Results for “generation”

Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augmented.

Search, explore, and run fal.ai generative AI models (image generation, video, audio, 3D). Use when user wants to generate images, videos, or other media with AI models.

Generates, remixes, polls, lists, downloads, and deletes Sora videos via OpenAI's video API using a bundled CLI.

Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.

Use the mcporter CLI to list, configure, auth, and call MCP servers/tools directly (HTTP or stdio), including ad-hoc servers, config edits, and CLI/type generation.

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

Pose-conditioned generation on RunComfy via the `runcomfy` CLI. Routes across Kling 2-6 Motion Control Pro / Standard (transfer the motion / blocking of a reference video onto a target character), community Wan 2-2 Animate (audio-driven character animation with pose conditioning), and Z-Image Turbo ControlNet LoRA (pose-conditioned image generation from an OpenPose / DWPose / canny / depth control image). Picks the right route based on video vs still and stylized vs photoreal. Triggers on "controlnet", "control net", "pose control", "openpose", "DWPose", "transfer pose", "motion control", "pose driven", "character pose", "depth control", "canny edge", "use this pose", or any explicit ask to condition generation on a pose / skeleton / motion / depth / canny reference.

Pose-conditioned generation on RunComfy via the `runcomfy` CLI. Routes across Kling 2-6 Motion Control Pro / Standard (transfer the motion / blocking of a reference video onto a target character), community Wan 2-2 Animate (audio-driven character animation with pose conditioning), and Z-Image Turbo ControlNet LoRA (pose-conditioned image generation from an OpenPose / DWPose / canny / depth control image). Picks the right route based on video vs still and stylized vs photoreal. Triggers on "controlnet", "control net", "pose control", "openpose", "DWPose", "transfer pose", "motion control", "pose driven", "character pose", "depth control", "canny edge", "use this pose", or any explicit ask to condition generation on a pose / skeleton / motion / depth / canny reference.

Pose-conditioned generation on RunComfy via the `runcomfy` CLI. Routes across Kling 2-6 Motion Control Pro / Standard (transfer the motion / blocking of a reference video onto a target character), community Wan 2-2 Animate (audio-driven character animation with pose conditioning), and Z-Image Turbo ControlNet LoRA (pose-conditioned image generation from an OpenPose / DWPose / canny / depth control image). Picks the right route based on video vs still and stylized vs photoreal. Triggers on "controlnet", "control net", "pose control", "openpose", "DWPose", "transfer pose", "motion control", "pose driven", "character pose", "depth control", "canny edge", "use this pose", or any explicit ask to condition generation on a pose / skeleton / motion / depth / canny reference.

Giskard RAGET (RAG Evaluation Toolkit): automatic testset generation (simple / complex / distracting / conversational), component-level scoring (retriever / generator / rewriter), hallucination and bias tests, CI integration. Compared to RAGAS and DeepEval. USE WHEN: user mentions "Giskard", "RAGET", "Giskard RAG toolkit", "automatic testset generation", "component-level RAG scoring", "hallucination test Giskard" DO NOT USE FOR: general RAGAS usage - use `rag-evaluation`; Stanford ARES - use `ares-framework`; CI/CD wiring - use `continuous-evaluation`

Generates unit tests, analyzes coverage gaps, and guides red-green-refactor workflows across Jest, Pytest, JUnit, Vitest, and Mocha.

Scaffold Next.js/React projects, generate components, analyze bundle sizes, and apply React/Next.js optimization patterns.

Plan and create lead magnets that capture emails, generate qualified leads, and drive product adoption.

Transforms sketches and text descriptions into photorealistic 3D renders for product design, with control over materials, lighting, and style.

Pays for external capabilities like image generation, real-time X/Twitter data, and alternative LLMs via micropayments without requiring API keys.

Generate a complete, production-ready application from an OpenAPI specification using the active framework's conventions and best practices.

Generate native .pptx slides using PptxGenJS with strict design rules, color palette adherence, and automated QA verification.

Generate polished code snippet images with syntax highlighting, supporting PNG, SVG, and WebP exports via the snipgrapher CLI.

Optimize Product Hunt launches with research, gallery image generation, and a day-of playbook.

Generates high-fidelity images using reasoning-driven prompts and structured creative briefs via muapi.ai.

Generates professional slide deck images from content, with style presets, audience targeting, and optional PPTX/PDF export.

Generates video clips from text prompts using Google's Veo API, supporting configurable duration, aspect ratio, and model selection.

Guides a structured requirements workflow from topic intake through brainstorming, spec and plan writing, user review, SRS generation, validation, and improvement reporting.

Analyzes project memory and git history to track improvements, update documentation, and generate LinkedIn content drafts after significant sessions.

Generates illustrations, diagrams, flowcharts, infographics, thumbnails, comics, and icons using multiple rendering backends, with workflows for various visual content types.

Guides a structured software requirements specification workflow from topic intake through brainstorming, spec writing, planning, review, SRS generation, validation, and improvement reporting.

Guides a structured requirements pipeline from topic intake through brainstorming, spec writing, planning, user review, SRS generation, validation, and improvement reporting.

Analyzes competitive landscapes using Porter's 5 Forces, positioning matrices, and battlecard generation to secure market position.

Measures distributional similarity between original GAN-generated images and their semantically manipulated counterparts using the Fréchet Inception Distance (FID) metric.

Computes the InfoLM metric from torchmetrics for evaluating text generation against ground truth, with configurable information measures and sentence-level scoring.

Use this when designing scripts or small tools for automation, maintenance, CI tasks, file manipulation, config generation, or local workflow support across shell, Python, or mixed environments.

Agent wallet, identity, and paid tools in one package. Register an agent, fund it via Stripe or USDC, then use the balance for web search, AI image generation, AI video generation, AI music creation, X/Twitter search, email send/receive, SMS and voice calls, contacts management, and 100+ LLM models. The funding and identity layer for autonomous agents that need to spend money, send messages, make phone calls, or call paid APIs.

Generate a complete set of favicons from a source image and update the project's HTML with appropriate link tags.

Generate sound effects from text descriptions using ElevenLabs, with support for looping, duration control, and prompt influence tuning.

Generate images using AI from OpenAI or Google Gemini, with support for textures, icons, sprites, and visual assets.

Generates Claude Code skills from the Trail of Bits Testing Handbook for security testing tools and techniques.