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Design and optimize pricing strategies for SaaS products including tiering, packaging, value metrics, and experimentation. Use when setting initial pricing, optimizing conversion, expanding revenue, or when users ask about pricing strategy, monetization, or revenue optimization.

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 go-to-market strategy including launch plan, messaging, channels, and timing during PRD v0.9 Go-to-Market. Triggers on requests to plan launch, define GTM strategy, or when user asks "how do we launch?", "go-to-market", "launch plan", "marketing strategy", "messaging", "launch channels", "GTM". Outputs GTM- entries with launch plan components.

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 professional email templates for various business scenarios including sales outreach, customer support, internal communications, follow-ups, and apologies. Creates personalized, tone-appropriate templates with subject lines and formatting. Use when users need to write business emails, cold outreach, or professional communications.

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。

On-page SEO optimization techniques including keyword density, meta tags, heading structure, and readability. Use when optimizing existing content or validating new content against SEO requirements.

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 social media content for Twitter/X, LinkedIn, Facebook, Instagram including posts, threads, carousels, and engagement strategies. Use when writing social posts, planning content calendars, or creating viral content.

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 podcast transcripts into comprehensive content marketing suites including blog posts, social media content, newsletters, SEO-optimized articles, and timestamps. Use when user provides podcast transcripts or wants to repurpose podcast content.

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。

Fetch Twitter/X post content by URL using jina.ai API to bypass JavaScript restrictions. Use when Claude needs to retrieve tweet content including author, timestamp, post text, images, and thread replies. Supports individual posts or batch fetching from x.com or twitter.com URLs.

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。

Transcribe local audio or video with Volcengine Doubao file ASR, including BigASR 1.0 Turbo direct upload and asynchronous 1.0 standard, 1.0 idle, or 2.0 standard jobs through TOS. Use when the user asks for 火山引擎、豆包语音、录音文件识别、音视频转文字、录音稿或字幕。作为 `lecture-to-md` 的可选云端 ASR 子 skill；默认走本地 sherpa-onnx X-ASR，仅在用户明确要求云端 API 或需更高准确率时启用。

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。

Competitive Ad Intelligence. Analyzes competitor advertising strategies including platform identification, landing page analysis, targeting reverse-engineering, positioning gaps, creative approach evaluation, and builds a "beat the competition" strategy with a competitor ad swipe file template.

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。

Choose and validate RuVector or AgentDB memory for an agent application, including provenance, isolation, retention and workload-specific retrieval benchmarks.

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 the Crabbox wrapper for OpenClaw remote validation across Linux, macOS, Windows, and WSL2, including delegated Blacksmith Testbox proof. Report the actual provider and id.

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。

AI creative director with recursive self-assessment. Generates concepts using world-class methodologies (SIT, TRIZ, Lateral Thinking, bisociation), scores against 6 weighted criteria with Cannes/D&AD/HumanKind calibration, and recursively refines until the 9+ threshold is reached. Accepts briefs in any format — text, voice transcript, PDF, or raw notes. Use when the user asks to generate creative concepts, brainstorm campaign ideas, develop a Big Idea or campaign platform, evaluate or critique existing creative work, find consumer insights, or shares a brief for ideation — including activations, PR-stunts, brand utility, experiential, and non-advertising ideas. Calibrates against a library of 569 legendary campaigns (P01-P18 pattern map) to detect saturation and ensure originality. Do not use for media planning, production budgeting, brand identity/logo design, copywriting final drafts, or market research data collection.

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 FIRE analysis cards, Chinese knowledge notes, or project-note JSON into validated EPUB books with directory cards, chapter cards, keyword index cards, backlinks, and GitHub-shareable ebook assets. Use when Codex needs to teach or automate the path from `fire-analysis-card` outputs to `project-note-json-to-epub` style EPUB files, including sample JSON manuscripts and attached `.epub` deliverables.

