1,113 件 ・ 関連度順
概要と使いどころ
Translate a polished markdown plan into self-contained beads in .beads/beads.jsonl. Treat this as a distinct translation problem, not task extraction. Use after /plan-synthesize produces a stable hybrid plan.
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 persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases.
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。
AI-powered conversion rate optimization: landing page audits, CRO scoring, survey segmentation, and lead magnet generation. Use when asked to audit a landing page, run a CRO analysis, segment survey responses, or generate lead magnets. For content quality scoring see content-ops.
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。
AI-powered revenue intelligence: sales call insight extraction, content-to-revenue attribution, and multi-source client reporting. Use when asked to analyze sales calls, build revenue attribution models, or generate client reports.
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。
Estimate the full development cost of an existing codebase from lines of code, architectural complexity, and team-composition overhead. Use for 'how much would this cost to build', 'what did this codebase cost', development-cost or build-cost estimates, calendar-time estimates, and Claude/AI ROI on a delivered codebase. Estimates by measured LOC and complexity, not by ticket volume.
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 data pipelines, ETL jobs, event processors, message-broker producers/consumers, application caching layers, database migrations, BigQuery queries, or event-sourcing handlers in TypeScript. Triggers on edits to indexer/ETL/migration code, Prisma or Drizzle schemas, BigQuery integration code, RabbitMQ/Kafka/SQS/BullMQ producer or consumer code, Redis cache wrappers, or mentions of "data engineering", "ETL", "pipeline", "indexer", "event sourcing", "data warehouse", "data migration", "message queue", "RabbitMQ", "Kafka", "SQS", "BullMQ", "Redis cache", or "caching".
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 writing, reviewing, or architecting Rust code — systems programming, async services, CLI tooling, web backends, or any work in `.rs` files. Triggers on editing `.rs` or `Cargo.toml` files, or mentions of "Rust", "Tokio", "Axum", "cargo", "borrow checker", "lifetime", "trait object", "async Rust", "crate", "rustc", or explicit requests to "build this in Rust" / "rewrite X in Rust". For adversarial security review of Rust code see security-reviewer.
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 games or interactive software in Phaser 3 with TypeScript — scenes, gameplay code, input, Arcade physics, animations, audio, tweens, asset preloading, save systems, performance work, or scaffolding with Vite. Triggers on "Phaser", "Phaser 3", "Phaser.Scene", "Phaser.Game", "Arcade physics", "Matter physics", "tilemap", "Tiled", "GameObject", "Container", "Group", "Vite + Phaser", or any `.ts`/`.js` file in a Phaser project. For game design and balance see game-systems-designer, game-balancer; for game intake see game-design-shaper. For Godot/C# see godot-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。
Use when implementing analytics with PostHog in a TypeScript app — capturing events, identifying users, adding feature flags, tracking errors, or wiring API lifecycle telemetry. Triggers on edits to analytics integration files, or mentions of "PostHog", "analytics", "feature flag", "event tracking", "capture", "identify", "A/B test", or "experiment".
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。
Catalog APIs, persistence models, and message/event payloads into DATA_MODEL.md at the project root. Use after implementation when a diff touches request/response types, schemas, ORM models, queue payloads, or webhook shapes. Triggers on "document data model", "update DATA_MODEL", "catalog API shapes".
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 writing or reviewing TypeScript backend tests — Jest unit tests for services/controllers (mocked Prisma) or Supertest integration tests against a real isolated PostgreSQL test database. Triggers on edits to `*.service.test.ts`, `*.controller.test.ts`, `*.integration.test.ts`, service/controller/API test files (`.ts`, not `.tsx`) under `**/__tests__/`, or mentions of "backend test", "service test", "API test", "database test".
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。
AI-powered SEO operations: keyword intelligence, competitor gap analysis, Google Search Console optimization, and trend detection. Use when asked to research keywords, analyze competitor content gaps, audit GSC performance, or detect trending topics. For growth experiments see growth-engine; for content optimization see autoresearch.
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 Karpathy-style autoresearch optimization on any content. Generates 50+ variants, scores with a 5-expert simulated panel, evolves winners through multiple rounds, outputs optimized version + full experiment log. Use when optimizing landing pages, email sequences, ad copy, headlines, form pages, CTA text, or any conversion-focused content. Triggers on "optimize this page", "run autoresearch", "score these variants", "A/B test this copy".
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。
A-Corp Foundry — the coordination engine for agentic companies. Register, learn the system, and discover specialized 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。
TDD Green Phase - Implement minimal code to make the failing test pass
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。
TDD Red Phase - Activate ONE test from the test list and make it fail with explicit predictions
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。
Fornece orientação para treinamento RL agentico nativo do PyTorch usando torchforge, biblioteca da Meta que separa infraestrutura de algoritmos. Use quando você quer abstrações limpas de RL, fácil experimentação de algoritmos, ou treinamento escalável com Monarch e TorchTitan.
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 Tavily integrations with best practices baked in. Reference documentation for developers using coding assistants (Claude Code, Cursor, etc.) to implement web search, content extraction, crawling, and research in agentic workflows, RAG systems, or autonomous agents.
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。
Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: E2E testing, web research, automat...
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。
Produce publication-style figures from MIKE+ .res1d results via the mike-plus MCP server — a stacked rainfall-runoff hydrograph, a single time series, a network map, a baseline-vs-scenario overlay, or a longitudinal profile (bed / crown / ground / max water level along a pipe string), in the house style (Arial 12, ticks inward, SI units, no title). Use when an agent must render MIKE+ results as a PNG without opening the GUI. No license required.
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。
Standard operating procedure for an auditable, headless MIKE+ workflow via the mike-plus MCP server — how to plan, copy, run, QA-gate, compare, plot, audit, and safely stop. Use FIRST whenever an agent is handed a MIKE+ model (.sqlite) or result (.res1d) plus a plain-language goal; it picks the operating mode and tells the agent which mike-* skill to open next and in what order.
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。
Navigating Sparse Evidence: Agentic Visual RAG via Explicit Context Selection and Consolidation Soochow University ; Baidu Inc.
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。
interview the user to design an agentic control loop (sensor, controller, actuator under disturbances) tailored to their codebase, then build it as locally-runnable components plus a scheduled coding-agent workflow
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。