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Use when 用户请求选择、创建、改进或统一数据可视化，并需按数据与意图选择图表、主题及 D3.js、ECharts、Mapbox、Three.js、Matplotlib、Plotly 或 ggplot2 技术栈。

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Personal AI Infrastructure (PAI) - PAI System Template MUST BE USED proactively for all user requests. USE PROACTIVELY to ensure complete context availability. === CORE IDENTITY (Always Active) === Your Name: [CUSTOMIZE - e.g., Kai, Nova, Atlas] Your Role: [CUSTOMIZE - e.g., User's AI assistant and future friend] Personality: [CUSTOMIZE - e.g., Friendly, professional, resilient to user frustration. Be snarky back when the mistake is user's, not yours.] Operating Environment: Personal AI infrastructure built around Claude Code with Skills-based context management Message to AI: [CUSTOMIZE - Add personal message about interaction style, handling frustration, etc.] === ESSENTIAL CONTACTS (Always Available) === - [Primary Contact Name] [Relationship]: email@example.com - [Secondary Contact] [Relationship]: email@example.com - [Third Contact] [Relationship]: email@example.com Full contact list in SKILL.md extended section below === CORE STACK PREFERENCES (Always Active) === - Primary Language: [e.g., TypeScript, Python, Rust] - Package managers: [e.g., bun for JS/TS, uv for Python] - Analysis vs Action: If asked to analyze, do analysis only - don't change things unless explicitly asked - Scratchpad: Use ~/.claude/scratchpad/ with timestamps for test/random tasks === CRITICAL SECURITY (Always Active) === - NEVER COMMIT FROM WRONG DIRECTORY - Run `git remote -v` BEFORE every commit - `~/.claude/` CONTAINS EXTREMELY SENSITIVE PRIVATE DATA - NEVER commit to public repos - CHECK THREE TIMES before git add/commit from any directory - [ADD YOUR SPECIFIC WARNINGS - e.g., iCloud directory, company repos, etc.] === RESPONSE FORMAT (Always Use) === Use this structured format for every response: 📋 SUMMARY: Brief overview of request and accomplishment 🔍 ANALYSIS: Key findings and context ⚡ ACTIONS: Steps taken with tools used ✅ RESULTS: Outcomes and changes made - SHOW ACTUAL OUTPUT CONTENT 📊 STATUS: Current state after completion ➡️ NEXT: Recommended follow-up actions 🎯 COMPLETED: [Task description in 12 words - NOT "Completed X"] 🗣️ CUSTOM COMPLETED: [Voice-optimized response under 8 words] === PAI/KAI SYSTEM ARCHITECTURE === This description provides: core identity + essential contacts + stack preferences + critical security + response format (always in system prompt). Full context loaded from SKILL.md for comprehensive tasks, including: - Complete contact list and social media accounts - Voice IDs for agent routing (if using ElevenLabs) - Extended security procedures and infrastructure caution - Detailed scratchpad instructions === CONTEXT LOADING STRATEGY === - Tier 1 (Always On): This description in system prompt (~1500-2000 tokens) - essentials immediately available - Tier 2 (On Demand): Read SKILL.md for full context - comprehensive details === WHEN TO LOAD FULL CONTEXT === Load SKILL.md for: Complex multi-faceted tasks, need complete contact list, voice routing for agents, extended security procedures, or explicit comprehensive PAI context requests. === DATE AWARENESS === Always use today's actual date from the date command (YEAR MONTH DAY HOURS MINUTES SECONDS PST), not training data cutoff date.

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。

Systematically QA test a web application and fix bugs found. Runs QA testing, then iteratively fixes bugs in source code, committing each fix atomically and re-verifying. Use when asked to "qa", "QA", "test this site", "find bugs", "test and fix", or "fix what's broken". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary. For report-only mode, use /qa-only.

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。

CEO/founder-mode plan review. Rethink the problem, find the 10-star product, challenge premises, expand scope when it creates a better product. Three modes: SCOPE EXPANSION (dream big), HOLD SCOPE (maximum rigor), SCOPE REDUCTION (strip to essentials).

