272 件 ・ 関連度順
概要と使いどころ
Add projections to collections to rename fields or map nested JSON paths to flat column names. Use when renaming fields for destination conventions, flattening nested paths to columns, or creating field aliases. Use when user says "rename field", "flatten nested JSON", "map JSON path to column", "field alias", or "projection".
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 creation and validation of custom dotnet new templates. Generates templates from existing projects and validates template.json for authoring issues. USE FOR: creating a reusable dotnet new template from an existing project, validating template.json files for schema compliance and parameter issues, bootstrapping .template.config/template.json with correct identity, shortName, parameters, and post-actions, packaging templates as NuGet packages for distribution. DO NOT USE FOR: finding or using existing templates (use template-discovery and template-instantiation), MSBuild project file issues unrelated to template authoring, NuGet package publishing (only template packaging structure).
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。
Fastify (Node.js 高速 HTTP サーバーフレームワーク, v5) の API リファレンス。 `fastify.register`, encapsulation, `fastify-plugin`, `decorateRequest` / `decorateReply`, `onRequest` / `preHandler` / `onSend` フック, JSON Schema + Ajv, Type Providers (TypeBox / Zod), `fastify.inject`, `FST_ERR_*`, find-my-way ルーティング, fastify-cli, v4→v5 移行。
Claude API (platform.claude.com) の tool use / Agent Skills / MCP リファレンス。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, computer use, web search / web fetch, memory tool, tool search）, Agent Skills (SKILL.md), Skills API, MCP connector, mcp_servers, MCP tunnels, Tunnels API。
Query UniProt's REST API (post-2022 endpoint at rest.uniprot.org) for protein sequences, annotations, GO terms, cross-references, ID mappings, and proteomes. Use when fetching UniProtKB entries, navigating the JSON schema, choosing between UniProtKB/UniRef/UniParc/Proteomes resources, deciding stream vs search endpoint for batch retrieval, running ID-mapping jobs with the async pattern, handling isoform suffixes, or filtering reviewed Swiss-Prot vs auto-annotated TrEMBL. Encodes the legacy URL migration (2022), the new JSON schema layout, and bulk-pull 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。
docs/PLAN/{YYMMDD}_{slug}/ の schema v3 仕様書を Codex で Gate 単位に実行する。 tasks.json を正とし、Gate 契約 (Goal/Constraints/AC) に基づいて実装・検証・レビューを行う。 Trigger: spec-codex-run, spec-run
Designs robust function/tool calling schemas for LLMs with JSON schemas, validation strategies, typed interfaces, and example calls. Use when implementing "function calling", "tool use", "LLM tools", or "agent actions".
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。
Generate validated JSON-LD structured data markup for any page type
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。
MySQLとMariaDBのテーブル設計や遅いSQLを見直し、インデックス、更新処理、接続管理、複製の遅延を点検して、修正案と検証手順をまとめるスキル。
Kotlinでデータベースの検索・更新処理を実装するためのパターン集です。JetBrains Exposedのクエリから接続管理、構造変更、テストまで扱います。
Run one tick of the sales business-pod (ADR-164 §4.1, Phase 2). Loads templates/sales.json, validates it against the pod-schema, resolves agents against ruflo's agent registry, reserves budget via the Phase-2 file-based stub ledger (atomic SQLite tracker is Phase 3 per ADR-164.1), constructs per-agent dry-run prompts, posts a summary envelope to room "sales" via the federation_bbs_publish JSONL backing store, and emits a structured {podName, tickId, agentsRan, totalUsd, envelopeId, status} line for /loop ingestion. Dry-run by default; --live is reserved for Phase 3.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementa
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。
Detect, validate, or generate Schema.org JSON-LD for a supplied page or entity. Use only when structured data or rich-result markup is requested.
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。
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library
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。
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.
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 and deploy an MCP server from an OpenAPI / Swagger spec using the mcp-use TypeScript SDK. Use this skill whenever the user wants to "turn this OpenAPI spec into an MCP server", "make this API usable from Claude/ChatGPT", "wrap this Swagger doc as MCP tools", "expose this REST API to an LLM", "generate MCP tools from a spec", or pastes/attaches an `openapi.yaml`, `openapi.json`, or `swagger.json` and asks for a Claude-compatible version. Trigger even if the user doesn't say "MCP" — if they describe an existing HTTP API (REST endpoints, an internal service, a third-party API they have a key for) and want an LLM to call it, this is the right skill. Covers spec ingestion (file path, URL, or pasted), operation-to-tool mapping, auth wiring (apiKey, bearer, basic, OAuth bearer), scaffolding with `create-mcp-use-app`, tool generation with proper zod schemas, live testing in the mcp-use inspector, and deploying to Manufact / mcp-use cloud.
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。
Infer JSON structure and types with jq-based schema discovery.
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。
Manages Amazon DocumentDB end-to-end — serverless-on-8.0 cluster setup, TLS/VPC/driver config, flexible-schema and vector-search data modeling, MongoDB compatibility assessment, DMS-based migration, slow-query diagnosis, major version upgrades (4.0->5.0->8.0), Well-Architected reviews (41-check wa_review.py), cost estimation, and security hardening. Retrieve for every DocumentDB question and when the user asks to set up or migrate MongoDB to AWS — DocumentDB is AWS's MongoDB-compatible managed database. Triggers: JSON document store, document database, MongoDB on AWS, Nested fields, Lambda cannot connect, TLS handshake, VPC port 27017, IAM auth, Secrets Manager, encryption at rest, $graphLookup, flexible schema, COLLSCAN, compound index, DMS migration, CDC cutover, $vectorSearch, RAG, Global Clusters, DR replication, cost sizing, audit, health check, production-readiness.
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。
Generate Zod/JSON Schema configuration validators with defaults and error messages.
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。
Parse SQLite databases into structured JSON data. Use when exploring unknown database schemas, understanding table relationships, and extracting map data as JSON.
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。
Compress tool outputs, logs, and JSON before they enter the context window — structural compression via a deterministic stdlib script (schema + samples + stats instead of 300 raw rows), no API, no summarization loss. Use when asked shrink this tool output, my context is full of JSON, compress these logs before analysis, or stop wasting tokens on raw data. Produces the crushed artifact with its token math shown, the crush-or-keep decision rules, and the fetch-the-original escape hatch.
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 structured data matching a JSON schema from websites. Handles complex nested schemas, arrays, pagination, and validation. Always outputs via formatOutput.
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。