「prompt」の検索結果

Use before any CLI/TUI display code is written, modified, or audited — runs the 7-stage discipline (Context, Register, Shape brief, Implement, Critique, Audit, Polish) for Typer, Rich, Textual, and Questionary work, grounded in per-project PRODUCT.md and DESIGN.TUI.md/DESIGN.md. Triggers on output formatting, display design, interactive prompts, visual consistency, TUI layout, progress display, dashboard design, design audit, design polish, design critique, shape brief, register decision (brand-cli vs product-cli), and AI-slop checks.

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Optimize existing CLAUDE.md, SKILL.md, agent definitions, and other AI-facing files for Claude comprehension and economy. Scope: optimization of existing content only — not upstream sync, not read-only auditing. Measures baseline metrics, delegates to the ai-doc-optimizer agent with file-type-specific context, runs independent verification via second agent, measures post-optimization metrics, and presents comprehensive before/after report. Supports iterative mode for large targets. Use when improving prompt effectiveness, reducing token waste, or rewriting instructions for LLM consumption. Invoke with /optimize-claude-md <file-or-directory>.

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Audit agent lifecycle — validates agent execution capability against configuration. Accepts plugin path, runs 8 semantic audits (capability vs config alignment, skill loading correctness, inter-agent contracts, prompt contradictions, tool sufficiency, dead agents, scriptable patterns, pattern learning), writes reports to .plugin-creator/audits/. Use when auditing agent lifecycle, checking agent capabilities, verifying tool access, finding dead agents, validating agent contract alignment, or confirming agents can execute workflows.

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Configure Claude Code permissions — tool approval rules, permission modes, managed policies, and sandboxing. Use when setting up permission rules, configuring allow/deny/ask policies, debugging permission prompts, deploying managed settings for organizations, or controlling Bash/Read/Edit/WebFetch/MCP/Agent tool access.

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Use when starting, stopping, or checking the dot-dash live session dashboard — a real-time browser UI that monitors all active Claude Code sessions, streams transcripts, and supports prompt injection

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Use when building, extending, or debugging FastMCP v4 (or v3) Python MCP servers. Activates on FastMCP tool/resource/prompt creation, provider and transform implementation (CodeMode, Tool Search), auth setup (MultiAuth, PropelAuth, KeycloakProvider), client SDK usage, FastMCPApp and Generative UI server building, fastmcp-slim client-only installs, nginx reverse proxy deployment, Prefab Apps, OTEL observability, testing, and v3→v4 migration. Grounded in official FastMCP v4 docs plus locally-verified v3 gotchas — zero speculation.

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Reviews LLM integration, prompt templates, model selection, token/cost budgets, evaluation harnesses, structured outputs, retries, and streaming. Checks trust boundaries and task evidence against the selected provider/model contract rather than prescribing universal model tiers or sampling settings.

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Analyze the causal system-wide impact of a proposed change across behavior, interfaces, state/data, runtime dependencies, tests, documentation, controls, people/processes, and model/prompt/context behavior. Use before planning when lexical reference search is insufficient to establish blast radius and verification obligations.

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Generates one worker task prompt conforming to the CLEAR + selective CoVe task design standard and swarm-task-planner structure. Use when creating or rewriting a single task entry or task block inside a plan — providing a title and brief description as input.

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Orchestrate parallel agent dispatch as a manager — not a micromanager. Use when coordinating 2+ independent workers, running SAM task waves, relaying discoveries between worker waves, handling blockers, or synthesizing results. Covers both SAM structured dispatch (the task does the work) and ad-hoc dispatch (reference agent-orchestration for prompt template).

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Decompose substantive work into phases, dispatch each phase to a sub-agent, and adjudicate what comes back. Use whenever a request asks for implementation, investigation, a fix, a review, or any change to files — including small ones — and whenever you are about to read source or run a diagnostic yourself instead of handing it off. Also use when a report from a sub-agent needs judging, when a phase needs re-dispatching, or when a user names one instance of a pattern. Does not apply when your own prompt begins "Your ROLE_TYPE is sub-agent." — then follow references/sub-agent-contract.md instead.

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You MUST use this before any creative work - creating features, building components, adding functionality, modifying behavior, or when users request help with ideation, marketing, and strategic planning. Explores user intent, requirements, and design before implementation using research-validated prompt patterns.

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Transform transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis data.

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When setting up commit message validation for a project. When project has commitlint.config.js or .commitlintrc files. When configuring CI/CD to enforce commit format. When extracting commit rules for LLM prompt generation. When debugging commit message rejection errors.

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Use when finding real role-holders at known company domains from an ICP, especially prompts like 'find all job titles at these companies', 'find qualified titles', 'find RevOps or marketing-ops buyers', or when exact title discovery should precede paid people search.

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Build an award-winning "3D scroll" website of ANY kind from ONE prompt — product launch, portfolio, agency, contractor/home-services, restaurant, real estate, personal brand, event, app, etc. Generates a high-end cinematic hero visual + clips (360° orbit, fly-through, reveal, explode, parallax) fitting the site's subject with the Higgsfield MCP, slices them into a scroll-scrubbed canvas frame sequence with ffmpeg, builds a branded multi-section site (Lenis smooth scroll + scroll reveals), and launches it on localhost. Use when the user asks for a "3D scroll" site, scroll-driven hero/reveal, a cinematic landing page, a buckssauce/Apple/Awwwards-style scroll site, or "build me a website like X" — for a product OR a service, person, place, or brand. Trigger words: 3D scroll, scroll animation, cinematic hero, fly-through, turntable, scroll site, landing page, portfolio site, agency site, Higgsfield site.

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Design, create, or validate saved and transient Vapi voice assistants. Use for new phone or web agents, production system prompts and first messages, saved-versus-transient architecture, model/voice/transcriber selection, multilingual compatibility, existing tool attachment, native call-control tools, assistant hooks, and Create Assistant API validation errors.

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Use when a validated ChatGPT/Codex Plugin needs accurate Plugin Directory fields, discovery metadata, starter prompts, and reviewer-facing listing details grounded in the packaged product.

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Use when a repository has a valuable agentic workflow, prompt chain, playbook, command, or runbook that should become a portable ChatGPT/Codex Skill without losing its real decision logic.

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Use when an existing repository contains agents, prompts, workflows, playbooks, commands, or Skills and you need to identify which capabilities are worth converting into a ChatGPT/Codex Plugin.

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Advises on setting up LLM-based text classification as a validated measurement design. Covers codebook construction, learning-regime and model choice, prompt construction, validation against human coding with kappa and F1, hybrid human-LLM review, downstream measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline for repeated free text against a closed codebook. Use when the user wants to classify, code, or label text at scale. Category discovery goes to topic-modeling.

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Advises on setting up and validating LLM text classification: codebook, learning regime and model choice, prompts, validation against human coding (kappa, F1), hybrid review, measurement error, and reporting, plus a short resumable batch pattern with a rule-based baseline. Use to classify, code, or label text at scale; category discovery goes to topic-modeling.

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When the user wants to create or optimize in-app paywalls, upgrade screens, upsell modals, or feature gates. Also use when the user mentions "paywall," "upgrade screen," "upgrade modal," "upsell," "feature gate," "convert free to paid," "freemium conversion," "trial expiration screen," "limit reached screen," "plan upgrade prompt," or "in-app pricing." Distinct from public pricing pages (see page-cro) — this skill focuses on in-product upgrade moments where the user has already experienced value.

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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.

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。