「intent」の検索結果

The branded entry point to ServiceGraph — use whenever the user explicitly names **ServiceGraph** — "use ServiceGraph to…", "what datasets does ServiceGraph have", "search ServiceGraph for…", "look this up in ServiceGraph", "pull contacts from ServiceGraph for these domains", "how many credits do I have on ServiceGraph". ServiceGraph is a multi-dataset platform of metrics-enriched business data for founders — where to launch, who to email, who to hire. This skill explains how to drive the API (api.servicegraph.co / mcp.servicegraph.co) against ANY dataset — discover what datasets exist, discover a dataset's schema and filters, search free brief rows, and unlock contact + metric detail with credits. Dataset-agnostic by design — it discovers everything through the API and never assumes which datasets or fields exist. When the user describes an intent WITHOUT naming ServiceGraph (e.g. "find a PR agency in NY"), defer to the matching specific skill (find-pr-agency, find-marketing-agency, find-law-firm, …); this skill is for explicit ServiceGraph requests and for datasets no specific skill covers yet. Skip non-US firms, consumer/personal services, and individual freelancers.

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Builds, revises, restyles, and critiques deliberate, editable Feishu / Lark whiteboards from content, references, existing SVGs, or live boards. Use for whiteboards, infographics, diagrams, system maps, timelines, decision boards, posters, and visual explainers that need factual fidelity, strong hierarchy, intentional composition, and evidence-based review rather than a generic grid of equal cards. Routes creation, revision, and critique through separate playbooks; commits to a content-and-design contract before drawing; validates deterministic SVG defects; renders and inspects the actual artifact; and uses an independent final reviewer for material deliveries.

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.

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Full-stack video production from script to rendered video. Combines script generation (optimized for TikTok/YouTube/Reels) with AI-powered video rendering using Remotion, AI voiceovers (Qwen3-TTS/ElevenLabs), music generation (ACE-Step), and browser-based demo recording. Multi-session project tracking with automatic intent reconciliation. Use when "create video", "produce video", "demo video", "product video", or any request to generate AND render video content.

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You MUST use this before any creative work or multi-step task. Explores intent, requirements, and design before implementation. Then documents the plan before coding.

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。

You MUST use this before any creative work or multi-step task. Explores intent and writes execution-ready plans before coding.

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 SEO content strategy for SaaS and B2B sites. Use when doing keyword research, planning content clusters, creating pillar pages, optimizing on-page SEO, fixing technical SEO issues, building links, refreshing content, implementing programmatic SEO, mapping search intent, optimizing for SERP features, or measuring content performance. Use for SEO audits, content gap analysis, and organic growth strategy.

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Design the MixerSceneIntent, approval state, JSON schema, CLI examples, audit entries, and tests for AI-Controlled Party mixer/Soundcraft Ui24R scene arming. Use whenever a task mentions mixer scenes, Ui24R snapshots/cues, operator-approved mixer control, show-director contract updates, schema updates, dry-run CLI shape, or follow-ups that revise this contract.

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Guides intentional Nothing-inspired UI and design-system work with typography, spacing, hierarchy, components, and platform-specific execution. Use when the user explicitly asks for Nothing style, Nothing design, or `/nothing-design`, not for generic UI or design tasks.

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Central dispatcher for the canvas/state render fleet. Routes intent to a specialist (`/mycelium:diamond-render`, `/mycelium:ost-render`, `/mycelium:cycle-render`) OR lists what's renderable. Cross-cutting views spanning multiple canvases (opp→sol→cycle traceability, confidence-over-time, cluster-graduation-flow) are research-gated and NOT emitted by default. See `${CLAUDE_PLUGIN_ROOT}/engine/render-conventions.md` for shared conventions.

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Use when a change request lands on a project that already has `.hedgehog/` and no build in flight — a finished build being adjusted, or an adopted repo's next piece of work. Triggers on any "change this", "fix this", "add this" on such a project. Sizes the request against the installed core's own layers and routes it to one of three exits: a tweak made and committed here, change-work through `hedgehog intent add` and the core's loop, or a re-plan. Not for a build still in progress — that is the core's loop skill's own job.

