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.
日本語の概要は準備中です。原文の説明を表示しています。
Polish mathematical modeling paper drafts for grammar, clarity, formula consistency, hedging calibration, overclaim detection, and contest formatting compliance. Use after paper-section-writer has drafted sections.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Polish mathematical modeling contest paper sections for language quality, logical clarity, formula consistency, and claim calibration.
This skill operates on already-drafted paper sections. It improves wording, fixes grammar, checks formulas, calibrates hedging to match evidence strength, detects overclaims, and ensures formatting compliance. It does not invent new content, add unsupported claims, or rewrite the paper's scientific argument.
Adapted from nature-polishing design principles: language serves the argument, polish should not hide weak reasoning, and claims must be proportional to evidence.
This skill does not write new paper sections, run experiments, generate figures, or perform final QA.
Use this skill:
paper-section-writer has drafted one or more paper sections.quality-assurance-auditor.The following should already exist or be provided:
paper/sections/.planning/symbol_table.md (if available).If paper sections do not exist, hand back to paper-section-writer.
Use or request:
paper/sections/*.md or paper/sections/*.tex — the drafted sections.methods/Qx/qx_final_method_explanation.md — for formula and notation verification.results/Qx/reports/qx_final_result_analysis.md — for claim verification.planning/symbol_table.md — for notation consistency.Identify the paper type and section.
Run the 12-point polish checklist (see below).
Calibrate claims against evidence.
Check formula and notation consistency.
Check terminology consistency.
Produce polished sections.
Match claim strength to evidence:
| Evidence Level | Appropriate Hedging | Example |
|---|---|---|
| Robust, multiple checks | Strong claim, no hedge | "The entropy-TOPSIS method produces stable rankings." |
| Single check, moderate perturbation | Moderate hedge | "The rankings appear stable under moderate weight changes." |
| Limited check, narrow range | Weak hedge | "The results suggest that rankings may be stable within the tested range." |
| No check, extrapolation | No claim allowed | Flag as unsupported. Do not write. |
Hedging phrases (strongest to weakest):
demonstrates / shows / establishes → strongestindicates / suggests / supports → moderatemay indicate / appears to / is consistent with → weakcould potentially / might possibly → weakest (use sparingly)Flag and downgrade or remove:
$$...$$ or \begin{equation}...\end{equation}) for important equations.$...$) for variable references and short expressions.planning/symbol_table.md.\ref{fig:...} or "Figure X" must correspond to an actual figure file.| Section | Top Priority |
|---|---|
| Abstract | Claim calibration, numerical precision, word count |
| Problem Restatement | Clarity, no added requirements |
| Assumptions | Necessity check, impact statements |
| Symbols | Completeness, consistency, distinction of variable types |
| Model Construction | Formula correctness, notation consistency, assumption traceability |
| Model Solution | Procedural clarity, reproducibility |
| Results Analysis | Claim-evidence alignment, figure/table references |
| Robustness | Stable vs fragile separation, boundary conditions |
| Strengths & Limitations | Specificity, honesty |
| Conclusion | Subquestion coverage, claim calibration |
When the source text is in Chinese and needs translation to English:
Before handing off, verify:
Stop and report a blocker if:
This skill must stop instead of guessing when:
When stopping, output:
After polishing:
→ quality-assurance-auditor
With:
Original: "Our entropy-TOPSIS model demonstrates significantly better performance than all existing evaluation methods, achieving optimal ranking accuracy."
Polish: "The entropy-TOPSIS model improves score differentiation compared to the equal-weight baseline (standard deviation: 0.15 vs 0.08). Among the three candidate methods tested (equal-weight, entropy-TOPSIS, AHP-TOPSIS), entropy-TOPSIS was selected for its objective weight derivation and ranking stability under ±10% perturbation."
Changes:
Original: "Let x_i be the score of city i. The weight w_j is computed by..."
Fixed: "Let $S_i$ be the composite score of city $i$. The indicator weight $\omega_j$ is computed by entropy method..."
Issue: x_i was used for scores in Q1 but for raw indicator values in Q2. Changed to S_i to match the global symbol table, and distinguished \omega_j (weight) from w_j (used elsewhere for a different variable).
Original (38 words): "After applying the entropy method to compute objective indicator weights and then using the TOPSIS method to compute the relative closeness of each city to the ideal solution, we obtained the final ranking as shown in Table 1."
Polished (two sentences, 15 + 14 words): "Entropy weighting produced objective indicator weights from data dispersion. TOPSIS then computed each city's relative closeness to the ideal solution, yielding the final ranking (Table 1)."
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Plan the smallest set of diagnostic, comparison, paper, and appendix figures or tables needed to support verified mathematical-modeling decisions and claims.
日本語の概要は準備中です。原文の説明を表示しています。