Total 42,909 skills, AI & Machine Learning has 6876 skills
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This skill should be used when creating a Claude Code slash command. Use when users ask to "create a command", "make a slash command", "add a command", or want to document a workflow as a reusable command. Essential for creating optimized, agent-executable slash commands with proper structure and best practices.
Create and manage Claude Code plugins with proper structure, manifests, and marketplace integration. Use when creating plugins for a marketplace, adding plugin components (commands, agents, hooks), bumping plugin versions, or working with plugin.json/marketplace.json manifests.
Spawn Codex subagents via background shell to offload context-heavy work. Use for: deep research (3+ searches), codebase exploration (8+ files), multi-step workflows, exploratory tasks, long-running operations, documentation generation, or any other task where the intermediate steps will use large numbers of tokens.
Generate videos with Pruna P-Video and WAN models via inference.sh CLI. Models: P-Video, WAN-T2V, WAN-I2V. Capabilities: text-to-video, image-to-video, audio support, 720p/1080p, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna video, p-video, pruna ai video, fast video generation, optimized video, wan t2v, wan i2v, economic video generation, cheap video generation, pruna text to video, pruna image to video
Create or edit images with Pilio Nano Banana 2 through the unified Pilio developer API. Use when the user wants Nano Banana 2 text-to-image generation, reference-image editing, product posters, or image composition from local inputs.
Generate images with Pruna P-Image models via inference.sh CLI. Models: P-Image, P-Image-LoRA, P-Image-Edit, P-Image-Edit-LoRA. Capabilities: text-to-image, image editing, LoRA styles, multi-image compositing, fast inference. Pruna optimizes models for speed without quality loss. Triggers: pruna, p-image, pruna image, fast image generation, optimized flux, pruna ai, p image, fast ai image, economic image generation, cheap image generation
Generate images and videos via Higgsfield AI through 30+ models including Nano Banana 2, Soul V2, Veo 3.1, Kling 3.0, Seedance 2.0, Flux 2, GPT Image 2, plus Marketing Studio for branded ad video/image with curated avatars and imported products. Use when: "generate an image", "make a picture", "create artwork", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "stylize a photo", "remix this image", "produce a clip", "render a scene", "create an ad", "make a UGC video", "generate marketing video", "make a product demo", "create unboxing", "TV spot", "virtual try-on", "product showcase", "brand video", "presenter video for product", "import product from URL", "create avatar for ad". Supports text-to-image, image-to-image, image-to-video, reference-based generation, and Marketing Studio (avatars + products + ad modes). Auto-detects whether passed IDs are uploads or previous jobs. Chain with higgsfield-soul-id when the user wants their face in the output. NOT for: training Soul Character (use higgsfield-soul-id), professional product photoshoots with mode-specific prompt enhancement (use higgsfield-product-photoshoot), text-only / chat / TTS tasks.
Generate brand-quality product images via mode-specific prompt enhancement on Higgsfield's gpt_image_2 model. The single entry point for any professional brand visual involving a product. Use when: "make a product photo", "studio shot", "lifestyle photo", "in use", "Pinterest pin", "hero banner", "website header", "carousel", "Meta ads", "ad creatives", "model wearing", "virtual try-on", "person holding product", "closeup with hands", "levitating product", "floating", "splash shot", "CGI style", "surreal product", "restyle", "Christmas version", "in [aesthetic] style", or any request involving a product, brand, or paid social creative. Modes: product_shot, lifestyle_scene, closeup_product_with_person, pinterest_pin, hero_banner, social_carousel, ad_creative_pack, virtual_model_tryout, conceptual_product, restyle. Backend assembles the final prompt — never write gpt_image_2 prompts freehand. Always go through this skill. NOT for: raw text-to-image with no brand/product (use higgsfield-generate), branded marketing video with avatars (use higgsfield-generate's Marketing Studio), Soul Character training (use higgsfield-soul-id).
Train a Soul Character — a personalized model on a person's face that Higgsfield uses for identity-faithful image and video generation. Use when: "create my Soul", "train my face", "make my digital twin", "build me an avatar", "learn my appearance", "create a character of me", "set up identity for video", "I want my face in generated images". Chain: train Soul (one-time, returns reference_id) → use in higgsfield-generate via `--soul-id <id>` with models like `text2image_soul_v2` or `soul_cinema_studio`. NOT for: one-shot face swaps (use higgsfield-generate with --image), named-character / non-photo avatars (use higgsfield-generate with prompt).
Show real token usage and estimated savings for the current session. Reads directly from the Claude Code session log — no AI estimation. Triggers on /caveman-stats. Output is injected by the mode-tracker hook; the model itself does not compute the numbers.
Decision guide for delegating to caveman-style subagents. Tells the main thread WHEN to spawn `cavecrew-investigator` (locate code), `cavecrew-builder` (1-2 file edit), or `cavecrew-reviewer` (diff review) instead of doing the work inline or using vanilla `Explore`. Subagent output is caveman-compressed so the tool-result injected back into main context is ~60% smaller — main context lasts longer across long sessions. Trigger: "delegate to subagent", "use cavecrew", "spawn investigator/builder/reviewer", "save context", "compressed agent output".
Full OpenAI-compatible GPT Image 2 coverage across images/generations, images/edits, and responses with the image_generation tool. Use when the one-shot image helper is not enough - text-to-image, mask edits, multi-image batches, streaming, partial_images, and mixed text+image Responses flows. Reads .env and respects process environment variables; works with any OpenAI-compatible gateway.