데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (8.9천건)
Refresh and update existing A-share investment presentation decks with latest data. Adapts the original deck-refresh skill for Chinese presentations, A-share data updates, and domestic market conventions. Triggers on "更新PPT", "刷新演示", "refresh deck China", "update presentation", "更新演示文稿", or "refresh slides [company]".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Institutional-quality equity research initiation reports for A-share companies. Covers company analysis, financial modeling, valuation, chart preparation, and final report assembly. Adapted from the original initiating-coverage skill for Chinese market conventions, CAS accounting, and AkShare data sources. Triggers on "A股首次覆盖", "首次评级", "initiate coverage China", "initiate on [company]", "A股研究报告", or "start research on [company]".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build professional data packs for A-share investment banking deals — M&A due diligence, IC materials, company profiles. Adapted from the original datapack-builder skill for Chinese data sources (AkShare, 巨潮, exchange filings) and CAS accounting. Triggers on "A股数据包", "投行数据包", "datapack China", "due diligence pack A-share", or "IC data pack [company]".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Populate A-share investment banking pitch deck templates with data from source files. Adapted from the original pitch-deck skill for Chinese companies, A-share market data, and Chinese business context. Triggers on "A股Pitch Deck", "路演PPT", "pitch deck China", "pitch deck A-share", or "fill pitch template for [company]".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comparable company analysis for A-share stocks. Uses AkShare MCP to build peer groups, pull financial data, compute valuation multiples (PE, PB, PS), and assess relative value within a Chinese industry sector.
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Clean and normalize A-share financial data for modeling. Adapts the original clean-data-xls skill for Chinese financial statements, CAS conventions, and A-share data formats. Triggers on "A股数据清洗", "财务数据清洗", "clean financial data China", "数据清洗", or "normalize financial statements".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Screen and evaluate A-share investment opportunities. Adapts the original deal-screening skill for Chinese market data sources, A-share screening criteria, and domestic deal origination. Triggers on "A股标的筛选", "标的筛选", "screening China", "deal screening A-share", "筛选投资标的", or "screen for [criteria]".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
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用于 XSS、SQL 注入、SSTI、SSRF、XXE、JWT、鉴权绕过、文件上传、请求走私、OAuth/OIDC、SAML 与原型污染等 Web 类 CTF 题;触发名:ctf-web
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用于对抗样本、模型提取、提示注入、成员推断、训练投毒、LoRA 滥用、LLM 越狱等 AI/ML 相关 CTF 题;触发名:ctf-ai-ml
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Strix SQL 注入测试手册,覆盖 union、blind、error-based 与 ORM 绕过技巧;触发名:strix-sql-injection
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Implement a playbook from its PROMPT specification files. Supports both single-framework (PROMPT-REACT.md, etc.) and 3-framework (PROMPT-3-FRAMEWORKS.md) modes.
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Drive the real Course Video Manager web app in a browser and capture proof of what it did. Use to verify a change before opening a PR, to reproduce a reported UI bug, to see what a page actually renders, or to capture a screenshot of app behaviour. Runs on a per-run, writable clone of a local copy of Matt's data by default — click anything that mutates; production is never written to.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows
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Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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SwiftUI's actual mental model — view identity, lifetime, and dependencies (the Demystify canon), state ownership decision rules, Observation's per-property tracking, body-performance discipline, and the main-actor concurrency contract. Use when state resets mysteriously, views re-render too often, animations glitch between branches, choosing @State vs @Bindable vs plain property, or debugging "why did body run."
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Generates user-facing usage statistics, activity summaries, and personalized insights dashboards (weekly recaps, year-in-review, Spotify Wrapped-style). Use when user wants to show usage stats, activity insights, or shareable recap screens. Different from analytics-setup which sends data to a backend — this shows insights to the USER on-device.
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Generates the complete win-back offer flow for churned subscribers — StoreKit Message API handling, eligibility verification, offer sheet presentation, and analytics. Use when implementing win-back campaigns or re-engagement for lapsed subscribers.
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Interpret app metrics and make data-driven decisions. Covers DAU/MAU, retention, LTV, ARPU, App Store Connect analytics, AARRR funnel analysis, cohort analysis, and diagnostic decision trees. Use when user wants to understand their metrics, diagnose problems, or build a data-driven growth plan.
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GeoToolbox PlaceDescriptor patterns with MapKit integration for location representation, geocoding, and multi-service place identifiers. Use when working with place descriptors, geocoding, or cross-service location data.
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Close the post-launch loop — turn a live app's App Store signals (reviews, analytics, sales, crashes, listing conversion) into a metric-tagged backlog for the next version, AND verify whether last cycle's changes moved the metric they promised to move. Read-only on App Store Connect; every change is surfaced and routed to another command, never auto-applied. Use before planning the next version, on a monthly cadence, or ~1-2 weeks after shipping to check if a change worked.
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Generates technical architecture specification from PRD. Covers architecture pattern, tech stack, data models, and app structure. Use when creating ARCHITECTURE.md or designing system architecture.
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Generates smart App Store review prompt infrastructure with configurable conditions and platform detection. Use when user wants to add review prompts, request ratings, or implement StoreKit reviews.
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Generate sample data and a multi-variant
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