データ・AIのスキル

Documentation-first project planning that produces implementation-ready blueprints and single-shot coding agent prompts. Generates 4 interconnected docs — SPECIFICATION.md, IMPLEMENTATION.md, TASKS.md, BRANDING.md — plus a self-contained PROMPT.md for autonomous execution by any coding agent. Includes interactive tech stack selection, design pattern recommendations, and architecture decisions with trade-off analysis. Trigger when user wants to: plan a project, create specs, architect a system, break work into tasks, choose a tech stack, get design pattern advice, generate a coding agent prompt, or do documentation-first development. Phrases: "plan my project", "spec this out", "architect", "help me plan", "what stack should I use", "generate a prompt", "break this into tasks", "project docs", "I want to build X".

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Create companion Codex workflows from book chapters, reader exercises, prompts, field notes, and teaching material. Use when Codex needs to turn a chapter excerpt, 書籍練習, AI 提示詞, worksheet, or author note into a reusable Skill-style workflow, GitHub-shareable companion artifact, or book link resource for readers.

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Render Obsidian-style weighted graph views from book JSON, keyword indexes, TOCs, card data, or node-link data. Use when the user asks for Obsidian graph view, graph view png, weighted graph, keyword graph, directory-keyword graph, spherical graph visualization, or high-weight nodes centered with clear Chinese labels.

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Generate systematic refinement steps from high-level specifications to concrete implementations in Isabelle/HOL or Coq, preserving correctness obligations at each step. Use when working with formal verification, program refinement, proof development, or when translating abstract specifications into executable code while maintaining formal guarantees. Supports data refinement (abstract types → concrete structures), algorithmic refinement (specifications → algorithms), and stepwise refinement with proof obligations.

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Identify bug-introducing commits using semantic analysis that extends traditional SZZ algorithm. Distinguishes semantic changes from refactorings or code movements using control-flow and data-flow similarity analysis. Use when analyzing bug-fix commits to trace back to bug-introducing changes, investigating software evolution, conducting empirical studies on defect prediction, or reducing false positives in bug localization. Supports git repositories and provides explanations for why commits are identified as bug-introducing.

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Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.

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Generate formal specifications (definitions, predicates, invariants, pre/post-conditions) in Isabelle/HOL or Coq from informal requirements, source code, pseudocode, or mathematical descriptions. Use when users need to: (1) Formalize algorithms or data structures, (2) Create function specifications with contracts, (3) Generate predicates and properties for verification, (4) Translate informal requirements into formal logic, (5) Specify invariants for loops or data structures, or (6) Create formal definitions for mathematical concepts. Supports both Isabelle/HOL and Coq equally.

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Generate complete, production-ready functions and classes from formal specifications, design descriptions, type signatures, or natural language requirements. Use this skill when implementing APIs from specifications, creating data structures from schemas, building classes from UML diagrams, generating code from contracts, or translating design documents into code. Supports multiple programming languages and follows language-specific best practices.

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Performs abstract interpretation over source code to infer possible program states, variable ranges, and data properties without executing the program. Reports potential runtime errors including out-of-bounds accesses, null dereferences, type inconsistencies, division by zero, and integer overflows. Use when analyzing code for potential runtime errors, performing static analysis, checking safety properties, or verifying program behavior without execution.

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Identifies and analyzes conflicts in software requirements including logical contradictions, technical incompatibilities, resource constraints, timeline issues, data conflicts, and stakeholder priority mismatches. Use when reviewing requirement sets, specifications, user stories, or project plans to detect conflicts that could block implementation or cause rework. Provides detailed conflict analysis with resolution strategies and impact assessment.

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Automatically generates change logs from git commits, patches, and pull requests. Use when preparing software releases, creating version summaries, or maintaining CHANGELOG.md files. Analyzes commit messages (including conventional commits), diff/patch files, and PR data to produce categorized Markdown change logs organized by type (Features, Bug Fixes, Breaking Changes, etc.). Ideal for release notes, version updates, and automated changelog maintenance.

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Full-stack autonomous math modeling agent for CUMCM (全国大学生数学建模竞赛). Reads the problem statement and data, iterates through research → coding → verification → LaTeX writing, and produces a publication-quality PDF paper. Use when the user provides a CUMCM problem and wants end-to-end automated modeling, coding, and paper generation.

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Monthly social media performance review for SMBs. Analyses post-level and account-level data from Instagram, LinkedIn, Facebook, or TikTok. Identifies what worked, what didn't, and why. Produces a client-ready report and specific recommendations that feed back into the content calendar. Accepts CSV exports, screenshots, or manual data input.

