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-resultsgit 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-results)<a href="https://agentmods.dev/skills/forger-labs-hq/researchforge/researchforge-results"><img src="https://agentmods.dev/badge/skills/forger-labs-hq/researchforge/researchforge-results/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-results"><img src="https://agentmods.dev/badge/skills/forger-labs-hq/researchforge/researchforge-results.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 | $0.00046 | $0.00509 |
| Opus 5.5 | $0.00018 | $0.00204 |
| Sonnet 5.5 | $0.00009 | $0.00102 |
| Haiku 4.5 | $0.00005 | $0.00051 |
Grade A, and why
researchforge-results 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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read and explain results
researchforge results show <run-id> --json
The JSON contains everything you may talk about: per-experiment measured metrics, baseline deltas, constraint checks, the ranking, the Pareto frontier over direction-inferable metrics, trade-off notes, and caveats.
How to summarize honestly:
- quote only numbers present in the JSON; never estimate, extrapolate, or fill gaps from memory — if a number is not recorded, say so;
- include the losers: rejected and failed experiments, with their recorded reasons, are first-class findings;
- surface every caveat the engine attached (including the one-off-result caveat) — a single full-benchmark win is promising, not validated;
- when candidates trade off (e.g. quality vs latency), present the frontier and let the user choose; do not silently pick for them.
For a visual overview, researchforge dashboard writes a self-contained
HTML dashboard (charts of every experiment vs the baseline, the trade-off
scatter with the constraint line, the funnel, and validation spread) to
.researchforge/reports/dashboard.html — offer researchforge dashboard --open when the user wants to see the results; it is built from the same
recorded data and nothing else. For a live view while runs are in
progress, researchforge serve --open starts a local read-only monitor
(requires pip install "researchforge[serve]").
If a candidate looks like a winner, the next step is the researchforge-validate skill — repeated runs are what earn the word "validated".
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 · 48 lines · 46 tokens per session scan A 69b2971a6af1
researchforge-results is a skill published in the GitHub repository forger-labs-hq/researchforge (9 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 46 tokens to every session and 509 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
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.
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.
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
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.
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.