2,265 件 ・ 関連度順
概要と使いどころ
Search an index of public repositories, GitHub issues, merged pull requests, repository READMEs, and curated documentation sites. Use when a programming question needs external documentation or upstream evidence.
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。
Splits a coding task into independent parallel agent workstreams with disjoint write sets, explicit inputs and outputs, dependency ordering, and a merge/review plan. Use when several subtasks can proceed without shared mutable state. Not for tightly coupled edits, one failing root cause, or parallel changes to the same files.
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 production voice bots and IVR with Python STT/TTS pipelines. Use when designing telephony, streaming audio, latency budgets, or voice quality monitoring.
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。
Designs provider runtimes for coding agents. Use when modeling model abstraction, streaming semantics, tool-call normalization, retries, or fallback routing.
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。
Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.
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。
Train one of AgentDB's 9 RL algorithms on a stream of episodes. Use when the user has accumulated successful/failed episodes and wants to derive a policy, or when a task type is repeated enough to benefit from RL routing.
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。
Architecting crawl and scraping systems: distributed crawler topology, URL frontier, politeness, compliance. Architecture-only. Not for single-page scraping (Vector) or ETL pipelines (Stream).
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。
Extracting and structuring design context from Figma via MCP Server for downstream implementation agents. Use for Figma-to-code bridging or Code Connect management.
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。
Query TikTok Shop store (seller) details, retrieving a complete store profile by sellerId with total sales, multi-period (1d/7d/30d/90d) sales and GMV, followers, rating, review count, positive feedback rate, delivery rate, response rate, in-store product count, promoting creator count, promotional video count, livestream count, price range, product categories, and estimated listing time. Trigger when users mention TikTok store detail, TikTok seller detail, TikTok store analysis, TikTok store data, TikTok store profile, TikTok Shop store detail, TikTok seller detail, Nexscope store profile. Even if the user does not explicitly mention "Nexscope" or "TikTok", trigger this skill whenever their need involves querying the full detail/profile of a specific TikTok Shop store (with a known sellerId).
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 public TikTok commerce livestreams through Nexscope.
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。
Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.
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 update STRATEGY.md. Use when starting a product, changing direction or roadmap, or when ce-ideate, ce-brainstorm, or ce-plan need upstream product grounding.
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。
Structures git workflow practices. Use when making any code change. Use when committing, branching, resolving conflicts, splitting uncommitted work in a messy working tree into clean atomic commits, opening or reviewing a pull request (PR), pushing to a remote, or when you need to organize work across multiple parallel streams. Use when cutting a release, choosing a semantic version bump, tagging, or writing a changelog.
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。
Condense any input — an idea, brief, PRD, transcript, or mixed notes — into a short spec: one kernel file plus supporting files that downstream skills build from. Also updates and validates existing specs. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec"
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, play, queue and download media with the user's Opal player through the opal MCP server. Use when the user asks to watch, play, find, queue, pause, skip or download a movie, show, anime, video, song or stream.
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。
Distill any intent input into the SPEC kernel + companions — the canonical, preservation-validated machine contract for downstream work. Use when the user says "create a spec", "distill this into a spec", "validate this spec", or "update the spec".
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。
Advanced and operational chat.agent capabilities for Trigger.dev, loaded on demand. Load this when working on the raw Sessions primitive (sessions / SessionHandle), a custom chat transport or the realtime wire protocol, durable sub-agents (AgentChat, chat.stream.writer), human-in-the-loop, steering, actions, background injection (chat.defer / chat.inject), fast starts (preload, Head Start via @trigger.dev/sdk/chat-server), context resilience (compaction, recovery boot, OOM, large payloads), chat.local run-scoped state, offline testing with mockChatAgent, or prerelease/version upgrades. For the everyday chat.agent({...}) definition and the useTriggerChatTransport happy path, use the trigger-authoring-chat-agent skill 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。
Git worktree lifecycle management. Use when team needs parallel isolated workspaces for multi-stream development, or when an experiment must not touch the main working tree. Creates isolated workspaces, manages branches, handles cleanup. Called by team and cook.
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。
Multi-agent meta-orchestrator. Use when task spans 5+ files or 3+ modules, or when user says 'parallel', 'split this up', 'do all of these'. Decomposes large tasks into parallel workstreams, assigns to isolated cook instances, coordinates merging.
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, normalize, and maintain the user's Career Context file so downstream platform outputs stay factual and consistent. Use when the user wants an agent to consolidate CV data, LinkedIn exports, GitHub history, project summaries, bio facts, achievements, or positioning into one professional source of truth before editing platform-specific assets.
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。
Remix v2 performance, streaming, caching, and server/client boundaries. Use when configuring HTTP caching, server-only modules, hydration safety, or prefetch. Triggers on headers export, Cache-Control, PrefetchPageLinks, Link prefetch, .server.ts, .client.ts, useHydrated, ClientOnly, window.ENV, links preload, useId.
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 Phoenix LiveView code for lifecycle patterns, assigns/streams usage, components, and security. Use when reviewing LiveView modules, .heex templates, or LiveComponents.
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。
Configure LLM providers, use fallback models, handle streaming, and manage model settings in PydanticAI. Use when selecting models, implementing resilience, or optimizing API calls.
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。
Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.
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。