「build」の検索結果

Use when designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.

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。

Practical incident-response runbook for a suspected or confirmed ComfyUI / GPU-daemon compromise. Triage, scope, contain, forensic collection, weight-integrity verification, rebuild from clean snapshot, postmortem. NOT for hygiene scans on a healthy box (use supply-chain-defense-for-ml-extensions), and NOT for runtime hardening of a fresh deployment (use gpu-hosted-asset-hardening).

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。

Blue-green and canary deployment orchestrator with traffic shifting and automated rollback. Activate on: blue-green deployment, canary release, rolling deployment, traffic shifting, rollback automation, progressive delivery, Argo Rollouts, Flagger. NOT for: K8s manifest generation (use kubernetes-manifest-generator), CI/CD pipeline setup (use github-actions-pipeline-builder), monitoring (use monitoring-stack-deployer).

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 designing a background-job system, choosing between BullMQ / Sidekiq / RQ / Temporal / SQS, deciding queue-vs-workflow, sizing concurrency vs rate limits, building dead-letter queues, or making handlers idempotent. Triggers: jobs running twice on retry, lost jobs after worker crash, DLQ filling up, Redis OOM from job backlog, exactly-once requested, "do we need Temporal?", visibility timeout / lockDuration confusion, exponential backoff vs jitter, fan-out fan-in workflows. NOT for outbound webhook publishing (different concerns), receiver-side webhook handling (different concerns), event-streaming/Kafka topology, or in-process async (event loop only).

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。

Track the full Toulmin epistemic ancestry of every claim produced by a multi-agent swarm. Each agent output is tagged with an argument chain (claim / data / warrant / relationship) derived from SwarmTracer spans and LineageEdge records, so operators can zoom into any sub-claim and see precisely which agents asserted it, what triggered each assertion, and how contradictions propagated or were synthesised away. The SwarmTracer pattern — wrapping the SwarmAgentExecutor to capture spans, record messages, and build a lineage graph — is the concrete runtime substrate. Argumentative lineage lifts that graph into the Toulmin vocabulary so reasoning provenance is both machine-queryable and human-interpretable.

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。

App Store and Google Play submission automator with Fastlane, screenshot automation, metadata management, and TestFlight/internal testing. Activate on: app store submission, Fastlane, TestFlight, Google Play Console, screenshot automation, metadata management, app review, code signing, provisioning profiles. NOT for: CI/CD pipeline setup (use github-actions-pipeline-builder), app architecture (use react-native-architect), analytics (use mobile-analytics-crash-reporting-expert).

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 building content-heavy sites with Astro, deciding between SSG/SSR/hybrid, choosing client directives (idle/visible/load/media), structuring content collections with type-safe schemas, integrating React/Vue/Solid/Svelte components in the same project, or migrating from Next.js for marketing/docs. Triggers: client:idle vs client:load tradeoffs, content collections schema with zod, image optimization via @astrojs/image, view transitions, server islands, MDX layouts. NOT for SPA-style apps that need full interactivity (use Next/Remix), Astro internals/plugin authoring, or non-content sites where islands provide no benefit.

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 and build mathematically rigorous, high-density, interactive data graphics and streaming telemetry visualizations following Mike Bostock's D3.js and Observable methodologies. Use when crafting custom SVG/Canvas charts, reactive streaming time-series, multi-dimensional brush-and-link coordinates, small multiples, or topological swarm graphs. NOT for generic dashboard template clones, basic spreadsheet charts, or off-the-shelf low-effort wrapper libraries.

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 and build beautiful, accessible graphical interfaces — web, desktop (Electron/Tauri), and native (iOS/macOS/Android). Use for visual hierarchy and layout, color and theming (light/dark, semantic tokens, WCAG contrast), typography systems, motion and micro-interactions, accessibility, component systems and design tokens, responsive/adaptive layout, and platform-native idioms. The GUI counterpart to beautiful-cli-design. NOT for terminal/CLI output (use beautiful-cli-design) or API/data schemas.

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 audio transcription pipelines with Whisper, Deepgram, and AssemblyAI including speaker diarization and real-time streaming. Activate on: transcription, speech-to-text, diarization, audio processing, meeting transcripts. NOT for: text-to-speech synthesis (voice-audio-engineer), music generation (ai-engineer).

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。

Plan or audit the cross-layer Harbor cooperative editor: Loro replicas for humans and agents, governed edit claims, salvage authority, and transport across shared, LAN, and remote harbors. Use when a change spans editor collaboration, identity, recovery, or transport contracts. For a single GPUI pane use gpui-rust-console; for motion use rust-gpui-motion; for a shader use gpui-shaders. NOT for non-collaborative screens, web editors, or generic Rust 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。

Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered applications.

