「prompts」の検索結果

Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.

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。

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.

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。

Add a professional footer to README files with engagement prompts, community channels, and call-to-action for subscriptions. Use when you need to enhance your README with a footer that encourages interaction and builds community.

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 and manage ad-hoc AI prompts in sgai workspaces without starting a full agentic session. Use when you need to run a one-off AI prompt against a workspace, check the status of a running ad-hoc prompt, or stop a running ad-hoc prompt.

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。

Especialista en configuración de agentes de IA: templates, tools, models, prompts y seed 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。

Map how customers discover, evaluate, and choose a product across Google, AI chatbots (ChatGPT, Claude, Perplexity), and social platforms, using the 5-stage awareness journey. Use when the user asks about search intent, AI SEO prompts, discoverability, buyer journeys, awareness stages, or what content to create for a brand, website, or keyword list.

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。

Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming 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。

Automate and research the GitHub Copilot app desktop experience. Use for driving Copilot App sessions, creating isolated app experiments, mapping app UI/accessibility controls, taking app screenshots, saving PNG captures, optimizing WebP images, testing prompts without submitting, using app/session APIs, canvases, automations, macOS Accessibility, AppleScript, and safe end-to-end Copilot App workflow probes.

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Default entrypoint and master ctf-sandbox-orchestrator workflow for CTF, exploit, reverse engineering, DFIR, pwnable, crypto, stego, mobile, AI-agent, cloud, container, Active Directory, Windows-host, and identity challenges. Use first when the user presents challenge infrastructure, binaries, prompts, hosts, or identities that should be treated as sandbox-internal by default and Codex needs to choose, route, and load the right downstream analysis path with concise evidence.

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Use Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production 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。

Use ZCF to initialize, update, and standardize Claude Code or Codex environments with prompts, workflows, MCP services, API settings, and backups.

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。

Bulk-generates SEO meta descriptions from page content using OpenAI's GPT-4o API with custom system prompts tuned for SERP CTR optimization. Validates character limits, checks for duplicate descriptions via Screaming Frog CSV exports, and outputs Yoast SEO-compatible import files.

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。

Expert product marketing guidance for SaaS growth - trial acquisition, user activation to first value, and freemium-to-paid conversion. Use when writing landing pages, onboarding flows, email sequences, pricing pages, upgrade prompts, or any growth/conversion copy.

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Generate images and video from text prompts via the mediagen CLI, across Gemini, OpenAI and Kie AI. Use when asked to create, generate, render, or edit an image or a video — a logo, an illustration, a mockup, a photo, a banner, a clip — or to mark existing media as AI-generated. Also covers choosing a provider or model for a generation task.

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。

Scaffold, build, and run MCP servers from a predictable Python project layout with tools, prompts, resources, auth, and OpenTelemetry hooks.

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。

Automatically optimize AI agent credit usage by routing tasks to the most cost-efficient execution path. Use when you want to reduce AI API costs by 30-75% without quality loss, classify task complexity before execution, route simple tasks to free or low-cost models, split complex tasks into optimized sub-tasks, or detect vague prompts before wasting credits.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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Adds new visual examples to the tdmcp prompt cookbook documentation — both the English and Portuguese versions. Use when asked to: add more cookbook examples, create visual examples for the docs, show more surprising things you can do, add entries to the prompt cookbook, expand the cookbook, or any request to extend the cookbook with new prompts and results. Runs a 3-phase pipeline: curate candidates → write EN → write PT.

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。

Orchestrates the tdmcp feature team end-to-end: design/wireframe → build → integrate → QA → release. Use whenever the user wants to build, implement, develop, ship, or add one or more tdmcp features/tools/effects/controls/AI-prompts for TouchDesigner, or to run them through a coordinated design→develop→QA→deploy pipeline. Also use for follow-ups: re-run, continue, update, fix, or improve a feature/batch, ship the next version, or rebuild just one part of a previous run. Single feature or a whole batch.

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。

Respan for LLM observability. Use to set up tracing or the gateway, manage prompts (create/version/deploy), run evals, experiments, and datasets, or work with traces and logs. Trigger for any Respan platform task or when instrumenting an app to send telemetry to Respan.

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 a user request or PRD document into a structured Design Spec JSON — theme, color, typography, density, and device type. Call this before building Stitch generation prompts.

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Generates 3 alternative Stitch prompts for A/B testing screen concepts — vary layout, visual style, or content while keeping the underlying information architecture consistent.

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。

Edits existing Stitch screens with text prompts — the iteration tool. Change colors, layout, content, or style without regenerating from scratch. Supports refinement loops via output_components suggestions.

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。

Orchestrate multi-LLM parallel debate and synthesis. Dispatches prompts to available omega CLI wrappers in parallel, collects independent responses, runs anonymized peer review ranking, and synthesizes via a chairman model. Supports collaboration templates, idle watchdog, inter-agent messaging, worktree isolation, and multi-turn sessions. No server required.

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。