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.
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 skills add aiming-lab/AutoResearchClaw --skill fba-simulatorgit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWrote 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.
[](https://agentmods.dev/skills/aiming-lab/autoresearchclaw/fba-simulator)<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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.
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.
- 8d ago First seen · 256 lines · 69 tokens per session scan A c172928c11ad
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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