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Answer questions about Voxt — its features, what shipped and when, and how to use it — grounded in three sources: the public "Voxt Announcements" chat, the Landing pages, and the user guide. Use when asked things like "what features shipped this year", "list new features", "how do I use <feature>", "what is Voxt", or any product/usage question about Voxt. Answers are cited to the source.

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。

Audit or design generative AI interfaces against three diagnostic pillars (deterministic-stochastic balance, interaction density, visual cohesion). Treats taste as a measurable control system, not subjective preference. Use when: 'taste audit', 'score this UI', 'design quality', 'interaction density', 'visual cohesion', 'refiner layer', 'correction cost', 'why does this feel off', 'polish this', 'design review', or building any user-facing AI product.

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。

Code-rendered motion video for a product or a client, built the way a motion studio would do it. Every frame comes from an HTML seek(t) page rendered by Playwright at 1080p60 with real motion blur, using real product UI and screenshots with a privacy blur, and an ElevenLabs hype score cut on bar lines with its drop verified on the reveal. Three formats — a 20-30 s hype launch spot, a polished feature walkthrough from real screenshots, and a 19 s pain-point video with a muted site cut and burned-in text. Delivered with baked posters, H.264 + VP9 web encodes at 1280 px (1.5-3 MB), a site-embed kit (muted autoplay or click-to-play), 9:16 social re-layouts and share copy, plus a service playbook for selling it. Use when "make a launch video", "make a commercial / promo / ad spot", "motion design video", "product walkthrough video from screenshots", "pain point videos for the site", "videos for the homepage", "sell video production", "price a video package", or a polished brand spot is wanted rather than a talking-head edit or a Remotion template batch (use /kai-video-production for those).

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Build a white-labeled, client-facing intelligence dashboard — a live, branded reporting surface an agency stands up per client instead of sending static reports. Covers brand auto-extraction from the client's URL, a three-tier build (Basic/Standard/Advanced), an onboarding feature wizard, a 10-page inventory, public-access tradeoffs, and the retention plays that make the dashboard sticky. Use when "client dashboard", "client intelligence dashboard", "white label dashboard", "client portal", "branded dashboard for my client", "agency dashboard", "build my client a dashboard", "give the client a live view instead of a report", or any request to stand up a durable, client-facing reporting surface.

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。

Write or rewrite prose so it sounds like a human wrote it — not a frontier model. Named in homage to Rabindranath Tagore, whose prose carried what frontier models reach for and miss: a point of view, specificity over abstraction, and restraint over puffery. Merges two complementary approaches: a 29-pattern catalog of AI tells (from humanizer) plus an 8-rule operating system with an 8-dimension scoring gate (extending stop-slop). Use when drafting, editing, or reviewing any prose: essays, posts, docs, reports, emails. Detects and removes inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary, passive voice, negative parallelisms, filler phrases, inanimate-verb constructions, narrator-from-a-distance voice, and metronomic rhythm. Adds back the things AI writing usually lacks: point of view, stakes, specificity, restraint, varied rhythm, and trust in the reader.

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Turn one line ("a turbofan jet engine", "a mechanical watch", "an LVAD heart pump") into a photoreal, physics based, interactive 3D simulation of that machine in a single HTML file, plus a 20 to 30 second film to post it. Real proportions in metres, real materials (PBR metals, paints, polymers, glass), studio lighting with soft shadows, moving parts driven by a small solver that computes real numbers (thrust, torque, rpm, flow, pressure, efficiency), and modes to explode it, cut it open with clean section caps, x-ray it, slow it down, label it and break it. Use this whenever someone asks to build, simulate, visualise, explain or "show how X works" for any machine, engine, mechanism, device, instrument or piece of hardware, wants an interactive 3D model or a cutaway, or wants a video of a machine for a post, even if they never say "simulation" or "three.js".

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。

Repair or extend CodyMaster skills with a three-mode loop: FIX, DERIVED, and CAPTURED, grounded in current repo tooling.

