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Render Blender scenes with the right engine and settings — Cycles for photoreal, EEVEE for speed/stylized, sample counts, denoising (OptiX/OIDN), light path tuning, color management (AgX/Filmic), file output (PNG/EXR/MP4), animation rendering. Use whenever the user asks to "render this", "produce an image", "render a frame / animation", "make a final image", "save the render", or any output-generation request. Make sure to use this skill even if the user does not say "render" — also covers "make a picture", "save the result", "produce a final image", "export as image".

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。

Export Blender scenes to glTF/GLB (web/AR), FBX (game engines), OBJ (universal), USD (VFX pipelines), STL (3D printing). Includes Unreal collision hulls and Multires normal-map baking for low-poly export, per-format settings, embed/unpack textures, axis conversion, polygon optimization (Decimate), and target-platform validation. Use whenever the user asks to "export this", "save as glTF / FBX / OBJ / STL / USD", "package for Unity / Unreal / Three.js / web / AR / 3D print", or any output format conversion. Make sure to use this skill even if the user does not say "export" — also covers "package this for the web", "make it work in Unity", "send to Unreal", "save for 3D printing".

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。

Convert 2D orthographic wireframe PNG drawings to 3D Blender models exported as glTF/GLB. Use this skill whenever the user provides wireframe images (technical drawings, line drawings, orthographic views, side/front/back panels) and wants to generate a 3D model, mesh, or .glb file. Triggers on phrases like "convert this wireframe to 3D", "make a 3D model from these drawings", "build a model from this wireframe", "generate GLB from these views", or any image-to-3D-mesh request involving line drawings. Make sure to use this skill even if the user does not explicitly say "wireframe" — also covers "orthographic views", "technical drawings", "line drawings of objects", "front and side views". Requires the Blender MCP addon to be running (port 9876) and Python with opencv-python, numpy, scipy installed.

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。

Closed-loop compare-adjust-repeat workflow for fitting Blender models to supplied front/side/back/top templates and originals. Use when the user asks to compare the product to templates/originals and adjust until it fits across all dimensions, or when all views must pass measurable bbox/centroid/silhouette/edge validation before export.

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。

Design and validate texture-state transitions for Blender animations without ugly whole-image crossfades, texture popping, misregistered morphs, or target-engine-incompatible Python-only swaps. Use when animating between multiple source textures, mascot/logo states, style states, lightmaps, decals, or UI skins.

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。

Set up Blender cameras with cinematic intent — focal length, depth of field (f-stop / focus object), composition (rule of thirds, leading lines), animated cameras (orbit, dolly, push-in), tracking constraints. Use whenever the user asks to "set up the camera", "frame the shot", "make it look cinematic / hero / portrait / wide-angle / telephoto", "add depth of field", "orbit the camera", or any composition/framing request. Make sure to use this skill even if the user does not say "camera" — also covers "hero shot", "close-up", "from above", "shallow focus", "85mm portrait look".

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。

Measure and validate supplied reference images, wireframes, texture atlases, and Blender renders before declaring a reconstruction 1:1. Use when an asset must match a template, when visual feedback says the output is off, when part counts must be exact, or before exporting a brand mascot/logo reconstruction. Pairs with reference-to-3d, contour-to-mesh, orthographic-registration, atlas-uv-fitting, and Blender MCP.

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 a scaffolded sequence of practice problems with graduated difficulty and strategic variability. Use when creating worksheets, homework sets, or independent practice materials.

