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Found 1,310 Skills
Comprehensive LLM audit. Model currency, prompt quality, evals, observability, CI/CD. Ensures all LLM-powered features follow best practices and are properly instrumented. Auto-invoke when: model names/versions mentioned, AI provider config, prompt changes, .env with AI keys, aiProviders.ts or prompts.ts modified, AI-related PRs. CRITICAL: Training data lags months. ALWAYS web search before LLM decisions.
Extract edge hints from daily market observations and news reactions, with optional LLM ideation, and output canonical hints.yaml for downstream concept synthesis and auto detection.
Interactive tutorial teaching Snowflake Cortex CLASSIFY_TEXT for categorizing unstructured text. Guide users through classifying customer reviews using Python and SQL. Use when user wants to learn text classification, Cortex LLM functions, or analyze unstructured feedback data.
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
Rewrite AI-sounding text into natural, human writing by removing common LLM patterns while preserving meaning and tone.
Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
Use when you want rubric based LLM quality scoring on generated outputs; pair with addon-deterministic-eval-suite.
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
Convert files, URLs, and media to markdown using the markit-ai CLI and SDK with pluggable converters and LLM support.
This skill should be used when the user asks to "run a tracking cycle", "measure AI visibility", "check share of voice", "run Morphiq Track", "track citations", "check GEO score", "generate prompts", "run content creation workflow", or mentions monitoring LLM mentions, running content creation workflows, measuring brand visibility, or generating query fanout content. Queries multiple LLM providers, produces delta reports, and maintains MORPHIQ-TRACKER.md as the persistent state file for the entire pipeline.
Set up and maintain a persistent, LLM-managed knowledge base for a digital health project — turning clinical observations, papers, interviews, and planning docs into a compounding, interlinked wiki.
Atlas Cloud API integration skill — quickly call 300+ AI image generation, video generation, and LLM models through a unified API. Use this skill when the user needs to integrate AI image generation (e.g., Flux, Seedream, DALL-E), AI video generation (e.g., Kling, Sora, Seedance), or call LLM APIs (OpenAI-compatible format) into their project. Applicable scenarios include: generating images, generating videos, calling large language models, using Atlas Cloud API, configuring ATLASCLOUD_API_KEY, querying available model lists, searching models by keyword, uploading local images/media files, one-step quick generation, image-to-video, text-to-image, text-to-video, AI content creation tool integration. Even if the user doesn't explicitly mention Atlas Cloud, this skill should be considered whenever AI media generation API integration development is involved.