fba-simulator

fba-simulator is a skill for Claude Code, Codex from aiming-lab/AutoResearchClaw. It costs 69 tokens per session (2,204 once invoked), scanned A, original, MIT.

A simulation skill for Flux Balance Analysis (FBA), a method that estimates how materials flow through a cell’s chemical reactions under set limits. It also runs related tests such as reaction-range analysis and gene or reaction knockouts.

In plain words
What is it for?
Use it to calculate growth rates and reaction flows, test gene or reaction removals, compare carbon sources, and find the range of flows that still supports a chosen outcome.
Why use it?
It provides numerical predictions about growth and chemical flows without requiring each possible cellular process to be measured experimentally.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to calculate growth rates and reaction flows, test gene or reaction removals, compare carbon sources, and find the range of flows that still supports a chosen outcome.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aiming-lab/autoresearchclaw/fba-simulator
About the project

AutoResearchClaw is a system that turns a research idea into a scientific paper through autonomous and collaborative AI research workflows. It is for researchers who want agents to investigate questions, run experiments, and produce papers, with optional human guidance. Catalogue skills and agents provide parts of its research workflow.

aiming-lab/AutoResearchClaw · 14,352 stars · on GitHub

Install

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.

Any agent
npx skills add aiming-lab/AutoResearchClaw --skill fba-simulator
Clone the repo
git clone --depth 1 https://github.com/aiming-lab/AutoResearchClaw

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for fba-simulator

README.md
[![agentmods](https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/fba-simulator.svg)](https://agentmods.dev/skills/aiming-lab/autoresearchclaw/fba-simulator)
Your own site
<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/fba-simulator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/fba-simulator.svg" alt="Measured on agentmods" height="20"></a>
Per session 69 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,204 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00069 $0.02204
Opus 5 $0.00034 $0.01102
Sonnet 5 $0.00014 $0.00441
Haiku 4.5 $0.00007 $0.00220

Measured 8d ago against content hash c172928c11ad, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

fba-simulator 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 8d ago.

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.

external/agents/Biology-Agent/skills/fba-simulator/SKILL.md · 256 lines

How it starts

The opening of the file, as written. The whole thing — 256 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Overview

The fba-simulator skill executes constraint-based metabolic simulations on a validated COBRApy model. FBA solves a linear program to find the flux distribution that maximizes (or minimizes) the objective function subject to stoichiometric and thermodynamic constraints.

This skill sits between model construction (gsmm-builder) and biological interpretation (flux-analyzer). All simulations are non-destructive: COBRApy context managers restore model state after each perturbation.


Workflow

Step 1 — Load Validated Model

import cobra
import cobra.io
import cobra.flux_analysis
import pandas as pd

model = cobra.io.load_json_model("my_model.json")
print(f"Model: {model.id}  Solver: {model.solver}")

Step 2 — Standard FBA

FBA maximizes the objective (typically biomass) subject to stoichiometric steady-state constraints: S·v = 0, lb ≤ v ≤ ub.

# Run FBA
solution = model.optimize()

print(f"Status          : {solution.status}")
print(f"Growth rate     : {solution.objective_value:.4f} h^-1")
print(f"Glucose uptake  : "
      f"{solution.fluxes['EX_glc__D_e']:.4f} mmol/gDW/h")
print(f"O2 uptake       : "
      f"{solution.fluxes.get('EX_o2_e', 0):.4f} mmol/gDW/h")
print(f"Acetate sec.    : "
      f"{solution.fluxes.get('EX_ac_e', 0):.4f} mmol/gDW/h")

# Save full flux distribution
solution.fluxes.to_csv("fba_fluxes.csv", header=["flux_mmol_gDW_h"])

Step 3 — Parsimonious FBA (pFBA)

pFBA first maximizes growth, then minimizes total absolute flux, producing the most "economical" solution consistent with maximum growth. This avoids biologically unrealistic high-flux split cycles.

pfba_solution = cobra.flux_analysis.pfba(model)

print(f"pFBA growth rate : {pfba_solution.objective_value:.4f} h^-1")
print(f"Total flux norm  : {pfba_solution.fluxes.abs().sum():.2f}")

pfba_solution.fluxes.to_csv("pfba_fluxes.csv", header=["flux_mmol_gDW_h"])

Step 4 — Flux Variability Analysis (FVA)

FVA computes the minimum and maximum flux each reaction can carry while maintaining at least fraction_of_optimum of the maximum growth rate. This reveals which fluxes are uniquely determined vs. flexible.

Read the full file on GitHub · 256 lines

Changes

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.

  1. 8d ago First seen · 256 lines · 69 tokens per session scan A c172928c11ad

Subscribe to this mod's changes

fba-simulator is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,352 stars, last pushed 19d ago), licensed MIT. It adds 69 tokens to every session and 2,204 once invoked, about $0.0003 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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