code-reviewer
FreeDetect whether approved modeling code is Python or MATLAB/Beita Tianyuan and route it to the matching reviewer using the compact named-check review contract.
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Collect and analyze relevant papers, reports, and reference methods to inform method selection without fabricating references or copying models blindly.
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Run score Not tested
See how to installReview the instructions the agent will receive before you install.
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-selectorNo reviews yet. Tell others what you thought.
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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.
The English summary is being prepared. Showing the original description.
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.
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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.
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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.
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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.
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Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
The English summary is being prepared. Showing the original description.