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 skill 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.