1,131 件 ・ 関連度順
概要と使いどころ
Diagnose whether a Codex Desktop update changed codexhost Composer/CDP bindings, private Renderer DOM or React state, Host bridges, routing, the Codex usage submission gate, or injected UI. Use after an installed Codex update or when compatibility regresses and the affected surface is unknown.
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。
Submit feedback on an Expo skill—or Expo itself—and control bundled anonymous usage telemetry (off by default / opt-in). Submit feedback with: npx --yes submit-expo-feedback@latest "ACTIONABLE_FEEDBACK". Optionally add either or both: --category "CATEGORY" and --subject "SUBJECT". Replace the uppercase placeholders before running. Use when a skill was useful, confusing, broken, missing context, or worth improving; when Expo, Expo CLI, EAS CLI, docs, or MCP worked well or fell short; when an AI agent repeatedly failed, got stuck, or needed the user to take over an Expo task (report it as an eval candidate); or when the user explicitly asks to enable or disable telemetry (tracking), check its status, or understand what it collects.
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。
Analyze and control AssemblyAI model, feature, duration, retry, concurrency, and LLM Gateway spend using measured usage. Use when setting budgets or investigating anomalies. Trigger with "AssemblyAI cost", "AssemblyAI billing", or "reduce AssemblyAI spend".
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。
Analyze and reduce avoidable Attio integration cost and load by measuring request volume, query complexity, synchronization churn, and retained data before changing architecture. Use when an Attio workload is wasteful or operationally expensive. Trigger with "Attio cost", "reduce Attio API usage", or "Attio sync efficiency".
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。
Manage Apollo.io API upgrades and endpoint migrations. Use when upgrading Apollo API versions, migrating to new endpoints, or updating deprecated API usage. Trigger with phrases like "apollo upgrade", "apollo migration", "update apollo api", "apollo breaking changes", "apollo deprecation".
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。
Optimize AppFolio API costs through efficient usage patterns. Trigger: "appfolio cost".
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 production-ready Apollo.io SDK patterns. Use when implementing Apollo integrations, refactoring API usage, or establishing team coding standards. Trigger with phrases like "apollo sdk patterns", "apollo best practices", "apollo code patterns", "idiomatic apollo", "apollo client wrapper".
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。
Optimize Claude API performance with prompt caching, model selection, streaming, and latency reduction techniques. Use when experiencing slow responses, optimizing token usage, or reducing time-to-first-token in production. Trigger with phrases like "anthropic performance", "claude speed", "optimize claude latency", "anthropic caching", "faster claude responses".
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 Anthropic Claude API rate limiting, backoff, and quota management. Use when handling 429 errors, optimizing request throughput, or managing RPM/TPM limits across usage tiers. Trigger with phrases like "anthropic rate limit", "claude 429", "anthropic throttling", "claude retry", "anthropic backoff".
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 policy guardrails, input/output validation, and usage governance for Claude API integrations. Trigger with phrases like "anthropic guardrails", "claude content policy", "claude input validation", "anthropic safety rules".
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。
Optimize Apify platform costs through memory tuning, compute unit management, and proxy budgeting. Use when analyzing Apify billing, reducing Actor run costs, or implementing usage monitoring and budget alerts. Trigger with "apify cost", "apify billing", "reduce apify costs", "apify pricing", "apify expensive", "apify budget", "compute units".
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。
Optimize Apify Actor performance: crawl speed, memory usage, concurrency, and proxy rotation. Use when Actors are slow, consuming too much memory, or being blocked by target sites, or when a crawl is too expensive per run. Trigger with "apify performance", "optimize apify actor", "apify slow", "crawlee concurrency", "apify memory tuning", "scraper 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。
Analyze application logs for performance insights and issue detection including slow requests, error patterns, and resource usage. Use when troubleshooting performance issues or debugging errors. Trigger with phrases like "analyze logs", "find slow requests", or "detect error patterns".
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 or review a typed application adapter around the Algolia JavaScript v5 client. Use when client calls, index names, errors, task waits, and credential usage are scattered. Trigger with "Algolia SDK patterns", "Algolia client wrapper", or "refactor search client".
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。
Establish a deterministic local EVM development loop with pinned forks, synthetic fixtures, and bounded Alchemy usage. Use when adding local chain tests or repairing flaky fork-based development. Trigger with "Alchemy local dev", "Hardhat fork with Alchemy", or "pin a mainnet fork".
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。
Govern Alchemy compute usage and spend with current account evidence, workload attribution, budgets, and reversible optimizations. Use when forecasting or reducing Alchemy cost. Trigger with "Alchemy cost", "Alchemy compute units", or "reduce Alchemy spend".
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。
Reduce Adobe integration spend using contract, invoice, usage, queue, storage, and retry evidence instead of invented per-call prices. Use when the task requires adobe workload cost and waste review. Trigger with "reduce Adobe cost", "Adobe spend review", or "PDF transaction budget".
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 the Appllama MCP (mcp.appllama.io) well — research real top-grossing mobile apps, their screens, flows, and UI elements, then build from what you learn. Load when the Appllama MCP is connected and the task involves building a mobile app or screen, researching app design patterns, studying onboarding/paywall/feature flows, improving an existing screen, or whenever an appllama_* / search_apps / list_app_screens tool is available. Covers the tool map, pagination, expiring media, and the full build-from-research playbooks.
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 ONLY for abstract DataChain SDK questions — API usage, method signatures, or code patterns — when no specific dataset or bucket is referenced. If the request mentions creating, saving, listing, exploring datasets or buckets, use datachain-knowledge instead.
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。
LLM integration patterns including API usage, streaming, function calling, RAG pipelines, and cost optimization
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。
draw.io diagram creation, editing, and review. Use for .drawio XML editing, PNG conversion, layout adjustment, and AWS icon usage.
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 products and pricing around validated willingness to pay, from Ramanujam & Tacke's "Monetizing Innovation". Use when the user mentions "pricing", "how much should we charge", "willingness to pay", "pricing page", "packaging", "freemium vs free trial", "are we leaving money on the table", "nobody buys at this price", "price increase", or "good-better-best". Also trigger when designing or auditing pricing and packaging, validating willingness to pay before building, segmenting customers by value, or choosing between subscription, usage-based, and freemium models. Covers price-before-product, willingness-to-pay talks, the four failures (feature shock, minivation, hidden gem, undead), leader/filler/killer packaging, and behavioral pricing. For offers and guarantees, see hundred-million-offers. For what customers value, see jobs-to-be-done.
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。
Master context engineering for AI agent systems. Use when designing agent architectures, debugging context failures, optimizing token usage, implementing memory systems, building multi-agent coordination, evaluating agent performance, or developing LLM-powered pipelines. Covers context fundamentals, degradation patterns, optimization techniques (compaction, masking, caching), compression strategies, memory architectures, multi-agent patterns, LLM-as-Judge evaluation, tool design, and project development.
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。
Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a components.json file. Also triggers for "shadcn init", "create an app with --preset", or "switch to --preset".
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。