데이터 분석, 머신러닝, LLM 앱, 프롬프트 설계 (8.9천건)
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".
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Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.
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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]".
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DCF valuation model for A-share stocks using Chinese financial data. Uses AkShare MCP for financial statements, WACC inputs (risk-free rate from China government bond yields), and growth projections. Use instead of the original dcf-model skill for Chinese equities.
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Parse and extract data from Chinese identity documents for KYC (Know Your Customer) compliance. Adapts the original kyc-doc-parse skill for Chinese ID cards, business licenses, and domestic compliance requirements. Triggers on "A股KYC", "身份文件解析", "parse Chinese ID", "KYC document", "客户身份识别", or "extract [document type]".
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Comparable company analysis for A-share equities. Adapts the original comps-analysis skill for Chinese market data sources, A-share trading multiples, and domestic peer selection. Triggers on "A股可比公司", "可比分析", "comps China", "peer comparison A-share", "可比公司分析", or "trading comps [industry]".
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RAG-specific best practices for LlamaIndex, ChromaDB, and Celery workers. Covers ingestion, retrieval, embeddings, and performance.
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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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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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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.
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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 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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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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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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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 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 data export/import infrastructure for JSON, CSV, PDF formats with GDPR data portability, share sheet integration, and file import. Use when user wants data export functionality, CSV/JSON/PDF export, GDPR compliance data portability, import from files, or share sheet for data.
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Generates a minimum version enforcement system with hard-block and soft-prompt update flows, App Store redirect, and remote config or App Store lookup for version checks. Use when user wants force update, mandatory update, or minimum version check.
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