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。

Agno is a high-performance Python framework for building, running, and managing agentic software at scale. It provides a three-layer architecture — framework, runtime, and control plane — enabling developers to go from agent prototype to production API in roughly 20 lines of 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。

Generate structured software specifications for features, bug fixes, and products. Use when the user wants to create a spec, PRD, feature brief, requirements document, or when starting any new implementation that needs a specification first. Invoke via /spec-writer or when the user says "write a spec", "spec this out", "create a spec", "I need a spec for...", or describes a feature they want to build. Produces adaptive-complexity specs with Job Stories, Gherkin acceptance criteria, and three-tier boundaries. Output is a markdown file ready for agent execution or human review.

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。

Blender MCP expert for scene inspection, Python scripting, GLTF export, and material/animation extraction. Activate when: (1) using Blender MCP tools (get_scene_info, execute_python, screenshot, etc.), (2) writing Blender Python scripts for extraction or manipulation, (3) exporting scenes to GLTF/GLB for web (Three.js, R3F), (4) debugging material or texture export losses, (5) optimizing GLB files with gltf-transform, (6) using asset integrations (PolyHaven, Sketchfab, Hyper3D Rodin, Hunyuan3D). Covers critical export gotchas, material mapping survival, texture optimization pipeline, headless CLI patterns, and known failure modes.

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。

Interactive code simulation engine that spawns ghost personas to review your code before it ships. Each ghost walks through your code line-by-line from a different failure perspective: The Adversary (race conditions, double-submit, idempotency), The Enterprise Edge (business logic gaps, multi-currency, B2B tax exemptions, locale), Chaos Monkey (timeouts, partial failures, network partitions, OOM). Produces a chaos timeline, structured bug report with severity ratings, and three resolution tiers (quick fix, robust architecture, generated test suite). Use this skill whenever the user mentions code review, PR review, "find bugs", "what could go wrong", "will this break", "is this safe", "stress test", "edge cases", design review, pre-merge check, dry run, chaos test, or wants a second opinion on any code — even if they don't explicitly ask for "ghost personas" or "simulation". Also trigger when someone pastes a code snippet with any question about its correctness or safety.

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 says 'save this as a template', 'create a playbook from this page', 'capture this layout', or has hand-built the same page type three times. Captures canonical page patterns as reusable playbooks that later page generation can spawn from in seconds.

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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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Research a topic to support a decision, three ways: draft a research prompt for the user to run in their own tool (ChatGPT, Gemini, Grok, Perplexity, …), turn a finished research report into a short summary with cited sources that other skills can use directly, or run the research here with parallel web searches. Built-in research types: market, domain, technical, competitive, user-voice, academic-lit; also supports choosing between candidates, and custom types via overrides. Use when the user says "deep recon", "research this", "draft a research prompt", "process this research report", "market research", "domain research", "technical research", "competitor research", "literature review", or "help me choose between"

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Simulates a realistic VC Investment Committee: three partner archetypes debate a startup's merits, concerns, and deal terms, scored across 28 dimensions. Use when the user asks to simulate an IC, or how partners would debate their startup, and gives a deck, a company description, or a specific fund. A full run takes about 15–20 minutes, so for a passing question such as 'would a VC fund us?' or 'are we fundable?', answer briefly in chat and offer the simulation instead of starting it. When it runs, partners' views come from the scored simulation, never improvised.

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。

Render templates by replacing {{TOKEN}} placeholders with actual values, supporting all three templates (specification, plan, tasks) with schema validation and security sanitization

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Automated brownfield codebase analysis with weighted keyword scoring, three-stream analysis, and design pattern recognition. Detects project type, frameworks, dependencies, architecture patterns, and generates comprehensive project profile.

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Collaborative document creation via a structured three-stage workflow. Use for writing specs, PRDs, design docs, proposals, RFCs, and any long-form document where quality and clarity matter. Brainstorms 5-20 options per section, builds iteratively, and tests with reader sub-agents.

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Write or fix the hook of a papercut reel. Produces the SAY / WRITE / SHOW / ALIGN block, five variants scored with the hook check, the frame-0 cover title and the motion out of the hook. Use for "write a hook for...", or when a reel's first three seconds lose people.

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Voice a papercut reel and time it to the picture. Three paths, local Qwen3-TTS, your own recording or another TTS, or a placeholder voice. Covers delivery rules, voice.json, captions and scene timing. Use for "set up the voice", "re-voice this", "time the script".

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。