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Pixel Path turns any two places (optionally via waypoints) into a pixel-art fly-through of the real road. It routes the trip, bakes the real buildings, land cover, landmarks, hills and horizon from OpenStreetMap and elevation data, generates scenery art with RetroDiffusion at a chosen level of detail (sparse, moderate or detailed; estimated up front, quoted exactly for free, budget-capped; free placeholder art also works), builds a page where you zoom along the route and scroll to go faster or slower, and runs a self-check that screenshots the result. Use when someone asks for a "pixel path", a "pixel road trip", to "drive" or "fly" from X to Y in pixel art, or a retro first-person view along a real route.

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Analyze Excel/CSV files with DuckDB SQL via bash.

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。

Generate charts: select type, extract data, render image.

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。

查询A股行情、历史K线与复权、财务指标、公司新闻公告、概念及入选依据、行业分类与成分、解禁、回购、股东增减持、涨停炸板详情、龙虎榜席位、板块热力图、指数、宏观及A+H上市信息。用于股票数据查询、原始数据测试和数据源异常排查。

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。

Semi-automated fact-checking, source credibility evaluation, misinformation detection, prebunking, and evidence-ledger based claim verification. Use when a user asks to verify a claim, article, social post, screenshot, URL, quote, data point, compare sources, detect manipulation, evaluate source reliability, or generate an HTML fact-check card/share-safe correction. Supports text, URLs, screenshots/images, two-source comparison, and topic-level prebunking in Bulgarian, English, Russian, EU policy, health, science, and geopolitical contexts. Trigger phrases: "is this true?", "is this fake?", "fact check this", "compare these sources", "проверка на факти", "истина ли е", "дезинформация ли е", "сравни тези източници".

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Streams change data capture from PostgreSQL into Redpanda or Kafka using Redpanda Connect's postgres_cdc input, which reads the WAL through logical replication (pgoutput). Use when configuring postgres_cdc (formerly pg_stream), setting up wal_level=logical, replication slots, and publications, enabling an initial snapshot before streaming, or troubleshooting slot growth and WAL accumulation. Also covers Redpanda Enterprise destination features such as Iceberg Topics, Tiered Storage, and server-side Schema ID Validation, since postgres_cdc itself requires a Redpanda Enterprise license.

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Apex - Senior Product Marketing Manager & CSO specializing in B2B/B2C IT Products & SaaS. Use for Go-To-Market strategies, product positioning, growth funnels, IT product copywriting, SEO content strategy, or marketing analytics. Also responds to 'Apex' or /apex command.

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把凌亂的文件（會議逐字稿、多場會議混在一起的檔案、實驗／數據報告、論文、財報）變成可直接上台的設計感簡報的工作流。 Use this skill whenever the user wants to turn a document, meeting notes, transcript, report, spreadsheet, or raw data into slides / a presentation / a deck — 包括他們說「做成簡報」「整理成投影片」「幫我做一份簡報」「把這份報告變簡報」 「turn this into a presentation / slides / a deck」，即使沒有明說步驟。 也適用於「只有主題沒有素材」（如「幫我做一份 AI 發展趨勢的簡報」）——會先做主題研究再進入流程。 也適用於跨 session 銜接（「繼續做 xxx 的簡報」「接上次」）——會定位進度再銜接。 它會先清點內容並把敏感資料（姓名、薪資、競業條款、個資）攔進備援區、停下來等使用者確認範圍， 再把「有設計感」翻成具體色碼與版型規格、生成單一 HTML 簡報， 並對任何圖表用原始數據重算統計值、抓出原文寫錯的數字。 Also use it whenever the user needs figures or charts from a report recomputed from the source data rather than trusted blindly. 也支援 Keynote Magic Move 式的 Morph Transition 轉場（使用者說「Magic Move」「Morph」「物件飛過去的轉場」時）。

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DyNote: systematically and efficiently extract raw Douyin/DY video data and analyze videos, comments, accounts, hashtags, and short-video scenes into evidence-graded learning notes, summaries, research briefs, scripts, and knowledge-base material. Use when the user asks to 抓取/提取/整理 抖音视频字幕、视频文案、ASR 转写、Qwen3-ASR 中文转写、原始材料归档、学习笔记、analysis plan、note budget、避免返工、复用已有素材、评论洞察、账号分析、赛道/话题研究、竞品拆解、电商/本地生活视频分析、事实核查、自动搜索素材, save Douyin content as Markdown/TXT, or use subtitle/local ASR as the factual spine with logged-in Douyin Web built-in AI / Doubao fallback as visual or quick-reading supplements.

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Use when the user drops or points at a local talking-head video and wants the kinetic supers + rapid camera reposition prompt for video-to-video AI (one real take turned into split-second camera snaps that lock off, with bold motion-graphics text synced to the speech). Triggers on /kinetic-multicam, "kinetic supers", "kinetic typography video", a dropped .mp4 plus a motion-graphics-text + camera request. PT examples for reliability, "faz o prompt de kinetic supers desse vídeo", "gera supers cinéticos com cortes de câmera", "bota motion graphics e câmera nesse take".

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Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

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。