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 forger-labs-hq/researchforge --skill researchforge-rungit clone --depth 1 https://github.com/forger-labs-hq/researchforgeWrote 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/forger-labs-hq/researchforge/researchforge-run)<a href="https://agentmods.dev/skills/forger-labs-hq/researchforge/researchforge-run"><img src="https://agentmods.dev/badge/skills/forger-labs-hq/researchforge/researchforge-run/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/forger-labs-hq/researchforge/researchforge-run"><img src="https://agentmods.dev/badge/skills/forger-labs-hq/researchforge/researchforge-run.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 | $0.00038 | $0.00530 |
| Opus 5 | $0.00019 | $0.00265 |
| Sonnet 5 | $0.00008 | $0.00106 |
| Haiku 4.5 | $0.00004 | $0.00053 |
Grade A, and why
researchforge-run 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Run the experiment funnel
Running requires an approved plan — the engine refuses unapproved runs. If approval is missing, go back to the researchforge-plan skill; never look for a way around the gate.
researchforge experiment run <plan-id> --json
What the engine does (so you can narrate it honestly):
- each attempt runs in its own detached git worktree with the contract's resource limits; the user's checkout is never touched;
- a screening baseline is measured with the screening command first, so screening results are compared like-for-like;
- experiments run one at a time: screening → survivors go to the full benchmark;
- failures and constraint violations are recorded, not retried silently.
This can take a while (the plan's approval showed worst-case wall time).
experiment run auto-starts a background live monitor when available and
prints Live monitor: <url> — always relay that URL to the user so
they can watch stages complete in real time while you narrate results in
chat (researchforge serve --status re-prints it; the monitor needs
pip install "researchforge[serve]"). If the process is interrupted or
times out:
researchforge experiment resume <run-id> --json
If the user does not want to continue an interrupted run, discard it so a fresh batch starts clean (ask them first — this cancels unfinished experiments, though finished results are kept):
researchforge experiment abandon <run-id>
When the run finishes, report per-experiment outcomes from the JSON — status, stage reached, and measured metrics. Screening numbers are screening numbers: never present them as full-benchmark or validated results. Then continue with the researchforge-results skill.
Rules
- The Python engine is the boundary: never work around a validation error, a protected path, or an approval gate — fix the artifact or ask the user.
- Approvals belong to the user: never pass
--yesor type a confirmation unless the user explicitly approved that step in this conversation. - Ground every summary in stored data: quote only numbers returned by
--jsonoutput or files under.researchforge/— never invent metrics.
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 · 59 lines · 38 tokens per session scan A d67d03c29d52
researchforge-run is a skill published in the GitHub repository forger-labs-hq/researchforge (8 stars, last pushed 7d ago), licensed Apache-2.0. It adds 38 tokens to every session and 530 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
fba-simulator
Run Flux Balance Analysis (FBA) and related constraint-based simulations using COBRApy. Covers standard FBA, parsimonious FBA (pFBA), Flux Variability Analysis (FVA), loopless FBA, gene/reaction knockouts, and carbon source swapping. Outputs flux distributions and CSV files.
flux-analyzer
Analyse FBA flux distributions to extract biological insights. Covers gene essentiality, phenotypic phase planes, flux sampling, pathway-level aggregation, secretion product prediction, and production of publication- quality figures.
gsmm-validator
Validate a COBRApy genome-scale metabolic model for mass/charge balance, stoichiometric consistency, biomass producibility, dead-end metabolites, thermodynamic loops, and GPR rule formatting. Outputs a structured validation report with errors and warnings.
stat-research-orchestrator
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
gsmm-builder
Build or load a genome-scale metabolic model (GSMM) using COBRApy. Covers loading from BIGG, constructing minimal models from scratch, setting medium constraints, and exporting validated .json model files.
mfa-pipeline-orchestrator
Orchestrate the full metabolic flux analysis pipeline from model loading to phenotype prediction and publication figures. Triggers when the user provides an organism name, BIGG model ID, or custom reaction list and wants end-to-end metabolic modelling run automatically.