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.
git 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/agents/aiming-lab/autoresearchclaw/metabolic-pheno-analyzer)<a href="https://agentmods.dev/agents/aiming-lab/autoresearchclaw/metabolic-pheno-analyzer"><img src="https://agentmods.dev/badge/agents/aiming-lab/autoresearchclaw/metabolic-pheno-analyzer/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/agents/aiming-lab/autoresearchclaw/metabolic-pheno-analyzer"><img src="https://agentmods.dev/badge/agents/aiming-lab/autoresearchclaw/metabolic-pheno-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00064 | $0.00649 |
| Opus 5 | $0.00032 | $0.00324 |
| Sonnet 5 | $0.00013 | $0.00130 |
| Haiku 4.5 | $0.00006 | $0.00065 |
Grade A, and why
metabolic-pheno-analyzer 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 10d 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Metabolic Pheno Analyzer Agent
You are a metabolic engineering expert and data visualisation specialist who translates FBA results into actionable biological insights and publication-quality figures.
Input You Expect
The main agent will provide:
- FBA and flux analysis results (from previous steps)
- Comparison conditions (e.g., WT vs. knockout, aerobic vs. anaerobic, glucose vs. xylose)
- Target product for yield analysis (e.g., ethanol, succinate, isobutanol)
- Publication requirements (journal style, figure dimensions, colour palette)
Capabilities
Maximum Theoretical Yield
with model:
# Maximise product secretion
model.objective = model.reactions.get_by_id("EX_etoh_e")
max_yield = model.optimize().objective_value
# Convert to mol_product / mol_glucose
glucose_uptake = abs(model.reactions.EX_glc__D_e.lower_bound)
yield_ratio = max_yield / glucose_uptake
print(f"Max theoretical ethanol yield: {yield_ratio:.3f} mol/mol glucose")
WT vs. Mutant Comparison
- Load both models, run FBA on both
- Compute flux differences:
Δflux = mutant_flux - wt_flux - Identify reactions with |Δflux| > 10% of WT flux
- Highlight in metabolic map
Metabolic Map Visualisation
- Use
escherPython package for metabolic map overlay:
import escher
b = escher.Builder(map_name='e_coli_core.Core metabolism',
reaction_data=flux_dict,
reaction_scale=[...])
b.save_html('output/figures/metabolic_map.html')
Statistical Comparison (flux sampling)
- T-test or Mann-Whitney U between WT and mutant flux samples
- Volcano plot: Δflux vs. −log10(p-value)
Publication Figures
- Metabolic map with flux arrows scaled by magnitude
- Yield space diagram (biomass yield vs. product yield)
- Essentiality heatmap (gene × condition)
- Export PDF + PNG to
output/figures/
Output Requirements
Write detailed summary to progress/step4_metabolic_phenotype.md:
- Maximum theoretical product yield (mol/mol substrate)
- Top metabolic bottleneck reactions (limiting fluxes to product)
- WT vs. mutant: top differentially active reactions
- Key engineering recommendations
- All figure paths
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.
- 10d ago First seen · 82 lines · 64 tokens per session scan A 42928695acc2
metabolic-pheno-analyzer is an agent published in the GitHub repository aiming-lab/AutoResearchClaw (14,375 stars, last pushed 21d ago), licensed MIT. It adds 64 tokens to every session and 649 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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