Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Analyze user-provided papers and reports before final method selection.
This skill checks whether the user has placed original paper files under workspace/papers/, reads those originals, and extracts transferable method cues, assumptions, variables, validation ideas, and limitations for the current subquestions.
This skill does not fabricate references, browse for new papers, write the final method card, or copy a published model blindly.
Use this skill:
problem-parser and problem-classifier have produced validated artifacts.method-selector.workspace/papers/.The following should already exist or be provided:
workspace/papers/.If no paper originals are present under workspace/papers/, stop and remind the user to place them there before analysis begins.
Use or request:
workspace/problem/problem-parser/problem_parse.json, if available.workspace/problem/problem-classifier/problem_classification.json, if available.workspace/papers/.Check workspace/papers/ first.
.pdf, .docx, .md, .txt, or clearly named extracted text files.workspace/papers/related_paper_analysis.md if it already exists.Stop early if the paper folder is empty.
workspace/papers/.Inventory the available papers.
Read papers for modeling signals.
Map literature cues to the current problem.
Q1, Q2, or Q3.Summarize risks and reuse boundaries.
Write the literature analysis report.
workspace/papers/related_paper_analysis.md.method-selector.Hand off to method-selector.
Produce one Markdown literature-analysis artifact:
workspace/papers/related_paper_analysis.mdThe report should include:
Write a Markdown report at workspace/papers/related_paper_analysis.md.
Suggested sections:
workspace/papers/.workspace/papers/.Before handoff, verify:
workspace/papers/ was checked for user-supplied paper originals.workspace/papers/related_paper_analysis.md.method-selector.Stop and report a blocker if:
workspace/papers/ contains no paper originals.This skill must stop instead of guessing when:
workspace/papers/.When stopping, output:
After producing workspace/papers/related_paper_analysis.md, hand off to:
method-selector
The handoff should include:
Input state:
workspace/papers/ contains no original paper files.Output:
Blocker: no user-supplied paper originals were found under `workspace/papers/`.
Why it matters: this skill is designed to analyze original paper artifacts, not fabricate literature notes from memory.
Recommended next action: place the relevant paper originals or extracted paper text under `workspace/papers/`, then run `related-paper-analyzer` again.
Input state:
workspace/papers/ contains several relevant PDF papers.Output:
workspace/papers/related_paper_analysis.mdThe report summarizes:
Q1, Q2, and Q3method-selector아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.
아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.
Detect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit whether the semantic evidence required by the active lean or submission profile exists and is current, without requiring one verbose artifact per skill or an arbitrary number of pass bullets.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run scoped or final cross-media consistency checks for mathematical-modeling artifacts, comparing canonical numbers, symbols, parameters, decisions, files, and paper claims without performing full-workspace audits for low-risk changes.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Map contest attachments to subquestions, audit and clean raw data, and emit one reusable data profile with quality, coverage, imbalance, concentration, and method-readiness evidence for downstream risk screening.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build one compact choice card at a genuine mathematical-modeling judgment point. Use before method screening, after a meaningful experiment, or before final claim/freeze approval so the human chooses the trade-off while AI handles mechanical consequences.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.