AI4Math-Paper-Reading

Route AI4Math paper-reading tasks to the appropriate bundled skill package.

Use when extracting reusable AI4Math research skills from papers, especially PDF-to-SkillCard workflows, paper triage, PDF preparation, proof-pattern extraction, and cross-paper SkillCard synthesis for a coding-agent-neutral paper-to-skill workflow.

Cluster, deduplicate, generalize, and rank proof-pattern SkillCandidates or SkillCards extracted from multiple papers. Use when a coding agent should synthesize single-paper extractions into a domain method map, merged SkillCards, and a synthesis report for an AI4Math skill library.

Search for papers that may provide new methods, new theory, sharper bounds, different assumptions, proof techniques, lower bounds, abstractions, or cross-domain transfers for a math research direction. Use when a coding agent should look beyond nearest-neighbor related work and generate innovation_candidates.json with reasons and risks.

Download open-access research-paper PDFs selected from reading_plan.json, candidate_papers.json, or innovation_candidates.json. Use when a coding agent should save paper.pdf, metadata.json, and a bilingual download report for confirmed paper IDs without bypassing paywalls, and before PDF-to-Markdown conversion or paper-to-skill extraction.

Extract reusable mathematical research skills from Markdown papers, especially proof-pattern skills such as descent lemmas, inequality relaxations, telescoping bounds, error decompositions, Lyapunov arguments, concentration bounds, Galerkin/Cea arguments, Aubin-Nitsche duality, and Lax-Milgram well-posedness. Use when a coding agent is asked to turn a math paper, proof, theorem section, convergence analysis, stability analysis, or error analysis into SkillCandidate JSON, SkillCard YAML, or a paper-to-skill report without relying on external LLM APIs.

Run the complete AI4Math paper-to-skill workflow from a user-provided PDF or Markdown paper. Use when a coding agent should preserve the original paper, convert PDF to paper.md when needed, extract proof-pattern SkillCandidates and SkillCards with line evidence, and optionally route reviewed cards into cross-paper synthesis.

Rank candidate research papers into must_read, should_read, maybe_read, and skip groups for a paper-to-skill workflow. Use when a coding agent should combine related-paper and innovation candidates, judge relevance, novelty, proof-pattern value, reading cost, access status, and produce a reading_plan.json with user confirmation checkpoints.

Convert local PDF papers to Markdown using a self-contained MinerU workflow. Use when a coding agent is asked to turn a PDF into a .md file, prepare Markdown input for paper-to-skill extraction, configure MinerU PDF conversion, create or reuse the ai4math Conda environment, or run PDF-to-Markdown preprocessing. The skill installs dependencies into the named Conda environment ai4math, creates .env in the current working directory, prompts interactively for MINERU_API_TOKEN when missing, and writes Markdown output in the current folder by default.

Retrieve and structure related math research papers from a research profile using the active agent's available search or browsing capability. Use when a coding agent should find same-problem, same-method, direct-extension, theoretical-background, or benchmark papers, record metadata and relevance reasons, and avoid building a custom retriever or crawler.

Build a research-interest profile from a small set of seed math papers, abstracts, or user notes. Use when a coding agent should infer the user's target domain, core problems, methods, assumptions, proof patterns, technical keywords, negative preferences, and search directions before related-paper retrieval or paper-to-skill extraction.

Route mathematical paper-reading tasks through structured extraction, dependency analysis, deep reading, literature search, and reference management modules.