「openai」の検索結果

Prepare meeting materials with Notion context and Codex research; use when gathering context, drafting agendas/pre-reads, and tailoring materials to attendees.

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。

Generates custom design system rules for the user's codebase. Use when user says "create design system rules", "generate rules for my project", "set up design rules", "customize design system guidelines", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.

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。

Manage issues, projects & team workflows in Linear. Use when the user wants to read, create or updates tickets in Linear.

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 a user asks to debug or fix failing GitHub PR checks that run in GitHub Actions; use `gh` to inspect checks and logs, summarize failure context, draft a fix plan, and implement only after explicit approval. Treat external providers (for example Buildkite) as out of scope and report only the details URL.

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。

Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions "implement design", "generate code", "implement component", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.

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。

Cut a Symphony release by bumping the committed version, landing it, tagging the merged commit, and verifying the Burrito release workflow. Use when asked to release, tag, or retag Symphony.

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。

Investigate stuck runs and execution failures by tracing Symphony and Codex logs with issue/session identifiers; use when runs stall, retry repeatedly, or fail unexpectedly.

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。

Land a PR by monitoring conflicts, resolving them, waiting for checks, and squash-merging when green; use when asked to land, merge, or shepherd a PR to completion.

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 a well-formed git commit from current changes using session history for rationale and summary; use when asked to commit, prepare a commit message, or finalize staged work.

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 Symphony's `linear_graphql` client tool for raw Linear GraphQL operations such as comment editing and upload flows.

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。

Pull latest origin/main into the current local branch and resolve merge conflicts (aka update-branch). Use when Codex needs to sync a feature branch with origin, perform a merge-based update (not rebase), and guide conflict resolution best practices.

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。

Push current branch changes to origin and create or update the corresponding pull request; use when asked to push, publish updates, or create pull request.

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 the user through an independent iFixAi audit of their own agent, checking whether it does the job it is supposed to do given their business rules and org structure. Prefer pointing it at the user's REAL deployed agent over its HTTP endpoint (its actual tools, retrieval, and governance) with `--provider http --endpoint <url>`; only when no endpoint is reachable, fall back to replicating the model beneath as a bare stand-in (Anthropic, OpenAI, Gemini, Azure, Bedrock, etc.). Graded by the judge(s) of their choice (the same model, one independent judge, or a cross-vendor panel). You are the operator who walks them through it and explains the scorecard, running the SAME `ifixai run` engine as the guided CLI. Use when the user asks to run iFixAi or to audit an agent.

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。

Remove multi-vendor AI provenance marks: invisible Unicode (Layer A), statistical text watermarks via rewrite (Layer B, always offer), and C2PA/EXIF/XMP/container metadata on PNG/JPEG/WebP/SVG/PDF/DOCX/ODT/HTML/MD/TEX. Covers Claude, Gemini/SynthID-class, OpenAI provenance, and open-LLM sampling marks. Use when the user asks to strip watermarks, remove C2PA/Content Credentials, clean AI metadata, remove invisible Unicode, anti-detect clean AI output, or runs /remove-ai-marks (aliases: /remove-claude-marks).

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查询 AIHOT（aihot.news）的中文 AI 资讯。用户问今天或最近 AI 圈发生了什么、AI 新闻和 AI 日报、大模型与 AI 产品发布、OpenAI／Anthropic／Google 等公司或某个 AI 话题的最新消息、当前最热的 AI 事件及来龙去脉、AI 模型排行、Codex 额度重置，或其它 AI 行业动态时使用。必须实时查询 AIHOT，不凭训练记忆回答新闻；匿名只读，无需 API Key。

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Connects to and performs inference with Google Cloud Agent Platform GenAI models, including First-Party Gemini models and Third-Party OpenMaaS models (Llama, DeepSeek, Qwen, etc.). Use when asked to perform inference, ask a model a question, run a test prompt, execute chat completions, or generate code for calling Gemini or OpenMaaS models, authenticate with GenAI SDK, OpenAI SDK, or legacy Agent Platform SDK, configure base URLs and global/regional endpoints, or troubleshoot 429 Resource Exhausted (DSQ), 400 User Validation, or 404 Not Found errors. Don't use for deploying models to endpoints or for running model evaluations.

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Drive OpenAI's Codex CLI (`codex exec`) as a non-interactive coding sub-agent from inside Claude Code. Load WHENEVER you want to delegate a coding/analysis/refactor task to Codex, get a second opinion / adversarial review from another model, fan out parallel agents across files or worktrees, or run a long mechanical job while you stay the planner. Covers the exact `codex exec` flags, sandbox tiers, output capture, JSON/schema modes, session resume, parallel fan-out, and the mandatory "delegate → capture → independently verify, never trust the self-report" supervision loop.

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。

Intakes external content, classifies chunks against LifeOS taxonomy, commits with provenance. Sources: .md/.txt, stdin, LifeOS dirs, CLAUDE.md/Cursor/OpenAI Custom Instructions, Obsidian/Notion/Apple Notes exports. MigrateScan classifies → routing table. MigrateApprove with --approve-all/--approve-target/--review/--dry-run. Confidence ≥70% auto, 40-70% confirm, <40% walk-through. USE WHEN /migrate, migrate content, import from other LifeOS, bring in old notes, import Cursor rules, import CLAUDE.md, bulk import, Obsidian/Notion/Apple Notes import. NOT FOR single-file edits, conversational interviews, identity edits.

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。

Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents, LlamaIndex, Pydantic AI, CrewAI, and others); choose between a native integration and manual instrumentation; set span types, tags, or metadata; or send DeepEval-SDK traces to Confident AI's Observatory. DO NOT TRIGGER for building DeepEval pytest eval suites, datasets, goldens, metrics, or deepeval test run (use the `deepeval` skill), or for raw OpenTelemetry / OTLP export without the deepeval package (use the `deepeval-otel` skill). This skill is purely DeepEval-SDK instrumentation — producing well-formed traces, not running evals.

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Autonomous research review loop using any OpenAI-compatible LLM API. Configure via llm-chat MCP server or environment variables. Trigger with "auto review loop llm" or "llm review".

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Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Use when fine-tuning LLMs, training custom models, or adapting foundation models for specific tasks. Invoke for configuring LoRA/QLoRA adapters, preparing JSONL training datasets, setting hyperparameters for fine-tuning runs, adapter training, transfer learning, finetuning with Hugging Face PEFT, OpenAI fine-tuning, instruction tuning, RLHF, DPO, or quantizing and deploying fine-tuned models. Trigger terms include: LoRA, QLoRA, PEFT, finetuning, fine-tuning, adapter tuning, LLM training, model training, custom model.

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Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.

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。