Claude Skills

Complete fal.ai text-to-video system. PROACTIVELY activate for: (1) Kling 2.0/2.5/2.6 Pro video generation, (2) Sora 2 for creative videos, (3) LTX Video with audio, (4) Runway Gen-3 Turbo for fast iteration, (5) Luma Dream Machine, (6) Video duration and aspect ratio, (7) Motion prompt engineering, (8) Camera movement keywords. Provides: Model endpoints, quality tiers, prompt structure, duration options. Ensures cinematic video generation with proper motion description.

Complete fal.ai serverless deployment system. PROACTIVELY activate for: (1) Creating fal.App class, (2) GPU machine selection (T4/A10G/A100/H100), (3) setup() for model loading, (4) @fal.endpoint decorators, (5) Persistent volumes for weights, (6) Secrets management, (7) Scaling configuration (min/max concurrency), (8) Multi-GPU deployment, (9) fal deploy commands, (10) Local development with fal run. Provides: App structure, Dockerfile patterns, deployment commands, scaling config. Ensures production-ready serverless ML deployment.

Complete fal.ai optimization system. PROACTIVELY activate for: (1) Queue vs run performance, (2) Parallel request batching, (3) Streaming for real-time UI, (4) WebSocket for interactive apps, (5) Model cost comparison, (6) Image size optimization, (7) Inference step tuning, (8) Webhook vs polling, (9) Result caching by seed, (10) Serverless scaling config. Provides: Parallel patterns, cost strategies, caching examples, monitoring setup. Ensures optimal performance and cost-effective usage.

Complete fal.ai model selection system. PROACTIVELY activate for: (1) Choosing image generation models (FLUX, SDXL), (2) Choosing video models (Kling, Sora, LTX), (3) Choosing audio models (Whisper, ElevenLabs), (4) Model quality vs speed comparison, (5) Cost optimization by model tier, (6) 3D generation models, (7) Model-specific parameters, (8) Development vs production model selection. Provides: Model comparison tables, decision trees, pricing tiers, performance benchmarks. Ensures optimal model selection for quality, speed, and cost.

Monitor brand reputation and sentiment across Twitter/X, Reddit, Instagram, YouTube, and LinkedIn using anysite MCP server. Track brand mentions, analyze customer sentiment, monitor social conversations, identify reputation issues, and measure brand health. Supports social media listening, sentiment analysis, mention tracking, and crisis detection. Use when users need to monitor brand mentions, track customer sentiment, identify reputation risks, analyze brand perception, or measure social media presence and brand health across platforms.

Read-only static security audit of Claude Code skills, commands, and plugins. Analyzes SKILL.md frontmatter, body content, supporting scripts, and hooks for security risks. Use this skill when the user asks to "audit a skill", "review skill security", "check SKILL.md for risks", "scan a plugin for dangerous patterns", "verify skill safety", "check skill permissions", "analyze skill hooks", "audit a skill from GitHub", "review a remote skill", "check a skill by URL", or needs a security assessment of any Claude Code skill, command, or plugin before enabling it.

ElevenLabs text-to-speech with mac-style say UX.

Complete fal.ai image-to-video system. PROACTIVELY activate for: (1) Kling 2.5/2.6 Pro image animation, (2) MiniMax Hailuo with prompt optimizer, (3) LTX image-to-video, (4) Runway Gen-3 Turbo, (5) Luma Dream Machine with loop, (6) Stable Video Diffusion, (7) Motion description prompts, (8) Portrait/product animation workflows. Provides: Model endpoints, motion keywords, animation techniques, workflow examples. Ensures natural image animation with proper motion description.

Complete fal.ai image-to-image system. PROACTIVELY activate for: (1) FLUX image-to-image transformation, (2) ControlNet (canny, depth, pose), (3) Inpainting with masks, (4) Upscaling (ESRGAN, Clarity), (5) Background removal (BiRefNet, RemBG), (6) Face restoration (GFPGAN, CodeFormer), (7) IP-Adapter style transfer, (8) Strength parameter tuning. Provides: Transformation endpoints, mask formats, ControlNet types, upscaling options. Ensures proper image editing and enhancement workflows.

