Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/k-dense-ai/scientific-agent-skills/cobrapynpx skills add K-Dense-AI/scientific-agent-skills --skill cobrapygit clone --depth 1 https://github.com/K-Dense-AI/scientific-agent-skillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00038 | $0.03589 |
| Opus 5 | $0.00019 | $0.01795 |
| Sonnet 5 | $0.00008 | $0.00718 |
| Haiku 4.5 | $0.00004 | $0.00359 |
Grade A, and why
cobrapy scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 497 lines — stays where its author put it; the contents beside it link to each section on GitHub.
COBRApy - Constraint-Based Reconstruction and Analysis
Overview
COBRApy is a Python library for constraint-based reconstruction and analysis (COBRA) of metabolic models, essential for systems biology research. Work with genome-scale metabolic models, perform computational simulations of cellular metabolism, conduct metabolic engineering analyses, and predict phenotypic behaviors.
Version note: Examples target cobra 0.31.1 on PyPI (import cobra). Docs: cobrapy.readthedocs.io. Repo: opencobra/cobrapy.
When to Use This Skill
Use this skill when:
- Loading, building, or exporting genome-scale metabolic models (SBML, JSON, YAML)
- Running FBA, pFBA, FVA, or flux sampling on COBRA models
- Performing gene or reaction knockout screens and production envelope analysis
- Designing or optimizing growth media and exchange constraints
- Gap-filling infeasible models or validating model consistency
Installation
uv pip install "cobra==0.31.1"
MATLAB model I/O (optional):
uv pip install "cobra[array]==0.31.1"
COBRApy uses optlang for solvers. GLPK installs automatically via swiglpk. For large MILPs/QPs, cobra 0.29+ adds a hybrid solver (HIGHS/OSQP); model.solver = "osqp" now routes through hybrid and may error on plain LPs in a future release—prefer model.solver = "hybrid" when available.
Core Capabilities
COBRApy provides comprehensive tools organized into several key areas:
1. Model Management
Load existing models from repositories or files:
from cobra.io import load_model
# Bundled locally (no network): textbook, iJO1366, salmonella
model = load_model("textbook") # alias for e_coli_core (95 reactions)
model = load_model("e_coli_core") # same core E. coli model
model = load_model("iJO1366") # genome-scale E. coli (bundled)
model = load_model("salmonella") # Salmonella iYS1720 (bundled)
# Remote (BiGG / BioModels; requires network, cached after first fetch)
model = load_model("iML1515") # E. coli genome-scale on BiGG
# Load from files
from cobra.io import read_sbml_model, load_json_model, load_yaml_model
model = read_sbml_model("path/to/model.xml")
model = load_json_model("path/to/model.json")
model = load_yaml_model("path/to/model.yml")
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 497 lines · 38 tokens per session scan A bb99605b1a4e
cobrapy is a skill published in the GitHub repository K-Dense-AI/scientific-agent-skills (40,390 stars, last pushed yesterday), licensed MIT. It adds 38 tokens to every session and 3,589 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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