157 件 ・ 関連度順
概要と使いどころ
Build recommendation systems with collaborative filtering, matrix factorization, hybrid approaches. Use for product recommendations, personalization, or encountering cold start, sparsity, quality evaluation issues.
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 performance optimization with CPU, memory, caching, bundle size. Use for slow workers, high latency, cold starts, or encountering CPU limits, memory issues, timeout errors.
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。
Specialized skill for building production-ready serverless applications on GCP. Covers Cloud Run services (containerized), Cloud Run Functions (event-driven), cold start optimization, and event-driven architecture with Pub/Sub. Use when ", gcp, cloud-run, serverless, containers, pubsub" 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。
Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization. Use when ", aws, lambda, serverless, api-gateway, dynamodb" 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。
Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models. Use when "azure function, azure functions, durable functions, azure serverless, function app, azure, serverless, functions, durable-functions, cloud" 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。
Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " 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。
You are an expert in Deno Deploy, the globally distributed serverless platform by Deno. You help developers deploy TypeScript/JavaScript applications to 35+ edge locations with zero cold starts, built-in KV storage, BroadcastChannel for real-time, cron scheduling, and npm compatibility — running code within milliseconds of users worldwide without managing 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。
Cloudflare Workers is an edge computing platform for running JavaScript, TypeScript, and WebAssembly close to users worldwide with sub-millisecond cold starts. Use when working with the Workers runtime, the Wrangler CLI, KV, D1, R2, Durable Objects, Queues, or Hyperdrive, or when a user asks to build and deploy an edge function.
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。
You are an expert in Cloudflare Workers AI, the serverless AI inference platform running on Cloudflare's global network. You help developers run LLMs, embedding models, image generation, speech-to-text, and translation models at the edge with zero cold starts, pay-per-use pricing, and integration with Workers, Pages, and Vectorize — enabling AI features without managing GPU 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。
This skill should be used before answering "does Genesis have X", "does Genesis lack X", auditing Genesis capabilities, comparing Genesis to an external system, or reviewing/summarizing the architecture. It routes to the canonical judgment-layer subsystem map so audits start from the map, not from a cold grep. Also fires after changing a subsystem's capabilities, to keep the map current.
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。
Marketing playbook for open-source projects led by non-technical founders. Covers README optimization, community building, contributor attraction, and translating OSS traction into revenue. By @WeiYipei.
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 a state document (context transfer) that allows any fresh AI instance to resume work without conversation history. Use this skill whenever the user asks to checkpoint, create a state transfer, save state, generate a handoff document, create compaction insurance, or says anything like 'snapshot this', 'create a context transfer', 'checkpoint', 'save state for handoff', 'write a recovery doc', or 'transfer context'. Also trigger when the user mentions compaction concerns, session handoff, multi-session continuity, or wants to preserve the current conversation's state. Works for any conversation type: projects, research, debugging, investigations, design sessions, or long-running chats. This skill produces a structured markdown document optimized for AI cold-start recovery, not human notes.
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。
账号被限流/异常/担心操作触发风控/需要恢复时用：先自检from:@用户名搜不到即限流→区分临时标签(等24h不动账号)与正式标签(写申诉邮件+48h不操作)→复盘触发原因预防复发。Trigger："推文搜不到了""被限流了""账号异常""触发风控""临时标签""怎么申诉""shadowbanned""account suspended""appeal suspension""tweets not showing up"。不适用：正常发内容没出问题(→内容创作skill)、新号开业装修养号(→x-account-setup)、没粉丝怎么起量(→x-cold-start-playbook)。
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。
规划变现路径/判断够不够格开创作者收益/决定卖什么产品时使用。三层金字塔定位：底层流量型(商单+广告分成,需500蓝V+3月5M曝光)、中层产品型(课程/付费专栏/带货)、顶层服务型(咨询/高端社群,利润率>80%)。IP路线可叠三层,泛流量卡底层。Trigger："怎么变现""够不够格开收益""卖什么产品""monetize""creator revenue""what to sell"。不适用：没粉丝的冷启动(→x-cold-start-playbook)、选IP还是泛流量(→x-positioning-tradeoff)、开蓝V/装修(→x-account-setup)。
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。
Runs CloudWatch Logs Insights queries via AWS SDK for JavaScript v3. Analyzes Lambda cold starts, API Gateway latency, and ECS container logs. Generates anomaly detection alarms with math expressions.
