1,011 件 ・ 関連度順
概要と使いどころ
Investigate a bounded Proto UI Contract, Prototype, Module, Host Capability, Adapter, schema, or ownership question and turn a genuinely unresolved direction into one semantic decision packet. Use for spec brainstorming, admission questions, competing semantic models, uncataloged behavior, or unresolved cross-layer boundaries.
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。
Work with any Upstash product, SDK, or tool, including serverless Redis (caching, sessions, leaderboards, key-value storage), Ratelimit (rate limiting and throttling), QStash (message queue, cron schedules, background jobs), Workflow (durable long-running functions), Vector (vector database for embeddings, semantic search, and RAG), Search (full-text and semantic search), Box (sandboxed cloud containers for AI agents, TypeScript/JavaScript and Python), Blob (serverless S3-compatible file storage with direct browser uploads), the Upstash CLI, and no-signup scratch Redis for agents. Use when the user mentions Upstash or needs any of these capabilities in a serverless, edge, or Node.js app.
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。
Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
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。
Work with the @upstash/search TypeScript/JavaScript SDK, a serverless full-text and semantic search database with built-in reranking. Use when adding search to an app or site, creating a search index, upserting documents with searchable content and filterable metadata, running keyword, semantic, or hybrid search queries, reranking results, filtering with SQL-like or structured filter syntax, paginating with range, fetching or deleting documents, resetting an index, or checking index info. Also use when the user asks for site search, product, document, or knowledge-base search, or a managed search service that needs no cluster to run.
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。
Search and explore an Obsidian vault through the NoteBrain CLI (semantic search, tags, backlinks, connections, hidden links, boosted retrieval). Use it whenever the user mentions their notes, knowledge base, Obsidian vault, semantic search, finding connections or unlinked notes, or asks exploratory questions like "what do I know about X", "find notes related to Y", "what connects to Z", or "summarize my notes on W" — even when they never say NoteBrain, vector search, or ChromaDB.
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。
Create reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.
Analyzes git commit history and generates professional changelogs with semantic versioning, conventional commit support, and multiple output formats (Keep a Changelog, Conventional, GitHub). Use when editing CHANGELOG.md, CHANGELOG.txt, or HISTORY.md files, preparing release notes, creating releases, bumping versions, updating changelog, documenting changes, writing release notes, tracking changes, version bump, tag release, or when user mentions "changelog", "release notes", "version history", "release", "semantic versioning", or "conventional commits".
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。
Idiomatic modern C++: RAII, smart pointers, move semantics, value semantics, STL algorithms, and safe concurrency. Use when writing, reviewing, or structuring C++ code.
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。
Transform reference photographs and visually dense images into sparse, recognizable, high-saturation flat-vector-style illustrations through semantic compression rather than literal tracing. Use when the user asks for photo-to-flat illustration, photo-to-vector-style redraw, 照片转扁平插画, 无渐变高饱和插画, iconification, visual simplification, composition-preserving abstraction, a consistent minimal illustration set, or revisions that remove gradients, glow, texture, blur, clutter, text, logos, numbers, or UI. Default to human-perceived semantic complexity 6 out of 10 or lower, fixed-HSB solid fills, crisp boundaries, and no gradients or light-halo effects.
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。
Discovers requirements and generates architectural, design, and deployment guidance for dynamic hybrid search systems by combining semantic search and keyword search. Optimized for AlloyDB hybrid search use cases in Google Cloud. Use when users need vector search combined with structured SQL filtering, faceted attributes, semantic reranking, in-database AI validation, or serverless hosting across transactional relational databases, analytical data warehouses, or managed database engines. DON'T use this skill for simple keyword-only search, or when a standalone non-relational vector database is required.
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。
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.
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。
Design and review durable REST, GraphQL, RPC, or event-facing API contracts. Use for public or shared API contracts, endpoint/schema consistency, compatibility, pagination, error semantics, and API documentation. Discovers existing consumers and conventions before recommending a style.
