gsmm-validator

gsmm-validator is a skill for Claude Code, Codex from aiming-lab/AutoResearchClaw. It costs 52 tokens per session (1,990 once invoked), scanned A, original, MIT.

A quality-checking tool for genome-scale metabolic models in COBRApy. It checks whether reactions and model rules are structurally consistent and whether the model can produce biomass, while reporting errors and warnings.

In plain words
What is it for?
Use it to check mass and charge balance, biomass production, dead-end metabolites, stoichiometric consistency, thermodynamic loops, and gene–reaction rules.
Why use it?
An invalid metabolic model can produce biologically meaningless flux results without obvious failures; these checks expose problems early.

Skill for Claude CodeCodex

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

Good fit Use it to check mass and charge balance, biomass production, dead-end metabolites, stoichiometric consistency, thermodynamic loops, and gene–reaction rules.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aiming-lab/autoresearchclaw/gsmm-validator
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 · 14,361 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 gsmm-validator
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 gsmm-validator

README.md
[![agentmods](https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/gsmm-validator/github.svg)](https://agentmods.dev/skills/aiming-lab/autoresearchclaw/gsmm-validator)
Your own site
<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/gsmm-validator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/gsmm-validator/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for gsmm-validator

Your own site · 80×15
<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/gsmm-validator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/gsmm-validator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,990 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 $0.00052 $0.01990
Opus 5 $0.00026 $0.00995
Sonnet 5 $0.00010 $0.00398
Haiku 4.5 $0.00005 $0.00199

Measured 9d ago against content hash d1d730506f32, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

gsmm-validator 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 9d 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/gsmm-validator/SKILL.md · 245 lines

How it starts

The opening of the file, as written. The whole thing — 245 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Overview

The gsmm-validator skill performs rigorous quality control on a COBRApy Model before it enters any flux analysis pipeline. An invalid model silently produces biologically meaningless fluxes; validation catches structural errors early.

Validation covers six categories: (1) mass/charge balance, (2) feasibility and biomass production, (3) dead-end metabolites, (4) stoichiometric consistency, (5) thermodynamic loop detection, and (6) GPR rule integrity.


Workflow

Step 1 — Load the Model

import cobra
import cobra.io

model = cobra.io.load_json_model("my_model.json")
print(f"Loaded: {model.id} ({len(model.reactions)} reactions)")

Step 2 — Mass and Charge Balance Check

Unbalanced reactions are among the most common modelling errors. COBRApy computes elemental balance per reaction.

errors = []
warnings = []

print("=== Mass/Charge Balance ===")
for rxn in model.reactions:
    # Returns dict like {"C": -1, "H": 2} if imbalanced; empty dict if OK
    imbalance = rxn.check_mass_balance()
    if imbalance:
        # Exchange and demand reactions are expected to be imbalanced
        if rxn.id.startswith(("EX_", "DM_", "SK_", "BIOMASS")):
            warnings.append(f"WARN  [{rxn.id}] boundary reaction imbalanced "
                            f"(expected): {imbalance}")
        else:
            errors.append(f"ERROR [{rxn.id}] mass/charge imbalance: "
                          f"{imbalance}")

for msg in errors + warnings:
    print(msg)
print(f"  {len(errors)} error(s), {len(warnings)} warning(s)")

Step 3 — Biomass Producibility (FBA Feasibility)

print("\n=== Biomass Producibility ===")
solution = model.optimize()

if solution.status != "optimal":
    errors.append(f"ERROR Model is {solution.status} — "
                  f"cannot produce biomass under current medium.")
    print(f"  FAIL: {solution.status}")
elif solution.objective_value < 1e-6:
    errors.append("ERROR Growth rate is effectively zero "
                  "(< 1e-6 h^-1). Check medium and objective reaction.")
    print(f"  FAIL: growth = {solution.objective_value:.6f} h^-1")
else:
    print(f"  PASS: growth = {solution.objective_value:.4f} h^-1")

Read the full file on GitHub · 245 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. 9d ago First seen · 245 lines · 52 tokens per session scan A d1d730506f32

Subscribe to this mod's changes

gsmm-validator is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,361 stars, last pushed 20d ago), licensed MIT. It adds 52 tokens to every session and 1,990 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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