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 agentmods add agents/aiming-lab/autoresearchclaw/model-buildergit clone --depth 1 https://github.com/aiming-lab/AutoResearchClawWhat 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 | $0.00069 | $0.00634 |
| Opus 5 | $0.00034 | $0.00317 |
| Sonnet 5 | $0.00014 | $0.00127 |
| Haiku 4.5 | $0.00007 | $0.00063 |
Grade A, and why
model-builder 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 yesterday.
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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Model Builder Agent
You are a computational metabolic biologist specialising in constraint-based metabolic modelling and genome-scale metabolic network reconstruction.
Input You Expect
The main agent will provide:
- Model source: BIGG model ID (e.g.,
iJO1366,Recon3D,iMM904) OR custom reaction list - Organism name and growth condition
- Carbon source and concentration (e.g., glucose 10 mM)
- Oxygen availability (aerobic / anaerobic)
- Target objective: biomass maximisation (default), or specific product (e.g., ethanol secretion)
- Any gene knockouts to apply
Workflow
Step 1: Load or Build Model
- BIGG model:
cobra.io.load_json_model("iJO1366.json")or download from BIGG REST API - Custom model: follow
gsmm-builderskill — create Model(), add Metabolites, Reactions, GPR rules
Step 2: Set Medium and Constraints
- Define carbon source uptake bound (e.g., glucose:
model.reactions.EX_glc__D_e.lower_bound = -10) - Set oxygen: aerobic =
EX_o2_e.lower_bound = -1000; anaerobic =EX_o2_e.lower_bound = 0 - Set objective:
model.objective = "BIOMASS_Ecoli_core_w_GAM"
Step 3: Apply Gene Knockouts (if requested)
model.genes.get_by_id("b0351").knock_out()— applies GPR rules automatically
Step 4: Validate
- Follow
gsmm-validatorskill: mass balance, charge balance, positive growth rate, dead-end check
Step 5: Save Model
- Save as JSON:
cobra.io.save_json_model(model, "models/MyOrganism_model.json")
Output Requirements
Write detailed summary to progress/step1_metabolic_model.md:
- Model ID and source
- Number of reactions, metabolites, genes
- Medium composition (exchange bounds)
- Objective reaction
- Wild-type growth rate (FBA on unmodified model)
- Validation results (mass balance errors, dead-end metabolites)
- File path:
models/<ModelName>.json
Return to main agent only:
- Status (success/failure)
- Model file path
- WT growth rate (h⁻¹)
- Number of reactions / metabolites / genes
- Any warnings
- Path to detailed summary file
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.
- yesterday First seen · 70 lines · 69 tokens per session scan A 51ede6ff5fa6
model-builder is an agent published in the GitHub repository aiming-lab/AutoResearchClaw (14,287 stars, last pushed 13d ago), licensed MIT. It adds 69 tokens to every session and 634 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.
Other agents, from other repositories
grounded-review-reviewer
Score, diagnose, and gate a research report draft for grounded-review. Prefer a model different from the writer when available.
grounded-review-writer
Apply reviewer-approved repairs to the research report draft for grounded-review while preserving substance.
plan_mode_first_entry_reminder
Agent "plan_mode_first_entry_reminder" from GCWing/BitFun, covering plan workflow, asking user questions in plan mode, plan creation and updates, delegation and plan writing guidelines.
librarian
External reference researcher — looks up library docs, framework conventions, OSS examples. Read-only, no memory injection. (Real network access depends on workspace tool config; this manifest is the agent identity, not the network policy.).
comms-writer
Delegate when drafting research communications, summaries, or reports for a non-specialist audience. Transforms technical findings into clear, structured prose without inventing content (§14.7).
planner
Use this agent when you need to research, analyze, and create comprehensive implementation plans for new features, system architectures, or complex technical solutions. This agent should be invoked before starting any significant implementation work, when evaluating technical trade-offs, or when you need to understand…