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。
Ranks skill matches by fit, performance history, and contextual relevance. Applies multi-factor scoring including success rate, resource usage, and task alignment. Activate on 'rank skills', 'best skill for', 'skill ranking', 'compare skills', 'optimal skill'. NOT for semantic matching (use dag-semantic-matcher) or skill catalog (use dag-skill-registry).
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。
Semantic image-text matching with CLIP and alternatives. Use for image search, zero-shot classification, similarity matching. NOT for counting objects, fine-grained classification (celebrities, car models), spatial reasoning, or compositional queries. Activate on "CLIP", "embeddings", "image similarity", "semantic search", "zero-shot classification", "image-text matching".
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 executable BDI reasoning with explicit negation, paraconsistent revision, trigger-based commitment updates, and abduction. Use for runtime agent semantics and conflicting desires. NOT for purely axiomatic modal logic, black-box planners, or classical logic without operational semantics.
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 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.
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。
Find the papers that answer a research query in Firecrawl's research paper index — a corpus of paper abstracts whose largest share is biomedical and life-science literature (PubMed, bioRxiv, medRxiv), alongside arXiv preprints in CS, physics, and math — using semantic search, semantic and structural expansion, and in-body verification. Use this skill for literature-finding and paper-retrieval tasks of any kind, including clinical, biomedical, drug, gene, disease, and other life-science questions, whether the answer is a single paper or a full multi-paper set. The index is reached only through the `firecrawl_research_*` MCP tools or the `firecrawl research` CLI subcommands. Calling `firecrawl_search` with its `categories` option set to `["research"]` is a different feature — it filters ordinary web search to research-affiliated websites (the list includes PubMed, bioRxiv, medRxiv, arXiv, and publisher sites) and returns page results from them, without querying the paper records in this index.
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。
The original reddapi.dev Reddit search skill (vector search, semantic search, trends, subreddit discovery), no Reddit OAuth or app registration needed. This is the same engine now packaged as reddit-research with added market-research playbooks and a fuller pitch on semantic vs keyword search; reddapi is kept live under its original name for existing installs and works standalone. Use when the user says 'reddapi' by name, or wants a minimal drop-in Reddit search skill without the extra research-workflow guidance. For the expanded research-oriented version with query playbooks, see reddit-research. For B2B lead scoring, see reddit-leads. For a bare API reference, see reddit-search-api.
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。
Apply semantic HTML/JSX and WAI-ARIA correctly and minimally. Apply when implementing any UI, especially forms, interactive components, or when accessibility is mentioned.
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。
Search PubMed biomedical literature with natural language queries powered by Valyu semantic search. Full-text access, integrate into your AI projects.
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。
spec-drift issue が起票され diff トリアージが必要なとき、C11 が再構成した issue 単位の完全 diff を hunk 化し name/type/required/enum/semantics 各軸の影響を before/after/evidence 付きで判定して triage-report を出したいときに使う。
Semantic + keyword search and connection-discovery across the user's own Apple Notes via the apple-notes MCP server. Use when the user wants to find, recall, or synthesize something from their notes, or surface non-obvious bridges/related notes. macOS, on-device.
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。
Erstellt und verwendet einen anbieterneutralen semantischen Routing-Graphen aus Personas, koordinierenden Rollen, Experten und aktiven Skill-Endpunkten. Verwenden, wenn ein LLM eine Anfrage von einer Leitrolle über einen Experten zu einem Skill routen, aus einem vorhandenen Agentensystem einen portablen Persona-Router extrahieren, eine semantische Domänenkarte mit einer lexikalischen Skill-Registry kombinieren oder fehlende Verbindungen zwischen Rolle und Skill sichtbar machen soll, statt still auf einen Ersatz zurückzufallen. Auslöser sind unter anderem semantisches Persona-Routing, Persona-Umbrella, Rollen-Router, Boss-Agent-Experten-Skill-Routing, Agentenrollen-Export oder die Wiederverwendung von Personas über mehrere LLM-Anbieter hinweg.
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。
Diagnose or enable Session Cartographer semantic search in Codex, including least-privilege localhost access to local Qdrant and the embedding server. Use when setup, Qdrant health, localhost reachability, sandbox network denial, or semantic indexing is in question.
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。