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 production-ready visual intent for Blender scenes and standalone assets, including focal hierarchy, depth layers, visual flow, scale and repetition rhythm, camera mobility, representation fidelity, and performance budgets. Use for original or reference-guided Blender work before construction choices are executed, especially environments, architecture, products, hero assets, or scenes whose modeling quality depends on coordinated spatial design rather than isolated objects.

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, calibrate, layer, and validate production Blender materials including bare and coated metal, brushed steel, rust, aged and wet wood planks, stone, concrete, plastic, glass, fabric, water, dirt, moss, and wetness. Use when material identity, UV direction, real-world texture scale, PBR channel handling, roughness response, anisotropy, normal/bump/displacement, transmission, volume absorption, aging, or surface layering determines realism.

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, configure, test, bake, and validate Blender physical effects including Cloth, Soft Body, Rigid Body, collisions, constraints, Fluid, smoke, fire, Dynamic Paint, Ocean, Wave, particles, Boids, force fields, fracture, debris, and secondary effects. Use when gravity, inertia, collision, flexibility, pressure, flow, flocking, emission, or breaking causes the requested result.

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。

Select, build, and validate Blender deformation and motion-control systems including Simple Deform, Curve, Lattice, Mesh Deform, Shrinkwrap, Surface Deform, Armature, shape keys, hooks, constraints, and drivers. Use when an object must bend, twist, taper, follow a path, conform to a surface, deform smoothly, articulate, or move through controlled mechanical relationships.

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。

Plan, build, place, and validate physically meaningful Blender object assemblies, including manufactured part boundaries, mechanical seams, mounting interfaces, pivots, hierarchy, constraints, instancing, modular grids, connector planes, and precision snapping. Use when several objects must form one product, machine, architectural module, vehicle, furniture item, mechanism, or environment structure without incorrectly welding independent parts, eyeballing alignment, or leaving continuous shells as overlapping primitives.

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 generates comprehensive unit tests for functions, classes, and modules. Use when you need to create tests for Python (pytest, unittest) or Java (JUnit, TestNG) code. Generates tests with comprehensive coverage including happy paths, edge cases, and error conditions. Analyzes existing test patterns in the codebase to match style and conventions. Supports mocking, parameterized tests, fixtures, and follows best practices for each framework.

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。

Analyze source code statically to detect potential functional bugs including null dereferences, incorrect condition checks, unreachable code, inconsistent state updates, logic errors, resource leaks, and type mismatches. Report suspicious code locations with detailed explanations, severity levels, and confidence assessments. Use when reviewing code for bugs, performing code audits, or when the user asks to find bugs, detect issues, analyze code for problems, or perform static analysis.

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 comprehensive test case documentation from test code, test framework output, existing test docs, and source code context. Use when documenting test suites, creating test specifications, generating test coverage reports, onboarding developers to testing practices, or preparing QA documentation. Analyzes test functions (pytest, unittest) to extract test names, docstrings, assertions, and organization, then produces structured markdown with both overview-level summaries and detailed test case specifications including purpose, preconditions, steps, expected results, and test data. Triggers when users ask to document tests, generate test specifications, create test reports, summarize test coverage, or explain what tests do.

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。

Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.

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。

Statically analyze code to detect security vulnerabilities including buffer overflows, injection risks (SQL, command, XSS), insecure deserialization, improper authentication, hard-coded credentials, and unsafe cryptography. Use when: (1) Performing security code review, (2) Analyzing code for OWASP Top 10 vulnerabilities, (3) Identifying CWE-classified weaknesses, (4) Generating security audit reports, (5) Reviewing code before deployment, or (6) Assessing third-party code security. Findings categorized by CWE ID and severity (Critical/High/Medium/Low).

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。

Instrument programs (Python, C/C++, Java) to capture snapshots of key program states at runtime, including variables, memory, and call stacks. Use when you need to debug complex issues, reproduce test cases, prepare traces for formal verification, or analyze program execution. Supports manual instrumentation points, automatic function/method instrumentation, and conditional triggers. Outputs structured JSON snapshots for debugging, replay, and verification workflows.

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。