# Researchforge Papers > Search arXiv for papers relevant to the project objective and review what was stored. Use when the user wants literature, related work, or asks what papers ResearchForge found. - Skill: `forger-labs-hq/researchforge-papers` (Agent Skill) - Install (CLI): `npx skillmds@latest add forger-labs-hq/researchforge-papers` - Raw SKILL.md: https://api.skillmd.com/api/skills/forger-labs-hq/researchforge-papers/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: forger-labs-hq (https://skillmd.com/u/forger-labs-hq) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/forger-labs-hq/researchforge-papers --- # 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: ```bash researchforge research search --json ``` To steer the search, pass explicit arXiv queries (repeatable): ```bash 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 ```bash researchforge papers list --json researchforge papers show --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.