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-level idiomatic Rust API design — the patterns that make illegal states unrepresentable and abstractions zero-cost. Type-state programming (PhantomData state machines), newtype + sealed + extension traits, typestate builders, RAII guards & Drop ordering, the interior-mutability decision tree (Cell/RefCell/Mutex/RwLock/Atomic/OnceLock), enum-driven state machines, error architecture (thiserror vs anyhow), dyn vs generic dispatch & object safety, the impl Trait / GAT / RPITIT toolbox, and Deref-for-smart-pointers (and its abuse). Use when designing a Rust library's public API, encoding invariants in the type system, choosing a dispatch or error strategy, or reviewing Rust for idiom. NOT for borrow-checker firefighting / toolchain / test-runner workflow (use rust-development-workflow), NOT for pd-console GPUI rendering/layout/panes (use gpui-rust-console), NOT for app packaging/notarization (use rust-app-distribution).

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。

Data structures and serialization formats for agent-to-agent communication. Covers message envelopes, structured output schemas, capability declarations, task handoff payloads, error/retry signaling, and context windows as data structures. Compares the version-pinned A2A, MCP, and JSON-RPC role boundaries covered by this bundle; OpenAI and LangChain-specific APIs require their own current sources. Teaches when to use rigid schemas versus free-form content with validation, typed versus untyped boundaries, and streaming versus batch. Activate on: "agent message format", "agent communication schema", "agent-to-agent protocol", "A2A protocol", "MCP message format", "structured output for agents", "agent interop", "interchange format", "agent serialization", "task handoff format", "capability declaration". NOT for: what agents say to each other (use agent-conversation-protocols), orchestration topology (use multi-agent-coordination), building agent infrastructure (use agentic-infrastructure-2026).

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。

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use jury_rig-architect), specific tool implementation.

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 RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.

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。

Plan, evaluate, and adopt agent infrastructure for a named workload. Use for agent runtime/framework selection, agent evaluation or observability plans, memory and tool boundaries, infrastructure cost controls, and organizational adoption. Covers workload-first comparison, risk and recovery planning, held-out evaluation, cost accounting, and staged rollout. Not a vendor ranking or a production certification. NOT for implementing a specific agent behavior (agentic-patterns), building a specific agent (ai-engineer), prompt optimization (prompt-engineer), or conventional ETL workflow design (jury_rig-architect).

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。

Architecture and systems design for agents that retain bounded state across sessions. Covers durable event history, owned state, derived memory, retrieval indexes, framework evaluation, recovery, deletion, and rebuild behavior. Uses constructed implementation examples and workload calibration rather than universal vendor, latency, similarity, retention, or cost claims. Activate on: "always-on agent", "persistent agent architecture", "episodic memory system", "agent memory design", "long-running agent", "stateful agent", "agent that remembers", "MemGPT architecture", "Letta deployment", "/always-on-agent-architecture". NOT for: choosing what data to feed the agent (use always-on-agent-inputs), brainstorming applications (use always-on-agent-applications), safety and privacy concerns (use always-on-agent-safety), general agentic patterns (use agentic-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。

Build stateful Agents on Cloudflare Workers with current, source-verified SDK APIs. Covers durable state, RPC, chat, schedules, queues, fibers, Workflows, MCP, and channels while keeping authorization and external effects application-owned. NOT for assuming SDK helpers grant application authorization or effect approval.

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。

Time-blind friendly planning, executive function support, and daily structure for ADHD brains. Specializes in realistic time estimation, dopamine-aware task design, and building systems that actually work for neurodivergent minds.

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。

Set up GitHub Actions CI pipeline with test, lint, type-check, and build jobs for a Node.js/TypeScript project. Includes output grouping, error annotations, and job summary 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。

ScepticAgent packaging and Chrome Web Store publishing workflow. Use whenever the user wants to build the zip, package the extension, publish or submit to the Chrome Web Store, update the store listing, or deal with manifest/icon/permission requirements for publishing.

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 this skill when building applications with Gemini models, Gemini API, working with multimodal content (text, images, audio, video), implementing function calling, using structured outputs, or needing current model specifications. Covers SDK usage (google-genai for Python, @google/genai for JavaScript/TypeScript, com.google.genai:google-genai for Java, google.golang.org/genai for Go), model selection, and API capabilities.

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。

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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。