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 --yes or type a confirmation unless the user explicitly approved that step in this conversation.
• Ground every summary in stored data: quote only numbers returned by --json output or files under .researchforge/ — never invent metrics.