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概要と使いどころ
Reviews Node.js runtime behavior in JavaScript or TypeScript, including server entrypoints, route handlers, middleware, streams, processes, dependencies, and environment configuration. Loads alongside language and CLI checks when applicable; investigates blocking I/O, backpressure, injection, lifecycle leaks, and misleading failure signals.
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 LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.
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Creates a GitHub milestone on the current repository and returns its number for downstream use. Use when starting a new sprint, release, or theme grouping of backlog items. No args triggers guided intake (title, due date, description); 'quick {title}' skips to description only. Checks for duplicates before creating. Returns milestone number for /group-items-to-milestone.
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。
ACCESS-GATED beta. Deepline Monitors are provider event feeds (job posts, email replies, funding, intent) that stream into your warehouse and trigger plays. Only use if you have monitor access: run `deepline monitors status` first; if it reports no access, do NOT use this recipe — tell the user to contact the Deepline team.
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。
Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.
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。
Reads model uncertainty from token log-probabilities. Covers collecting logprobs and aggregating multi-token labels, confidence tiers and margins for triage, calibration assessment with ECE, Brier scores, and reliability diagrams against human labels, responsible downstream use of confidence, and reproducibility archives. Use when the user asks about classifier confidence by item, logprobs, top-k tokens, calibration, ECE, Brier, reliability diagrams, or routing low-confidence cases to human review. Codebook and validation design go to text-classification.
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。
Reads a model's own uncertainty from token log-probabilities: collecting and aggregating label logprobs, confidence tiers and margins for triage, calibration against human labels (ECE, Brier, reliability diagrams), downstream use, and reproducibility. Use for per-item classifier confidence or routing low-confidence cases to human review; codebook and validation design go to text-classification.
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。
Java coding standards for Spring Boot services: naming, immutability, Optional usage, streams, exceptions, generics, and project layout.
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。
Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
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。
Critical Akka.NET best practices including EventStream vs DistributedPubSub, supervision strategies, error handling, Props vs DependencyResolver, work distribution patterns, and cluster/local mode abstractions for testability.
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。
Guide a PACK-X author through the SPF fill cycle 01-11. Calls R28 Diagnostician to select mode (assembly/hybrid/full SPF) and leads through phases. Protects the read-only upstream invariant via PreToolUse hook.
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 AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.
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。
Persist AI chat conversations to Neon Postgres with full support for AI SDK message parts including tools, reasoning, and streaming. Uses UUID v7 for chronologically-sortable IDs.
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。
Live real-time dashboard for monitoring AI agent swarms with smart completion detection, clickable agent detail views, idle time tracking, and activity streaming. Auto-launches a beautiful web UI with capybara-inspired colors.
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 AI agents using the Cloudflare Agents SDK. Load when creating agents with persistent state, scheduling, RPC, MCP servers, email handling, or streaming chat. Covers Agent class, AIChatAgent, state management, and Code Mode for reduced 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。
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 copilots, assistants. Triggers: agent component, agent ui, chat agent, shadcn agent, react agent," agentic ui, ai assistant ui, copilot ui, inference ui, human in the loop
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Central authority for Claude Agent SDK (TypeScript and Python SDKs). Covers SDK installation, authentication (Anthropic key, Bedrock, Vertex), sessions and resumption, forking sessions, streaming vs single mode, custom tools, permissions (allowedTools, disallowedTools, permissionMode), MCP integration, system prompts (CLAUDE.md, appendSystemPrompt, outputStyle), cost tracking, todo tracking, structured outputs, hosting patterns, plugins, and SDK branding guidelines. Assists with building custom agents, configuring SDK options, and troubleshooting SDK issues. Delegates 100% to docs-management skill for official documentation.
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 conversational AI agents using Vercel AI SDK + OpenRouter. Use when creating Next.js frontends with streaming UI, tool calling, and multi-provider support.
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 a 10-component messaging library from your positioning + ICP + competitor aggregate. Produces positioning statement, value propositions, key differentiators, taglines, CTA variants, proof points, messaging hierarchy, status-quo alternatives. Writes to marketing/messaging/messaging.md as the canonical messaging library every downstream content / paid / outbound / lifecycle skill reads. Triggers - "product messaging", "messaging library", "value props", "messaging framework", "build messaging", "messaging components"
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。
Extract visual identity from screenshots (primary) or website URL (fallback) and compile into a DESIGN.md-format brand kit — YAML token frontmatter (colors, typography, rounded, spacing, components) + 8 ordered prose sections. The YAML tokens are machine-authoritative; downstream visual skills consume them directly. Writes to marketing/brand/brand-kit.md. Triggers - "brand kit", "brand identity", "visual identity", "design tokens", "extract brand", "DESIGN.md"
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。
Synthesize multiple per-competitor research files (produced by /competitor-research) into a single canonical threat matrix. Writes to marketing/competitors/aggregate.md as the locked canonical version every downstream skill reads. Triggers - "competitor aggregate", "synthesize competitors", "competitor matrix", "threat matrix", "lock competitor canonical"
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 structured ICP documentation by scraping case studies, testimonials, and solutions pages from your website. Produces firmographics, champion + economic buyer personas, pain points, decision criteria, anti-ICP, customer proof points, and voice-of-customer synthesis. Writes to marketing/icp/ICP.md as the canonical ICP every downstream skill reads. Triggers - "icp research", "ideal customer profile", "persona research", "customer segments", "who are our customers", "build ICP"
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。
Develop positioning strategy with anchor, differentiators, status-quo alternatives, and a positioning statement. Reads from marketing/icp/ICP.md + marketing/competitors/aggregate.md. Writes to marketing/positioning/positioning.md as the canonical positioning every downstream skill reads. Triggers - "positioning", "category definition", "how do we position", "positioning strategy", "anchor + differentiators"
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。
Maintain upstream OAuth identity-provider directive parsers, shared configuration dispatch, OAuth/OIDC discovery, GitHub identity claims, JWKS and static public PEM verification, EdDSA/Ed25519 token validation, key refresh, and real portal OAuth E2E tests. Excludes portal signing-key publication and gatekeeper-owned direct OAuth sessions.
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。