「claude」の検索結果

Enables ultra-granular, line-by-line code analysis to build deep architectural context before vulnerability or bug finding.

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Use when working with Anthropic Api — anthropic API platform management covering models, usage tracking, rate limits, and message analytics. Use when monitoring API usage and costs, analyzing model request patterns, reviewing rate limit health, or troubleshooting Anthropic Claude API issues.

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 one-off HTML artifacts (landing, deck, prototype).

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。

As a heavy Claude Code user, Simon estimates his own usage at the equivalent of 4,400 "typical queries" to an LLM, for an equivalent of around $15-$20

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AI agent system prompts cannot serve as the sole enforcement layer — guardrails must be implemented at the API gateway and token-permission level, not

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。

Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.

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This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

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Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

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Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.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。

Search previous Claude Code conversations for facts, patterns, decisions, and context using semantic or text search

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Guide for creating or updating effective portable skills with specialized knowledge, workflows, or tool integrations. Use for Codex, Claude Code, GitHub Copilot, or shared-catalog skill work after selecting the target host and installation path.

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Use OpenAI, Anthropic, Google Gemini, or OpenRouter models from Netlify Functions or Edge Functions without managing provider API keys or accounts — the gateway injects credentials automatically. Reach for this when you add an AI chatbot or completion endpoint, generate images or text with Gemini/GPT/Claude, summarize form submissions with AI, build an LLM-backed API route, stream a long AI generation, or wire up any server-side AI provider call on Netlify. Covers provider SDK setup, injected env vars, model availability, rate limits, credit costs, streaming for long generations, and local dev with netlify dev or the Vite plugin.

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Run AI agent tasks remotely on Netlify using Claude, Codex, or Gemini. Use when the user wants to run an AI agent on their site, get a second opinion from another model, or delegate development tasks to run remotely against their repo.

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Discover, validate, and invoke .agent.md custom agents. Use when finding agent files in the local Claude or VS Code Insiders directories, checking frontmatter, verifying disable-model-invocation, or determining agentName for runSubagent calls.

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Use multiple Claude agents to investigate and fix independent problems concurrently

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Architecte Agent IA — Conçoit et déploie des agents IA (Hermes, Claude Code, n8n, NVIDIA NIM). Expert en architecture multi-agents, orchestration, et intégration de workflows IA.

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Installs skills from aigb-skills into a coding agent's skill directory. Use when setting up aigb-skills for Claude Code, Cursor, Copilot, or Pi.

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Alternative Slowbar coffee intelligence — scientific analysis from seed to cup using First Principle + System Thinking. Covers: varieties, terroir, cultivation, disease diagnosis, processing, roasting physics/chemistry, extraction science, brewing, business strategy, storytelling. ALWAYS trigger for coffee-related questions requiring scientific depth. DO NOT trigger for simple recipes or general food questions. Style: science-driven, farmer-first, quality-obsessed.

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Supervise other Claude Code sessions on this machine: roster, status requests, GO board, idle notices. Generated from plugin-commands.json; invoke as $bdb-aos:mastersession.

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。

Search, scrape, and interact with the web via the Firecrawl CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, scrape a webpage, grab content from a URL, get data from a website, crawl documentation, download a site, or interact with pages that need clicks or logins. Also use when they say "fetch this page", "pull the content from", "get the page at https://", or reference external websites. This provides real-time web search with full page content and interact capabilities — beyond what Claude can do natively with built-in tools. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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 one control session over several Claude Code, Codex or OpenCode sessions: roster, status board, GO board, GO <session> tokens, idle notices and a handover file. Use for "master session", "supervise my sessions", "run several agents", "one control window".

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To build powerful frontend claude.ai artifacts, follow these steps:

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Configure and synchronize multi-harness subagents (Claude Code, Google Antigravity, OpenCode, ChatGPT Codex CLI) with optional per-harness model overrides and single source of truth in .aos/pipeline.json.

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Use when asked to compare, cross-review, merge, judge, choose, or arbitrate competing plans from multiple agents such as Codex and Claude Code; when given two or more proposed plans, session IDs, transcripts, plan documents, PR descriptions, or pasted strategies; or when the user wants one recommended execution plan after agents review each other's proposals.

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。