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 5.5 | $0.00015 | $0.00212 |
| Sonnet 5.5 | $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 2mo 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.
- 2mo 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 (9 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 530 once invoked, about $0.0002 per session on Opus 5.5. 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
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fba-simulator
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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.
gsmm-builder
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stat-research-orchestrator
Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.
stat-result-validator
Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.