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Found 1,288 Skills
Lossless LLM-optimized compression of source documents. Use when the user requests to 'distill documents' or 'create a distillate'.
Text analytics using LLM APIs — sentiment analysis, customer feedback classification, document entity extraction, multi-language support (English/Luganda/Swahili), feedback aggregation, and NLP feature implementation for PHP/Android/iOS. Sources...
Design patterns for the Langroid multi-agent LLM framework. Covers agent configuration, tools, task control, and integrations.
Quick single-paper lookup via AlphaXiv LLM-optimized summaries with tiered source fallback. Use when user says "explain this paper", "summarize paper", pastes an arXiv/AlphaXiv URL, or provides a bare arXiv ID for quick understanding - not for broad literature search.
End-to-end SGLang SOTA performance workflow. Use when a user names an LLM model and wants SGLang to match or beat the best observed vLLM and TensorRT-LLM serving performance by searching each framework's best deployment command, benchmarking them fairly, profiling SGLang if it is slower, identifying kernel/overlap/fusion bottlenecks, patching SGLang code, and revalidating with real model runs.
Char (formerly Hyprnote) platform help — open-source, bot-free, local-first AI meeting notepad with system audio capture, markdown output, plugin SDK, and optional cloud STT/LLM (GPL-3.0). Use when setting up Char on macOS for the first time, speaker identification not working in group meetings, configuring local-only transcription with Cactus or Ollama for full offline use, choosing between Char's cloud STT providers (Deepgram, AssemblyAI, Soniox, OpenAI, etc.), app not launching or bouncing on dock without opening, telemetry concerns with PostHog or Sentry in a local-first app, building a Char plugin or using the automation hooks system, comparing Char to Granola or Meetily or Fathom for privacy, or configuring the CLI for template management. Do NOT use for picking between note-takers generally (use /sales-note-taker) or reviewing a single call for coaching (use /sales-call-review).
Score and compare images using vision LLMs as judges. YAML-defined criteria presets for 11 use cases (text-to-image, photorealism, document OCR, charts, UI, portrait, product, scientific, invoice, alt-text, artistic style). Supports OpenAI, Anthropic, Gemini, Mistral, and OpenRouter as judge providers. Keys auto-decrypted via SOPS + age.
Use this skill whenever an LLM agent needs to search, browse, or download 3D models from Poly Pizza (poly.pizza) using their REST API. Triggers on any task involving: finding free low-poly 3D models, searching the Poly Pizza catalogue, fetching model metadata or download URLs, retrieving popular models, or downloading .glb files from Poly Pizza. Use this skill proactively whenever the agent needs to obtain 3D assets programmatically, even if the user just says "find me a 3D model of X" without mentioning Poly Pizza by name.
Set up an LLM-judge evaluation that extracts canonical use cases for a PostHog feature at scale and streams the results to a Slack channel as a live feed. Use when someone wants to understand how users are actually using a specific AI/LLM-powered feature in production — what they're investigating, what questions they're trying to answer, and what patterns surface — without manually reading hundreds of traces. Assumes the feature emits `$ai_generation` and `$ai_evaluation` events with `$session_id` linkage to the trigger user's recording (the standard setup post the session-summary linkage PRs).
Use when revising existing wiki pages because knowledge has changed, a new piece of information updates or contradicts existing content, or the user wants to directly edit wiki content with LLM assistance.
Extract text from PDFs as structured, semantic Markdown. Use when converting a PDF to Markdown, extracting text from a PDF, processing one or more PDFs into Markdown output, reading PDF contents for analysis, ingesting documents for RAG pipelines, preparing PDFs for LLM context, or any task where PDF text needs to be in a machine-readable format. ALWAYS use this skill when the user has a PDF and needs its content as text or Markdown — even if they don't explicitly say "convert to markdown".
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production. Use when: langfuse, llm observability, llm tracing, prompt management, llm evaluation.