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Convert Claude Code plugins into generic, platform-agnostic format. Supports full conversion (skills, agents, hooks), flatten mode (skills only — agents converted to skills, hooks absorbed), or nested mode (agents nested as pure markdown within parent skills, hooks absorbed). Full mode supports per-group or unified output layouts — unified merges all groups into a single directory organized by component type. Strips Claude Code-specific implementation details to produce clean markdown files preserving only the intent and instructions. Use when asked to "make this portable", "convert to generic format", "export plugin", "create universal skills", "strip platform dependencies", "make harness-agnostic", "decouple from Claude Code", "flatten to skills only", "nest agents in skills", or when the user wants their plugin content usable outside Claude Code. Also use when the user wants to share skills with teams using different agent frameworks.

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Use the open-source free `CSharpier` formatter for C# and XML. Use when a .NET repo intentionally wants one opinionated formatter instead of a highly configurable `dotnet format`-driven style model.

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 distinctive, production-grade web interfaces with intentional aesthetic direction. Avoids generic AI aesthetics (centered layouts, Inter/Arial, purple gradients, uniform corners). Use for UI components, dashboards, landing pages, React/HTML/CSS layouts where design quality matters.

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Use when you need semantic retrieval from perpetual memory at query time. Parses query intent, searches vector store, ranks by relevance and recency, and injects context. Complements memory-search.cjs with the perpetual_memory LanceDB table.

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。

Intent-to-command mappings for fullstack-typescript tech pack features.

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。

Improve UI typography by fixing font choices, type scale, hierarchy, weight, and readability so text feels intentional. Use when the user mentions fonts, typography, type scale, readability, text hierarchy, or sizing that feels off.

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。

Perform an adversarial review of the current uncommitted changeset, validating it against project contracts and architectural intent.

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。

Mobile (Android) app recon and analysis — decompile APK, extract secrets, find hardcoded endpoints, exposed Firebase / AWS / API keys, deeplink abuse, intent vulnerabilities, exported components, WebView XSS, MobSF static analysis. Use when target's scope includes Android apps (.apk).

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。

Reflective Memory. Use when working with memory, reflection, context management, or intent tracking. Provides the reflective memory practice — how and when to use keep tools for capturing insights, tracking commitments, and maintaining awareness.

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 evidence-labeled semantic cores for SEO, LLM-readable architecture, and site information design. Use this skill when the user asks for semantic core, query clusters, search intents, audience segments, entity/topic maps, language or locale priorities, competitor/source evidence, pillar-topic planning, or a structured handoff before URL architecture and internal linking. It must mark unknown search volume, difficulty, and ranking data as unknown instead of inventing metrics.

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 raw intent must be captured and routed to the right next state.

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Produces structured markdown session continuation documents for multi-session workflows. Captures active work streams, decisions with rationale, knowledge-file deltas learned during the session, the exact files to load next conversation, ingested content, artifacts, and open items into an ingestion-optimized handoff. Carries pending KF updates and unresolved items forward across handoff chains so context never drops. Generates a closeout checklist and echoes the starter prompt to chat. Use when approaching context limits, wrapping up a session, or managing work across conversations. Trigger on: "create a handoff," "session closeout," "wrap up this session," "build session handoff," "BSH," "continue next session," "we're running out of context," "context is getting long." Do NOT use for Memory optimization, context budget analysis, project audits, deciding whether work is ready for autonomous/Claude Code execution (use rootnode-handoff-trigger-check), or summaries with no continuation intent.

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Produce a truthful Japanese App Store metadata pack and app-name shortlist in twelve minutes without a multi-agent research round. Use for a live launch sprint, fallback ASO copy, app naming under a hard deadline, or when local product evidence is available but market research is intentionally deferred.

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。