501 件 ・ 関連度順
概要と使いどころ
Comprehensive Cloudflare platform skill covering Workers, Pages, storage (KV, D1, R2), AI (Workers AI, Vectorize, Agents SDK), networking (Tunnel, Spectrum), security (WAF, DDoS), and infrastructure-as-code (Terraform, Pulumi). Use for any Cloudflare development task. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Builds remote MCP (Model Context Protocol) servers on Cloudflare Workers with tools, OAuth authentication, and production deployment. Generates server code, configures auth providers, and deploys to Workers. Use when: user wants to "build MCP server", "create MCP tools", "remote MCP", "deploy MCP", add "OAuth to MCP", or mentions Model Context Protocol on Cloudflare. Also triggers on "MCP authentication" or "MCP deployment". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 sandboxed applications for secure code execution. Load when building AI code execution, code interpreters, CI/CD systems, interactive dev environments, or executing untrusted code. Covers Sandbox SDK lifecycle, commands, files, code interpreter, and preview URLs. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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 web performance using Chrome DevTools MCP. Measures Core Web Vitals (FCP, LCP, TBT, CLS, Speed Index), identifies render-blocking resources, network dependency chains, layout shifts, caching issues, and accessibility gaps. Use when asked to audit, profile, debug, or optimize page load performance, Lighthouse scores, or site speed. Biases towards retrieval from current documentation over pre-trained knowledge.
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。
Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling". Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Reviews and authors Cloudflare Workers code against production best practices. Load when writing new Workers, reviewing Worker code, configuring wrangler.jsonc, or checking for common Workers anti-patterns (streaming, floating promises, global state, secrets, bindings, observability). Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.
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。
Extracts Airbnb accommodation search results from a destination query via SSR-embedded data, returning listing ID, URL, name, coordinates, rating, price, photos, and badge info for each result, plus pagination cursors for multi-page retrieval. Use when user mentions Airbnb search results, Airbnb listings, vacation rental search, short-term rental listings, scrape Airbnb, get Airbnb data, find rentals on Airbnb, Airbnb destination search, Airbnb property list, Airbnb stays search, Airbnb accommodation results, pull Airbnb listings, collect Airbnb search data, Airbnb scraper, Airbnb search page extraction, Airbnb search by destination.
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。
Develop, adapt, review, test, and troubleshoot Nexent external memory provider plugins, including plugin.yaml manifests, searchable and ingestible provider protocols, error mapping, network-isolated unit tests, deployment configuration, and Mem0-based examples. Use when adding a new external memory vendor, changing an existing memory plugin, validating partner adapters, or diagnosing plugin discovery, authentication, retrieval, ingestion, or CI failures.
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。
Max-3-cycle protocol for agent sub-tasks with WHY context and coordinator validation. Use when spawning sub-agents to complete scoped work.
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。
Access 2,000+ AI models and API tools through one MCP interface for inference, media generation, search, scraping, embeddings, social data, and structured retrieval. Use sandbase_discover before building custom integrations or declaring external data inaccessible; prefer an existing dedicated tool or API key when the user already has one.
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。
Hunt vector-store / embedding-layer weaknesses in RAG pipelines (OWASP LLM08 Vector and Embedding Weaknesses) — persistent corpus poisoning that survives across sessions and users (distinct from one-shot indirect prompt injection, which is owned by hunt-llm-ai), cross-tenant vector-database IDOR (unauthenticated or unscoped queries against Pinecone/Weaviate/Chroma/Milvus/Qdrant/pgvector), source-text/metadata leakage in similarity-search results, and retrieval-hijack via adversarial embedding proximity ('SEO poisoning' for RAG). Targets: any app with a shared knowledge base, document upload feeding a chatbot, or a directly reachable vector-DB port. Validate: a second, clean session/account must inherit a poisoned result, or a cross-tenant artifact must be independently verifiable — confabulation is not a finding, same bar as hunt-llm-ai. Use when target is RAG-backed, exposes a vector-DB port, or lets users upload documents that other users' queries later retrieve.
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。
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.
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。
Research Coordinator v12.0 - Human-Centered Edition (Systematic Review Automation) Context-persistent platform with 24 specialized agents across 9 categories (A-G, I, X). Features: Human Checkpoints First, VS Methodology, Paradigm Detection, Systematic Review Automation. Supports quantitative, qualitative, mixed methods research, and systematic review automation. Language: English. Responds in Korean when user input is Korean. Triggers: research question, theoretical framework, hypothesis, literature review, meta-analysis, effect size, IRB, PRISMA, statistical analysis, sample size, bias, journal, peer review, conceptual framework, visualization, systematic review, qualitative, phenomenology, grounded theory, thematic analysis, mixed methods, interview, focus group, ethnography, action research, paper retrieval, AI screening, RAG builder, humanization, AI pattern detection
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。
This skill should be used when the user asks to "search for academic papers", "find working papers on SSRN", "look up a DOI", "download paper PDF", "check open access availability", "search NBER papers", "find arXiv preprints", or "search CrossRef". Provides unified search, metadata retrieval, and PDF download across arXiv, NBER, SSRN, CrossRef, OpenAlex, Unpaywall, and Semantic Scholar. Covers economics, finance, social science, CS/AI, and all academic disciplines.
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。
Use when the user asks Codex to research, find learning materials, process "素材：" links, "请你读" / "精读" a material, build a material radar, or use multi-source information retrieval for career learning and Agent infra tracking. Do not use the career/Agent-infra routing bias for user-directed `整理笔记` into a named note.
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。
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
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。
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For broader biological evidence lookup across databases, use bio-database-evidence. Use this for direct HTTP/REST work or UniProt-specific control.
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。
Generate comprehensive market research reports and industry/competitive analysis in the style of top consulting firms. Use for market sizing, competitive landscape, market entry, investment thesis, strategic recommendations, and consulting-style business reports. Do not use for scientific reports, paper writing, LaTeX submission/PDF builds, EDGAR/FRED/Treasury/Data Commons data retrieval, or biomedical literature evidence work.
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。
Local evidence retrieval (FlashRAG-style) for VCO/vibe: search protocols/config/skills docs and return citeable snippets with file+line anchors.
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。
Teach the user a topic as an adaptive tutor — retrieval practice, spaced repetition with decay, and persistent memory in ~/.drill-me/. Use when the user wants to learn or be drilled on something, says "drill me on X", "teach me X", or wants to study a topic, a codebase, or a document.
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。