Complete fal.ai audio system. PROACTIVELY activate for: (1) Whisper speech-to-text, (2) Transcription with timestamps, (3) Translation to English, (4) F5-TTS voice cloning, (5) ElevenLabs premium TTS, (6) Kokoro multi-language TTS, (7) XTTS open-source cloning, (8) Subtitle generation (SRT), (9) Audio file formats. Provides: STT/TTS endpoints, language codes, voice cloning setup, timestamp formatting. Ensures accurate transcription and natural speech synthesis.

Complete fal.ai API reference system. PROACTIVELY activate for: (1) @fal-ai/client JavaScript setup, (2) fal_client Python setup, (3) fal.subscribe/run/stream methods, (4) Queue management (submit/status/result), (5) Webhook configuration, (6) File upload to fal.media, (7) REST API endpoints, (8) Real-time WebSocket connections, (9) Error handling patterns. Provides: Client configuration, method signatures, queue workflow, webhook payloads, common parameters. Ensures correct API usage with proper authentication and error handling.

Complete Python type hints system. PROACTIVELY activate for: (1) Built-in generics (list[str], dict[str, int]), (2) Union types (str | None), (3) Type parameter syntax 3.12+, (4) Protocol for structural typing, (5) TypedDict for dict schemas, (6) Literal and Final types, (7) TypeGuard and TypeIs, (8) ParamSpec for decorators, (9) Mypy/Pyright configuration. Provides: Type syntax, Protocol patterns, TypedDict, mypy config. Ensures static type safety with gradual typing strategy.

Complete Python testing system. PROACTIVELY activate for: (1) pytest fundamentals, (2) Fixtures and scopes, (3) Parameterized tests, (4) Mocking with pytest-mock, (5) Async testing with pytest-asyncio, (6) Coverage configuration, (7) FastAPI testing with httpx, (8) Property-based testing with hypothesis, (9) Snapshot testing. Provides: pytest patterns, fixture examples, mock setup, coverage config. Ensures comprehensive test coverage with best practices.

Complete OpenCV computer vision system for Python. PROACTIVELY activate for: (1) Image loading with cv2.imread (BGR format gotcha), (2) Video capture with cv2.VideoCapture, (3) Color space conversion (BGR to RGB, HSV, grayscale), (4) Image filtering (GaussianBlur, medianBlur, bilateralFilter), (5) Edge detection (Canny), (6) Contour detection with cv2.findContours, (7) Image resizing with interpolation methods, (8) Template matching, (9) Feature detection (SIFT, ORB, AKAZE), (10) Drawing functions (rectangle, circle, text), (11) Video writing with cv2.VideoWriter, (12) Morphological operations, (13) Deep learning with cv2.dnn module, (14) GPU acceleration with cv2.cuda, (15) Coordinate system (x,y vs row,col) gotchas. Provides: Image processing patterns, video capture/writing, memory management, performance optimization, Jupyter notebook workarounds. Ensures correct BGR handling and memory-safe OpenCV usage.

Set up a Ralph Wiggum task - runs Phase 1 (requirements interview) and Phase 2 (implementation planning), then stops with instructions to run the building loop. Creates namespaced specs in specs/[task-name]/.

Bash 5.3 new features and modern patterns (2025)

Add Architecture Decision Record (ADR) entries to an ADR file. This skill should be used when recording significant architectural decisions during development. It handles formatting and appending entries using Michael Nygard's ADR template.

Tailwind CSS v4 fundamentals covering installation, CSS-first configuration, design systems, and 2025/2026 best practices

Security-first bash scripting patterns for 2025 (mandatory validation, zero-trust)