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Route mathematical paper-reading tasks through structured extraction, dependency analysis, deep reading, literature search, and reference management modules.
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Basé sur la classification professionnelle SOC
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.
| name | math-paper-reading |
| description | Route mathematical paper-reading tasks through structured extraction, dependency analysis, deep reading, literature search, and reference management modules. |
Use this skill when a coding agent needs to read, structure, explain, trace, or manage mathematical papers with a data-driven workflow.
Start with agent_router.md. It is the master router and decides which
package-local module to load:
skill_base.md: extract definitions, lemmas, theorems, proofs, and
dependency structure into JSON.skill_pathway_proof.md: compute dependency paths and render proof graphs.skill_paper_deep_read.md: provide layered paper reading and gap filling.skill_reference_manager.md: maintain the local paper reference database.skill_literature_search.md: generate targeted literature-search strategies.Prefer the router and package-local module files over this thin compatibility entrypoint when executing a concrete workflow.