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。

Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. Inside: 4-step research framework (website → social → traffic → ad spend) · 3-version Wayback evolution analysis · X/Twitter propagation-chain mapping · growth-flywheel 6-stage scoring · KOL identification · content-effectiveness ranking · ICP + freemium pricing teardown · self-check checklist. New in 1.2: time-machine competitor archaeology — Google date-range search back to pre-fame years, star-history launch-day reconstruction (AppFlowy ~40-channel zero-budget launch teardown), competitor funding news as a launch-timing signal, and the "open source alternative" positioning play. Sourced from first-hand founder interviews (AFFiNE co-founder, 2026-04, self-reported figures marked). 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Your competitor just launched. You have no idea how they grew so fast. Should you reverse-engineer their website? Track their social media? Map their growth flywheel? This gives you the complete SOP — from Wayback Machine snapshots to X/Twitter propagation analysis to growth flywheel scoring. Built from 150+ AI startup competitive analyses including the Lovable case study (4.3M views, 229K impressions launch day). By @WeiYipei. What's inside: • 4-step research framework (website teardown → social media → traffic sources → ad spend) • 3-version website evolution analysis (V1 / Beta / Launch via Wayback Machine) • X/Twitter propagation chain mapping (4-phase model + single-post teardown template) • Growth flywheel 6-stage scoring system (Activation → Referral → Acquisition → Retention → Revenue → Product) • KOL identification framework (Traffic King / Topic Starter / Mindset Shifter) • Content effectiveness ranking (validated: Visual Demo > User Story > Competitor Comparison) • ICP deep analysis template + Freemium pricing teardown • Complete self-check checklist 🇨🇳 你的竞品刚刚爆了。你完全不知道他们怎么做到的。该拆官网？盯社媒？画增长飞轮？这份 SOP 给你从 Wayback Machine 快照到 X/Twitter 传播链路还原到飞轮六阶段评分的完整方法论。基于 150+ AI 创业公司竞品分析实战，含 Lovable 完整案例。 🇯🇵 競合が突然バズった。どうやって成長したか全く分からない。このSOPは、Waybackスナップショットからツイッター伝播チェーン分析、フライホイール6段階スコアリングまでの完全な競合調査フレームワークを提供します。150以上のAIスタートアップ分析から構築。 🇰🇷 경쟁사가 갑자기 폭발적으로 성장했습니다. 어떻게 그렇게 빨리 성장했는지 모릅니다. 이 SOP는 Wayback 스냅샷부터 X/Twitter 전파 체인 분석, 플라이휠 6단계 스코어링까지 완전한 경쟁사 조사 프레임워크를 제공합니다. 150개 이상의 AI 스타트업 분석에서 구축. Triggers: "competitor research" | "competitive analysis" | "competitor analysis" | "growth flywheel" | "market research" | "竞品调研" | "竞品分析" | "增长飞轮" | "传播链路" | "競合分析" | "경쟁사 분석" | "social media teardown" | "Twitter propagation" | "content strategy analysis"

Implements the Specification pattern for encapsulating query logic. Enables composable, reusable, and testable query criteria with support for includes, ordering, and pagination.

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。

Kolla-Ansible day-2 operations skill for post-deployment infrastructure lifecycle management. Covers service reconfiguration (globals.yml changes, config overrides, prechecks, targeted reconfigure with --tags), minor and major OpenStack upgrades (image pull, upgrade procedure, rollback), container management (restart, logs, health inspection), maintenance mode (compute disable, instance drain, host maintenance), password rotation, certificate renewal, and rolling updates. This skill is for operations after initial deployment -- the kolla-ansible deployment skill covers initial bootstrap and deploy.

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。

Opens a deliverable (talk, course module, paper, book chapter, or referee report) for AI-assisted work across sessions. Interviews the author for the manifest, then scaffolds deliverable.yml, an append-only HANDOFF.md, a numbered planning/ wiki with open questions, an inbox/ for dictated braindumps, a gitignored checks/, and the house deck template for talks. Use when starting deadline-driven work that spans sessions, or when asked to open a deliverable, set up a talk, paper, or module for the pipeline, or make a manifest. Not for one-off tasks.

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。

Define reusable social media content pillars for a product, founder, brand, or campaign. Use when a content calendar needs durable themes, repeatable post types, and clearer editorial direction.

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。

Debug and troubleshoot ElevenLabs conversational AI agents and Twilio calls. Use when diagnosing agent issues, analyzing failed calls, troubleshooting audio problems, investigating conversation breakdowns, reviewing error logs, or optimizing underperforming agents. Includes transcript analysis, error diagnosis, and performance troubleshooting.

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 SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.

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。

Uses Bubble's Plugin API and the Stripe.js SDK to embed a self-service subscription management portal inside a Bubble application. Configures Bubble's API Connector to call Stripe's Billing Portal Sessions endpoint and handles webhook events for real-time plan change updates.

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 and validates Ansible playbooks from infrastructure requirements. Uses ansible-lint for validation and queries Ansible Galaxy API for discovering certified roles and collections.

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 Ansible playbook YAML with proper module usage, handler chains, and role structures using ansible-core built-in modules. Supports Jinja2 template rendering and vault-encrypted variable 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。

Validates Ansible playbooks using ansible-lint and the Ansible Galaxy API. Performs check-mode dry runs, validates role dependencies, and detects deprecated module usage across collections.

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。

Validates Ansible playbooks and roles using ansible-lint and yamllint APIs. Enforces best practices for idempotency, variable naming, and handler usage with custom rule profiles.

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。

Transform Markdown or text specifications into human-readable HTML documents with summaries, reordered structure, visual explanations, diagrams, charts, and traceability to the source. Use when asked to convert a spec, requirements document, API spec, product spec, design doc, or technical document into readable HTML, especially when the user wants summarization, restructuring, diagrams, graphs, images, or visual aids rather than a literal Markdown-to-HTML conversion.

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。

Enable, disable, and manage Claude Code skills. Use when the user asks to enable/disable/toggle/manage skills, check skill status, list skills, manage skill collections, or clean up the skills directory.

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。