Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
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概要と使いどころ
Hybrid search combining semantic and keyword retrieval for RAG pipelines. Implement BM25 + dense vector search with fusion strategies.
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。
Pinecone vector database setup, configuration, and operations for RAG applications
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。
Milvus distributed vector database configuration for large-scale RAG applications
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。
Chroma local vector database setup and operations for development and production
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。
LangChain memory integration including ConversationBufferMemory, ConversationSummaryMemory, and vector-based memory
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。
Security vulnerability assessment identifying OWASP risks, injection vectors, authentication issues, and data exposure with severity classification.
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。
SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.
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。
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
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Use when a task needs an app on the user's own screen — a native desktop application (instrument control software, Origin, GraphPad, ImageJ, a reference manager, a spreadsheet), or a desktop browser window that needs window-level control. Read before the first `computer` call. Covers the observe-act-observe loop, why element indexes go stale, which action to prefer for which control, what "unverified" means and why it must never be reported as success, and the recovery for each error code. Not for page-level web automation (use the browser connector) and not for anything that can be done from the shell.
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。
Use this skill whenever an Agent needs to query, find, install, deploy, update, tag, adopt, diagnose, or safely remove Skills Hub content, including natural-language requests to manage Skills for Codex, Claude Code, Cursor, or another Agent. Always use the Skills Hub CLI workflow instead of editing Skill directories or the database.
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。
Implement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.
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。
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
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。
Analyzes multiple provided pages to identify keyword overlap and potential cannibalization issues. Suggests differentiation strategies. Use PROACTIVELY when reviewing similar content.
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。
Combine vector and keyword search for improved retrieval. Use when implementing RAG systems, building search engines, or when neither approach alone provides sufficient recall.
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。
Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Orchestrates context across multi-agent workflows, enterprise AI systems, and long-running projects with 2024/2025 best practices. Use PROACTIVELY for complex AI orchestration.
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。
Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.
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。
Use when working on multiple branches simultaneously, context switching without stashing, reviewing PRs while developing, testing in isolation, or comparing implementations across branches - provides git worktree commands and workflow patterns for parallel development with multiple working directories.
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郑希观点库——基于易方达基金经理郑希 2012–2026 年全部公开观点原文语料,外加从语料蒸馏、有本人原话佐证的郑希投资方法的可溯源 research skill。能做: (1) 溯源问答——他怎么看 AI算力/光通信/新能源/半导体/ROE 等,引用其原话作答; (2) 讲解他的投资方法/框架/选股逻辑;(3) 前瞻应用——用他的方法分析当下任意主题/行业/个股,语料没谈过也能据框架推演; (4) 风格化点评——用他季报/手记的口吻写市场点评、季度展望; (5) 言行对照——用他全部基金真实数据(季度持仓/净值/业绩/规模/任职回报)核对"说的"与"买的",或答他的业绩/重仓/规模; (6) 全市场查询对比——内置约 2.7 万只基金列表,按需抓任意基金真实数据做查询或与郑希对比; (7) 郑希框架评分——给一只基金按他的方法打分(多像郑希会买的)。 When the user mentions 郑希/易方达郑希/zhengxi, asks his view on a sector/stock/theme, his 投资方法/框架/选股/风格/持仓/业绩/净值/规模, wants to apply his approach, a commentary 用郑希口吻, to check words vs holdings, or to look up/compare/score ANY China mutual fund (任意基金/某基金经理/同类对比/给基金打分)——use this skill, even if they don't say "skill", even if the topic isn't in his corpus (fall back to his method). 引用忠于原文、不杜撰;推演与原话区分。研究学习辅助,非投资建议。
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。
Korean and French screenwriting practice (韩国/法国编剧方法) — from the international masterclass; Oh Seung-uk (Christmas in August, The Shameless; field research in disguise, "write the emotion", first act and last act first, spaces as skeleton, reading to hostile listeners, surviving producers), Choi Seok-hwan (King and the Clown; genre as promise to the audience, "story = deepening crisis + intensifying desire", two reversals and bittersweet endings, the producer/director/writer trio), and Frédéric Auburtin (Paris je t'aime; dialogue last, Italian-style table reads, "12 lines not 13", dignity for characters, originals born from commissions, editing by emotion). Use when working in a Korean-style commercial drama or genre film, a French auteur/commission context, when planning field research, when a producer relationship is the problem, or when you need the Korean "three live together" development model or the French "emotion first, structure second" editing approach.
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。
Use when the user gives a topic and wants an automated topic-driven narrated explainer, podcast, or knowledge-summary video (Bilibili / YouTube / Xiaohongshu / Douyin / WeChat Channels), or asks to learn visual design patterns from a reference video/image. Trigger when the user mentions creating a knowledge video, narrated explainer, video podcast, or animated infographic-style video from a topic — even if they don't say "video podcast" explicitly. Also trigger when the user wants to regenerate, re-render, rebuild, update, or iterate on a narrated video this skill already produced — e.g. they edited the script/prompt, changed the visuals, or swapped the background music and want the final video remade (reuse the existing videos/{name}/ directory, never start a new project). Do NOT trigger for generic video editing, trimming, format conversion, color grading, or non-narrative video tasks. Produces 4K video via research → script → TTS → Remotion → MP4 + BGM.
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。
Assesses whether and how an existing mobile product should migrate to React Native. Use when auditing one or more product repositories for migration readiness, including products whose iOS, Android, and other clients live in separate directories or repositories; choosing brownfield, greenfield, or a checkpoint-based path; defining a representative trial; or preparing a baseline and ROI decision before implementation. When product scope or material evidence is unavailable, grills the stakeholder with exactly one question per turn instead of sending a questionnaire.
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Fetch and analyze WeChat Official Account hot articles / 公众号爆款文章 by sector or keywords. Use when the user asks for 爆款文章分析, 赛道爆款, 公众号爆款数据, reading counts, likes, shares, comments, title patterns, writing style, 爆款原因分析, or writing references for content creation.
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。
为公众号文章创作打造的专业数据洞察工具。全网持续收录每日爆款文章,收录依据:低粉高阅读、阅读靠前、数据增长中、原创靠前,助力号主获取数据参考。
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。
Run the full R package release gate — regenerate docs, run the test suite, run R CMD check --as-cran, and triage every ERROR / WARNING / NOTE against CRAN policy before a release or submission. Use when the user says "check my R package", "R CMD check", "is this package CRAN-ready", "run devtools::check", "prepare for CRAN submission", or points at a directory containing a DESCRIPTION file. Produces a check report + CRAN-submission checklist in `quality_reports/`.
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。