「openai」の検索結果

繰り返すLLMへのAPIリクエストで、共通の指示や資料を再利用しやすく並べるスキル。キャッシュ利用量と費用、応答時間を確認し、構成の見直しを支援します。

• 繰り返すAPIの共通指示を並べ替え
• キャッシュの読み書きと保存費用を試算
• キャッシュ利用率と応答時間を測定

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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 for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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 Codex (OpenAI's codex app-server) as a full agent provider — planning, tool orchestration, MCP tools, server-side history, session resume — alongside or instead of Claude. ChatGPT subscription or OpenAI API key, vault-only via the selected gateway. Per-group via `ncl groups config update --provider codex`. Distinct from using OpenAI as an MCP tool (where Claude remains the planner).

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 the user asks for text-to-speech narration or voiceover, accessibility reads, audio prompts, or batch speech generation via the OpenAI Audio API; run the bundled CLI (`scripts/text_to_speech.py`) with built-in voices and require `OPENAI_API_KEY` for live calls. Custom voice creation is out of scope.

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, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

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 Codex through instrumenting or extending repositories with OpenAI Ads Measurement Pixel and optional Conversions API (CAPI). Use when adding Ads conversion tracking, browser pixel events, server-side conversion events, event_id deduplication, CAPI secret placeholders, incremental conversion coverage, or validating Ads conversion setup. Applies to local repositories and PR review contexts; prioritize safe, reviewable diffs and never place API keys or secrets in source code or client bundles.

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, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.

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。

Validate changesets in openai-agents-js using LLM judgment against git diffs (including uncommitted local changes). Use when packages/ or .changeset/ are modified, or when verifying PR changeset compliance and bump level.

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。

Compatibility alias for OpenAI platform documentation guidance. Delegate to the canonical local `openai-docs` payload while preserving route compatibility.

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 for running tests in the openai-dotnet repository. Use this when asked to run, debug, or validate tests, or when writing new tests. Explains test modes (Playback, Record, Live), how to identify recorded vs non-recorded tests, environment variable configuration, and what to do when recordings are missing or stale.

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。

Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.

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。

OpenAI Codex CLI and cross-platform skill authoring. Use when setting up Codex CLI, converting or syncing skills between Claude Code and Codex, configuring agents/openai.yaml, or validating cross-platform skill compatibility.

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。

Speech-to-text transcription via OpenAI Whisper. Supports two modes — Local CLI (no API key, runs on-device) and Cloud API (fast, scalable, requires OPENAI_API_KEY). Use when the user needs to transcribe audio files, translate speech, or convert audio to text.

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。

Delegates tasks to the OpenAI Codex CLI (codex exec) via delegation-core. Use when delegation-core selects Codex or OpenAI credentials are already configured.

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。

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

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。

Azure OpenAI Service integration. Manage Models, Deployments, Prompts, Completions. Use when the user wants to interact with Azure OpenAI Service data.

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。

Assess an openai-guardrails-python GitHub issue or pull request as a maintainer. Use to verify the claimed need and practical impact, compare supported alternatives or competing approaches, separate code quality from repository readiness, recommend the maintainer action, and draft a copy-ready comment when evidence, changes, or closure should be 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。

Create the required PR-ready summary block, branch suggestion, title, and draft description for openai-guardrails-python after runtime, tests, examples, build/test configuration, or behavior-impacting docs change.

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。

Start and carry an explicitly invoked openai-guardrails-python implementation through a fresh isolated worktree and a local PR-ready handoff. Fetch the latest origin/main, keep task changes uncommitted, replay them onto the latest main before final review, run applicable verification and $implementation-final-review, use $pr-draft-summary to generate the complete PR draft and branch name, then create one clean local commit with takeover provenance when applicable. Use only when the user explicitly invokes this skill; never push, open a PR, or mutate GitHub.

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。

Review an openai-guardrails-python release plan or final release candidate against the previous remote tag, determine the compatible release type, audit runtime and packaging risk, inspect current CI, and produce an English ship-or-block report. Use for pre-release readiness checks, not ordinary PR review or implementation.

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 the mandatory verification stack when changes affect runtime code, tests, examples, or build and test behavior in the OpenAI Guardrails Python repository.

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。

Choose compatibility-aware scope for runtime and API changes in openai-guardrails-python. Use before initial implementation and each review-feedback batch to decide whether to patch, reset the design, preserve compatibility, or reject unsupported cases.

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。