This skill should be used when users request help optimizing, improving, or refining their prompts or instructions for AI models. Use this skill when users provide vague, unclear, or poorly structured prompts and need assistance transforming them into clear, effective, and well-structured instructions that AI models…
Write better prompts for Kling 3.0 AI video generation. Use when the user wants to create, write, improve, or refine prompts — text-to-video, image-to-video, keyframes, multi-shot sequences, or dialogue scenes.
End-to-end crop disease detection skill supporting classification (which disease), detection (where is the disease), and segmentation (disease region) at three granularity levels, with multiple SOTA models and training strategies built in.
AI product development wisdom from 120+ Lenny's Podcast episodes. Covers AI product strategy, LLM applications, ML integration, AI agents, and building AI-native products. Use when building AI products, integrating LLMs, designing AI features, or developing AI strategy.
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance…
A set of instructions for controlling LiteLLM usage and spending. It covers virtual keys, which can identify users or applications, along with budgets, limits, and request labels.
Magic ETL dataflows via community-domo-cli — list, get-definition, create, update, run, execution status; JSON DAG actions, transforms, joins. Use when automating dataflows with the community Domo CLI end-to-end. For REST/Java-CLI–first flows or mixed API patterns, use magic-etl instead.
Use when the user wants to make Claude Code cheaper by improving prompt-cache hit rate, especially for MiMo or other OpenAI-compatible routed models. Trigger for Claude Code cache-hit analysis, cache-friendly command construction, paired baseline vs candidate evaluation, token usage logging, or use of…
Tactical LLM-friendly site optimization for making a website the best citable answer for AI assistants and RAG retrieval. Use for auditing and optimizing sites such as mlllm.io for llms.txt, AI citation readiness, pillar pages, direct-answer blocks, TL;DR sections, FAQ/schema, article/news schema, topic-to-URL…
Automate an MLOps project with mise tasks, lefthook hooks, Docker images, GitHub Actions, and MLflow tracking on a SQL backend. Use when adding a task runner, git hooks, CI/CD, or experiment tracking to a working package.
A tool that uses the Gemini web service to generate text and images. It can accept reference images and keep context across multiple messages using a session.
Use when writing Deepbox code, examples, or full projects so imports, module selection, types, errors, docs pages, and framework-specific patterns stay accurate.
A document question-answering setup that finds relevant passages in your files and uses a language model to generate answers. It accepts PDF, DOCX, TXT, and Markdown files, and can store searchable document collections.
A Russian-language skill for searching a local knowledge base built from documents and other sources such as web pages, YouTube, audio, and Obsidian notes.
Refresh the operational-to-ontology projection, validate class and property contracts, enrich changed nodes for semantic search, and inspect Oracle Scheduler evidence.
Builds Data Vault 2 models in dbt with the datavault4dbt package — staging, hubs, links, satellites, and business-vault entities — using the YAML-metadata macro pattern with correct hashkeys, hashdiffs, naming, and materializations. Use when creating or editing datavault4dbt models, setting up a raw vault, choosing…
Guide for building Graph Neural Networks with PyTorch Geometric (PyG). Use this skill whenever the user asks about graph neural networks, GNNs, node classification, link prediction, graph classification, message passing networks, heterogeneous graphs, neighbor sampling, or any task involving torchgeometric / PyG. Also…
AI media generation via deAPI. Transcribe YouTube/audio/video, generate images from text, text-to-speech, OCR, remove backgrounds, upscale images, create videos, generate embeddings. 10-20x cheaper than OpenAI/Replicate.
Use when (re)indexing a Sweet Search project. Runs the full-profile indexer with GPU model prewarming (CoreML cascade on M3+, candle Metal on M1/M2, ORT CPU elsewhere), kills ORT CPU models during indexing to avoid memory contention, and rewarms them for query readiness on completion. Incremental runs under 20 files…
Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.
Use when the user needs self-hosted or local Chroma for semantic search, including ChromaClient, HttpClient, or Python EphemeralClient, local persistence, Docker or chroma run, or OSS Chroma without Chroma Cloud features.
Use when building a RAG pipeline that ingests PDFs, Excel, CSV, or images — especially when debugging silent data loss, choosing between OCR tools, or handling edge cases like scanned pages, merged cells, or embedded charts.
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originalMIT
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: