「stream」の検索結果

Check for and apply updates to installed skill branches from upstream.

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。

Check for and apply updates to installed skill branches from upstream.

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。

Efficiently bring upstream NanoClaw updates into a customized install, with preview, selective cherry-pick, and low token 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。

Integrate Convex static self hosting into existing apps using the latest upstream instructions from get-convex/self-hosting every time. Use when setting up upload APIs, HTTP routes, deployment scripts, migration from external hosting, or troubleshooting static deploy issues across React, Vite, Next.js, and other frontends.

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。

Integrate Convex static self hosting into existing apps using the latest upstream instructions from get-convex/self-hosting every time. Use when setting up upload APIs, HTTP routes, deployment scripts, migration from external hosting, or troubleshooting static deploy issues across React, Vite, Next.js, and other frontends.

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。

Azure AI VoiceLive SDK for Java. Real-time bidirectional voice conversations with AI assistants using WebSocket. Triggers: "VoiceLiveClient java", "voice assistant java", "real-time voice java", "audio streaming java", "voice activity detection java".

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。

Azure AI Agents Persistent SDK for .NET. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Use for agent CRUD, conversation threads, streaming responses, function calling, file search, and code interpreter. Triggers: "PersistentAgentsClient", "persistent a...

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。

Azure AI Agents Persistent SDK for Java. Low-level SDK for creating and managing AI agents with threads, messages, runs, and tools. Triggers: "PersistentAgentsClient", "persistent agents java", "agent threads java", "agent runs java", "streaming agents java".

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。

Batteries-included agent component for React/Next.js from ui.inference.sh. One component with runtime, tools, streaming, approvals, and widgets built in. Capabilities: drop-in agent, human-in-the-loop, client-side tools, form filling. Use for: building AI chat interfaces, agentic UIs, SaaS...

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。

Detects and prevents code injection attacks targeting serverless functions (AWS Lambda, Azure Functions, Google Cloud Functions) through event source poisoning, malicious layer injection, runtime command execution, and IAM privilege escalation via function modification. The analyst combines static analysis of function code, CloudTrail event correlation, runtime behavior monitoring, and IAM policy auditing to identify injection vectors across the expanded serverless attack surface including API Gateway, S3, SQS, DynamoDB Streams, and CloudWatch event triggers. Activates for requests involving Lambda security assessment, serverless injection detection, function event poisoning analysis, or serverless privilege escalation investigation.

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。

Rebase onto latest upstream, checking for soft conflicts

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。

Educational single-threaded TypeScript vanity address generator for Solana using @solana/web3.js with async iteration, event-loop yielding, streaming generator API, and best-effort memory zeroization.

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。

Comprehensive Lean Six Sigma consulting skill supporting all belt levels (White Belt to Master Black Belt). Use this skill for DMAIC/DMADV project execution, Lean waste elimination (VSM, 8 Wastes/DOWNTIME, 5S), statistical analysis (process capability Cp/Cpk, control charts, hypothesis testing), and Six Sigma training/education. Triggers include "improve process", "reduce defects", "sigma level", "DMAIC project", "value stream mapping", "Kaizen", "process capability", "control chart", "root cause analysis", "5 Whys", "Fishbone diagram", "FMEA", "DOE", or requests involving process improvement methodologies.

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。

Translate a fuzzy analytical question into a rigorous investigation plan. Interrogates the ask, grounds the plan in the available data dictionary, applies analytical best practices, and produces a structured brief of query specifications for a downstream query-writing skill. Plans, does not write SQL. Use when: "why did X drop", "is there a relationship between A and B", "who are our highest-value customers", "what's driving the change in Y", "investigate this trend", "design an analysis for", "scope this analytical question". (narrative-common)

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 connecting AI SDK for Python streams to AI SDK UI useChat clients.

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 for AI SDK for Python basics. Configure a model, make messages, stream, declare tools, build a basic agent.

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 a language-agnostic ghost package (spec + portable tests) from an existing repo by extracting SPEC.md, exhaustive tests.yaml (operations and/or scenarios), INSTALL.md, README.md, VERIFY.md, and upstream LICENSE files with provenance and regeneration instructions. Use when prompts say "/tw-ghost", "ghostify this repo", "spec-ify/spec-package this library", "ghost library", or ask to extract portable spec/tests for libraries or tool-using agent loops (scenario testing); do not use for implementation work or editing skills.

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 a local codebase and infer the architecture it is actually using, including repo kind, best-fit dominant architecture, directly evidenced coexisting patterns, major subsystem exceptions, confidence, docs-vs-code drift, and repo-fit hints for downstream agents. Use when prompts ask what architecture a repo uses, whether it is really hexagonal or just layered, what subtle patterns shape a repo or slice, how a target slice fits a hybrid monorepo, whether documented architecture matches implementation, or when `/tw-tk` needs a repo-dialect preflight before making a minimal change.

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。

Load when building .NET ATProtocol applications with CarpaNet source generators, XRPC queries, Jetstream subscriptions, OAuth 2.0 DPoP, or Lexicon JSON models.

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。

Load for Blazor BFF server work involving YARP proxy routes, cookie sessions, access-token forwarding/refresh, downstream API calls, BFF handlers, or browser-to-API auth failures; not for client component rendering or API JWT validation alone.

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。

Load when the user asks for CTO advice on feature strategy, architecture direction, build-vs-buy, infrastructure, market positioning, roadmap, self-hosting, cost, or organizational tradeoffs; not for reviewing an existing implementation-plan workstream.

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。

Author a completed-spec fixture — a seeded intent graph plus a believable four-phase interview transcript — for a brunch feature, so it can feed any downstream tooling that consumes a completed spec or dogfood the UI. Use when you need a real completed specification in .brunch/brunch.db for testing or demo: pick a feature from memory/SPEC.md (or a free description), choose a stress profile (coverage|clean), and produce a registered scenario seed + an interview-questions doc that match exactly.

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。

Integrating TanStack Router with TanStack Query: queryClient in router context, ensureQueryData/prefetchQuery in loaders, useSuspenseQuery in components, defaultPreloadStaleTime: 0, setupRouterSsrQueryIntegration for SSR dehydration/hydration and streaming, per-request QueryClient isolation.

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。

Run an extremely strict maintainability review for abstraction quality, giant files, and spaghetti-condition growth. Use for a thermo-nuclear code quality review, thermonuclear review, deep code quality audit, or especially harsh maintainability review.

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。