJ, Simons MN, Maranas CD. 2017. SteadyCom: predicting microbial abundances while ensuring community stability. PLoS Comput Biol 13(5):e1005539.
  • Zomorrodi AR, Maranas CD. 2012. OptCom: a multi-level optimization framework for the metabolic modeling and analysis of microbial communities. PLoS Comput Biol 8(2):e1002363.
  • Harcombe WR, Riehl WJ, Dukovski I, et al. 2014. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7(4):1104-1115. (COMETS)
  • Dukovski I, Bajic D, Chacon JM, et al. 2021. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat Protoc 16(11):5030-5082.
  • Bauer E, Zimmermann J, Baldini F, Thiele I, Kaleta C. 2017. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput Biol 13(5):e1005544.
  • Machado D, Andrejev S, Tramontano M, Patil KR. 2018. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46(15):7542-7553.

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in systems-biology/community-metabolic-modeling of GPTomics/bioSkills.

  • SKILL.md
  • examples/community_modeling.py
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

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Works with

Categories

Questions about Bio Systems Biology Community Metabolic Modeling

What does Bio Systems Biology Community Metabolic Modeling do?

Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for…. Bio Systems Biology Community Metabolic Modeling is an agent skill from GPTomics/bioSkills. Builds and simulates multi-species metabolic community models from member genome-scale models, using MICOM for abundance-weighted steady-state community FBA and cooperative tradeoff, SMETANA for cross-feeding and competition scoring, and SteadyCom/COMETS for common-growth-rate and dynamic simulation.

When should I use Bio Systems Biology Community Metabolic Modeling?

Bio Systems Biology Community Metabolic Modeling fits situations like: modeling a microbiome; predicting cross-feeding and competition; abundance-weighting members from metagenomics; choosing steady-state vs dynamic community modeling.

How do I install Bio Systems Biology Community Metabolic Modeling in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-community-metabolic-modeling -a claude-code`. Or copy the skill folder (systems-biology/community-metabolic-modeling in GPTomics/bioSkills) into .claude/skills/bio-systems-biology-community-metabolic-modeling in your project. Claude Code loads it when a task matches its description.

How do I install Bio Systems Biology Community Metabolic Modeling in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-community-metabolic-modeling -a codex`. Or copy the skill folder (systems-biology/community-metabolic-modeling in GPTomics/bioSkills) into .agents/skills/bio-systems-biology-community-metabolic-modeling in your project. Codex loads it when a task matches its description.

Can I use Bio Systems Biology Community Metabolic Modeling in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-systems-biology-community-metabolic-modeling -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-systems-biology-community-metabolic-modeling, .gemini/skills/bio-systems-biology-community-metabolic-modeling, .github/skills/bio-systems-biology-community-metabolic-modeling and .opencode/skills/bio-systems-biology-community-metabolic-modeling in your project.

What does Bio Systems Biology Community Metabolic Modeling need to run?

Going by SKILL.md and its folder, Bio Systems Biology Community Metabolic Modeling needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Bio Systems Biology Community Metabolic Modeling access the network?

SKILL.md contains no URLs. Its commands use pip, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Bio Systems Biology Community Metabolic Modeling safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Systems Biology Community Metabolic Modeling use?

Bio Systems Biology Community Metabolic Modeling is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Systems Biology Community Metabolic Modeling use?

About 2.4k tokens (SKILL.md is roughly 9.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Systems Biology Community Metabolic Modeling?

Skills that share tags, products or a category with Bio Systems Biology Community Metabolic Modeling: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Singlecell Qc (xuzhougeng/wisp-science, 1k stars) and Trackplot (ygidtu/trackplot, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Systems Biology Community Metabolic Modeling?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.