Individual skill
math-abstract
Use when 高教社杯/CUMCM/电工杯 mathematical modeling abstracts, keywords, highlights, first-page claims, or high-density summaries need drafting, revision, audit, or style diagnosis, especially when feedback mentions too many numbers, weak focus, process-listing, empty official prose, buzzwords, missing structure, weak keywords, unsupported values, or first-page claim verification.
math-abstract/SKILL.md
Source commit da53b41cab7e25be906f5899488229387e7921c0
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- Last checked
- Nov 5, 2026
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- Repository stars (not skill ratings)
- 58
- Repository last updated
- Jul 19, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai-assisted Repository topic
- codex-skills Repository topic
- cumcm Repository topic
- data-visualization Repository topic
- decision-analysis Repository topic
- evidence-driven Repository topic
- forecasting Repository topic
- latex Repository topic
- math-modeling Repository topic
- mathematical-modeling Repository topic
- operations Derived category
- security Derived category
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SKILL.mdcore · 11,288 bytesagents/openai.yamlasset · 444 bytes
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# Contest Abstract
## Purpose
Turn contest evidence into a dense, judge-friendly abstract, keywords, and first-page claim set.
Two modes:
- **Style coaching mode**: diagnose or rewrite a user-provided abstract for readability, focus, number burden, contest prose, and first-screen judge value. Use only claims supplied by the user; mark evidence gaps instead of inventing support.
- **Paper-ready mode**: generate or approve a final abstract only from verified paper evidence, using the evidence gates below.
Core rule: the abstract follows evidence. No number, ranking, feasibility statement, robustness statement, or innovation claim may appear in paper-ready text unless traceable to the body, table, figure, formula, registry row, or run log.
National-first quality gate: `国一候选` is a quality target, not an award promise (`不承诺获奖`). The abstract may compress only conclusions that show 题目贴合, 建模洞察, 证据可信, 可复现, and 边界清楚. A catchy but unsupported claim must be downgraded to an evidence gap.