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 rules/forger-labs-hq/researchforge/researchforge-papersgit 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/rules/forger-labs-hq/researchforge/researchforge-papers)<a href="https://agentmods.dev/rules/forger-labs-hq/researchforge/researchforge-papers"><img src="https://agentmods.dev/badge/rules/forger-labs-hq/researchforge/researchforge-papers.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00363 |
| Opus 5 | $0.00000 | $0.00181 |
| Sonnet 5 | $0.00000 | $0.00073 |
| Haiku 4.5 | $0.00000 | $0.00036 |
Grade A, and why
researchforge-papers 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 5d 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.
What it actually says
Find and review papers
Search
The engine generates queries from the objective (and repository scan, if any), retrieves candidates from arXiv, deduplicates, ranks, and stores them:
researchforge research search --json
To steer the search, pass explicit arXiv queries (repeatable):
researchforge research search -q "all:mixture of experts routing" --json
Report how many papers were retrieved, deduplicated, and stored — from the JSON, not from memory.
Review
researchforge papers list --json
researchforge papers show <paper-id> --json
When discussing a paper, use only its stored metadata (title, authors, abstract, ranking signals). ResearchForge stores metadata and abstracts, not full texts — say so if the user asks for details an abstract cannot support.
Next step after papers exist: the researchforge-landscape rule synthesizes them into research directions.
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.
- 5d ago First seen · 49 lines · 0 tokens per session scan A e40da17baaf8
researchforge-papers is a cursor rule published in the GitHub repository forger-labs-hq/researchforge (7 stars, last pushed 3d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 363 tokens. 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.
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