BZD AI工具使用披露

数学建模竞赛一站式技能包：一次安装即可获得全流程与 BZD 系列全部技能——赛题逐句翻译(bzd-problem-translator)、建模思路(bzd-modeling-ideas)、问题重述(bzd-problem-restatement)、赛题分析与建模设计(analysis-modeling)、编程求解与冻结(coding-visual)、论文撰写(paper-writing)、图表选型(model-figure-selector)、科研绘图模板库(mathmodel-figure-templates)、模型字典(bzd-model-dictionary)、以及摘要/假设/求解/符号/格式/文献附录自查与整卷评审(bzd-review-paper)、AI工具使用披露、高校国奖查询。所有 $技能 交叉引用在同一插件内闭环，无跨插件依赖。推荐只安装本插件即可覆盖国赛全流程。

19 skills

数学建模竞赛全流程工作流插件：analysis-modeling 负责读取题面附件、拆解子问题、审计数据与假设并生成 ANALYSIS_MODELING_REPORT.md；coding-visual 负责可复现工程实现、模型验证、权威结果冻结与一图一脚本的最终论文图；paper-writing 负责按 Typst/LaTeX 双引擎模板组织章节、插入冻结结果并导出论文。捆绑子技能 model-figure-selector，在结果冻结后按论文证据选择并生成学术图表。三个阶段通过 reports/ 与 results/ 交接合同衔接。

4 skills

Review a user-provided Chinese mathematical-modeling abstract without requiring the problem statement or full paper, identify concrete writing defects, and prioritize revisions. Use when the user asks to check, diagnose, or improve a modeling-paper abstract; do not claim coverage or factual consistency that cannot be verified without the problem or paper.

1 skill

Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.

1 skill

Review a mathematical-modeling paper's model-assumption section against the complete problem statement. Diagnose whether each assumption is necessary, evidence-based, compatible with the task, appropriately simplified, and usable by the later model. Use for assumption review, not automatic full-paper review or unsupported assumption generation.

1 skill

Generate a coherent, whole-paper mathematical modeling solution framework from a complete contest problem. Use after the user supplies a CUMCM or other modeling problem and asks for modeling ideas, an overall solution plan, question-by-question analysis, candidate-model comparison, model-selection reasons, innovations, validation, or cross-question linkage. For every question, explain the task and mathematical essence, compare multiple feasible models in tables, recommend a route with explicit reasons, and keep all questions connected through shared data, variables, parameters, constraints, and validation.

1 skill

Comprehensive development workflow for building AI agents with the Atomic Agents framework. Includes specialized agents for schema design, architecture planning, code review, and tool development. Features guided workflows, progressive-disclosure skills, and best practice validation.

7 skills · 2 agents

Per-deal-and-packaging Commercial domain. v2.8.0 ships 8 skills: orchestrator + pricing-strategist (model picker + Van Westendorp WTP + packaging) + deal-desk (deal scorer + discount approval routing + redline) + partnerships-architect (5-tier classifier + joint GTM + revshare modeler) + channel-economics (cost-to-serve + ROI + mix optimizer) + commercial-policy (data-backed discount matrix + exception flow + linter) + rfp-responder (Shipley structured RFP/RFI/RFQ + win-theme + winrate predictor, context: fork) + commercial-forecaster (4Q-weighted bookings + cohort NRR/GRR + funnel-confidence with mandatory assumption disclosure). Hard rules: pricing outputs model+range (never a single number), deal outputs route to named human approver (never auto-approve), forecast outputs surface conversion assumption, RFP never invents claims for GAP requirements. 21 stdlib Python tools, 28+ reference docs. Distinct from business-growth (sales execution), c-level-advisor/cro-advisor (strategic), finance (close+report).

8 skills · 9 commands · 1 agent

Runs M&A diligence at scale with cited tabular review, builds disclosure schedules and closing checklists, drafts board consents and minutes in house format, and tracks entity compliance deadlines across jurisdictions.

13 skills · 1 agent · 7 MCP servers

The official MCP for agent-skills. Curated catalog, progressive disclosure flow: Search → load canonical SKILL.md → fetch only validated references. Safe, lean, zero config.

Skills plus explorer and reviewer subagents for building, scaffolding, debugging, understanding, and auditing applications with the Atomic Agents Python framework. Progressive-disclosure references for schemas, agents, tools, context providers, prompts, orchestration, memory, hooks, providers, project structure, and testing — plus a troubleshoot diagnostic, isolated-context subagents for codebase mapping and code review, and a new-app scaffolder.

7 skills · 2 agents

Consistency audit across docs, code, tests and CI with per-fact authority and advisory-only LLM findings. Audit probes are instructed to leave project inputs unchanged; each local run attempts disclosure-approved report and index writes in a separate Intent OS checkout, reporting held filing explicitly. Invoked by /release Phase 1.6.

1 skill · 1 command