「tool」の検索結果

A comprehensive toolkit for AI agents operating under budget constraints.

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Search Deutsche Bahn train connections using the bahn-cli tool.

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Use when you need this project's coding standards (line length, quotes, import order, naming, commit format) — derive them from the REAL tooling config as a pointer + digest, never a guessed claim.

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Use when the user shares a Sentry URL, says "check Sentry", or wants to investigate production errors. Uses Sentry MCP tools for deep analysis.

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Use when PHPStan, Rector, or ECS output appears — "phpstan says mixed", type errors, "fix code style", "run rector" — even when Eloquent/Laravel/model code is also mentioned.

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Use when building an MCP server in Python (FastMCP) or Node/TypeScript (MCP SDK) — agent-centric tool design, input schemas, error handling, and the 10-question evaluation harness.

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Use when working with MCP (Model Context Protocol) servers — their tools, capabilities, and best practices for effective agent workflows.

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Use when creating, merging, splitting, filling, or extracting from a PDF — library-per-task, output validated. Triggers on 'merge these PDFs', 'fill this PDF form', 'split the PDF', 'create a PDF'.

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Use when creating a Laravel migration — table prefixes, column naming, multi-tenant awareness, php artisan make:migration. Other stacks: use stack-native migration tooling.

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When the user says 'refactor this', 'rename class', or 'move method'. Safely refactors code in any language — finds all callers, updates downstream dependencies, verifies via quality tools.

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Use when creating or modifying Artisan commands. Covers clear signatures, safe execution flow, helpful output, and project conventions for console tooling.

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Use when spawning a task()/agent()/workpool() subagent with an outputSchema, to reduce yield-tool retry failures by stating the exact result envelope and required fields in the task prompt.

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Use for any question about a codebase, its architecture, file relationships, or project content — especially when graphify-out/ exists, where the question should be treated as a graphify query first. Turns any input (code, docs, papers, images, videos) into a persistent knowledge graph with god nodes, community detection, and query/path/explain tools.

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Use when invoked as $new-project or when creating a brand-new repository or project from scratch — scaffolds mise-pinned tools, a just task surface, the standard repo files, and CI for Python (uv), TypeScript (bun), Rust (cargo), or Go.

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Retrieve current documentation, API references, and code examples for any library, framework, SDK, CLI tool, or cloud service — including well-known ones like React, Next.js, Prisma, Django, or Spring Boot. Use for API syntax, configuration options, version migrations, setup instructions, and library-specific debugging, even when you already think you know the answer: training data on API details and signatures is frequently outdated. Prefer this over web search for library documentation.

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This skill is for interface design — dashboards, admin panels, apps, tools, and interactive products. NOT for marketing design (landing pages, marketing sites, campaigns).

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[AI & Tools] Use when you need to apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification.

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[Quality] Use when you need to research and configure code quality tooling for any tech stack — linters, formatters, static analysis, pre-commit hooks, and CI gates.

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[User-Invoked] Use ONLY when the user explicitly types /dual-ai or /dual-ai <workflow-id> — fans out one prompt, or one workflow invocation per tool, to two fresh parallel AI sessions (Claude Code + Codex CLI), both pre-set to xhigh reasoning effort and full-permission mode before the prompt executes. NEVER auto-activate.

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[AI & Tools] Use when you need create, verify, or enhance Claude Code custom agents (Claude Code custom agent files).

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Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.

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Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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Systematically find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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Build AI agents with LangChain framework. Use when building agents, tools, memory, MCP integrations, RAG pipelines, multi-agent systems, or any LLM-powered applications using LangChain or LangGraph in Python or TypeScript.

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。