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 mfa-pipeline-orchestratorgit 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/mfa-pipeline-orchestrator)<a href="https://agentmods.dev/skills/aiming-lab/autoresearchclaw/mfa-pipeline-orchestrator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/mfa-pipeline-orchestrator/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/mfa-pipeline-orchestrator"><img src="https://agentmods.dev/badge/skills/aiming-lab/autoresearchclaw/mfa-pipeline-orchestrator.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 | $0.00056 | $0.00962 |
| Opus 5 | $0.00028 | $0.00481 |
| Sonnet 5 | $0.00011 | $0.00192 |
| Haiku 4.5 | $0.00006 | $0.00096 |
Grade A, and why
mfa-pipeline-orchestrator 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 12d 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 — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
MFA Pipeline Orchestrator
Overview
Coordinates all mfa-agent sub-agents in sequence, tracking progress via progress/ markdown files so any failed step can be resumed independently.
Full pipeline:
Model source (BIGG ID / custom reactions)
→ [model-builder] models/<Model>.json + validation report
→ [fba-runner] simulations/fba_fluxes.csv + scan_summary.json
→ [flux-analyzer] analysis/essentiality.csv + phase_plane.png
→ [metabolic-pheno-analyzer] output/figures/*.pdf + yield table
Workflow
Step 0: Parse User Request
Extract and record in progress/step0_inputs.md:
- Model source (BIGG ID or custom)
- Organism and condition (aerobic/anaerobic, carbon source, concentration)
- Objective reaction (biomass or product)
- Gene knockouts to apply
- Analysis goals (essentiality, phase plane, yield optimisation, WT vs. mutant comparison)
- Target product (if yield analysis requested)
Step 1: Invoke model-builder
Provide: model source, medium constraints, objective, knockouts.
Wait for progress/step1_metabolic_model.md.
Read: model file path, WT growth rate, model statistics.
Step 2: Invoke fba-runner
Provide: model path, simulation types requested (FBA, pFBA, FVA, knockout screen), carbon source sweep if requested.
Wait for progress/step2_fba_simulation.md.
Read: flux CSV paths, essential gene count, secretion fluxes.
Step 3: Invoke flux-analyzer
Provide: model path, FBA results, analysis goals (essentiality, phase plane, sampling), nutrient pair for phase plane.
Wait for progress/step3_flux_analysis.md.
Read: essential genes, phase plane optimum, engineering targets.
Step 4: Invoke metabolic-pheno-analyzer
Provide: model path, all previous results, target product, publication requirements.
Wait for progress/step4_metabolic_phenotype.md.
Read: max theoretical yield, figure paths.
Progress File Specification
progress/step1_metabolic_model.md
# Step 1: Metabolic Model
## Status: PASS / FAIL
## Model: <BIGG_ID>.json
## Reactions: N Metabolites: M Genes: G
## WT growth rate: X h⁻¹
## Validation: mass balance errors=0, dead-ends=N
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.
- 12d ago First seen · 101 lines · 56 tokens per session scan A b244b759f6db
mfa-pipeline-orchestrator is a skill published in the GitHub repository aiming-lab/AutoResearchClaw (14,389 stars, last pushed 23d ago), licensed MIT. It adds 56 tokens to every session and 962 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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