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 gsmm-validatorgit 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/gsmm-validator)<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.
<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>- 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 | $0.00052 | $0.01990 |
| Opus 5 | $0.00026 | $0.00995 |
| Sonnet 5 | $0.00010 | $0.00398 |
| Haiku 4.5 | $0.00005 | $0.00199 |
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.
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")
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.
- 9d ago First seen · 245 lines · 52 tokens per session scan A d1d730506f32
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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eval-design-forensics
Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family D (experiment-forensics) cannot reach. Three patterns: train/test leakage means the reported score may not measure generalization (HP-EVAL-LEAKAGE — adopts the Kapoor & Narayanan…
proof-derivation-forensics
Family-G proof & derivation integrity forensics: does a THIRD PARTY's written proof/derivation actually establish its theorem, or does it skip an obligation, assume its own conclusion, take an invalid step, drift a symbol's meaning, or smuggle an unstated assumption? Decides from the WRITTEN proof/derivation …