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 scraping JavaScript-heavy pages or SPAs, crawling multiple URLs concurrently, extracting structured data with reusable CSS/JSON schemas, or building automated web data pipelines. Wraps the Crawl4AI library (`crwl` CLI and Python SDK) with schema-generation patterns for LLM-free extraction. Triggers on crawl4ai, crwl, scrape JS-heavy site, scrape SPA, headless browser scrape, schema-based extraction, batch crawl, sitemap crawl, web data pipeline. SKIP when a static HTML page can be read with `defuddle` / `fetch-web` — those are faster cold-start and don't need a browser.
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。
Assess and improve garden soil through testing, amendment, composting, and biodynamic preparations. Covers jar test, spade test, earthworm count, amendment by soil type (clay, sandy, depleted, compacted), composting methods (hot, cold, vermicomposting), no-till practices, cover cropping, and biodynamic preparations 500-508. Use when starting a new garden bed, when plants underperform despite adequate water and light, when transitioning to organic or biodynamic practice, when soil has become compacted or depleted, or when building a composting system.
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 Therapeutics Data Commons through the PyTDC Python package for registry discovery, approved dataset access, task-aware splits (scaffold, cold-start, temporal, combination), evaluator metrics, benchmark groups, and bounded molecular-oracle workflows. Use this skill to find which TDC datasets exist for a therapeutic task, load them with a split that does not leak, score predictions with the task's own official metric rather than a generic one, and run benchmark groups reproducibly. Also trigger on PyTDC, Therapeutics Data Commons, tdc.single_pred, tdc.multi_pred, ADMET Benchmark Group, scaffold split, get_split, or molecular oracles such as GSK3B, JNK3 and DRD2.
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 guide + benchmarked pitfalls for building Monte-Carlo / Simulated Annealing refinement layers on top of greedy hex-grid layouts for code visualization (packages/gui/src/store/hexLayout.ts, sandbox/hex-sandbox, future Rust ports). Use when: (1) planning to add MC/SA on top of an existing hex-grid pack, (2) deciding whether a simpler SA would match a hierarchical MC with force-directed + chain drag, (3) budgeting wall-clock for million-node cold-start, (4) debugging "blob turns Swiss-cheese" or "folders stop nucleating" during MC refinement, (5) choosing between folder cohesion and raw Σ-link-length as cost function. Contains four hard-won, quantified lessons and anti-patterns that would take a full day of experiments to rediscover.
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 hybrid recommendation systems combining multiple strategies for improved accuracy. Use this skill when the user needs to overcome single-method limitations, combine collaborative and content-based filtering, or build a production recommendation pipeline — even if they say 'combine recommendation approaches', 'best recommendation architecture', or 'cold start plus personalization'.".
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 content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based 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。
Profile and audit mobile app performance -- cold/warm/hot startup time, memory leaks, battery drain, network efficiency, frame rate jank, and binary size. Triggers: diagnosing slow launches, investigating ANRs, reducing app size, or benchmarking rendering 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。
Read-only repository explorer. Use PROACTIVELY for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives. Skip it when the issue already names the exact file or symbol, or a previous turn already returned usable file:line evidence. Returns only compact path:line citations; its reads and greps never enter the main conversation.
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。
End-of-session pass that persists durable facts from the CURRENT session into .claude/memory/. Runs IN-SESSION in the main agent (never via Task — a subagent starts cold with no transcript), prompted by the Stop hook's nudge. Triggers on "persist durable facts from this session", "run memory-extraction", "flush session memory", or "extract memory before close". Reads the in-context transcript plus existing memory, applies the durable-fact predicate below, and writes one file per fact plus one MEMORY.md index line — append-or-update, never clobbering a consumer's existing 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。