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Found 1,279 Skills
Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.
Instrument, trace, evaluate, and monitor LLM applications and AI agents with LangSmith. Use when setting up observability for LLM pipelines, running offline or online evaluations, managing prompts in the Prompt Hub, creating datasets for regression testing, or deploying agent servers. Triggers on: langsmith, langchain tracing, llm tracing, llm observability, llm evaluation, trace llm calls, @traceable, wrap_openai, langsmith evaluate, langsmith dataset, langsmith feedback, langsmith prompt hub, langsmith project, llm monitoring, llm debugging, llm quality, openevals, langsmith cli, langsmith experiment, annotate llm, llm judge.
Yoast SEO platform help — the most widely used WordPress SEO plugin (13M+ installs) with a Shopify app. Covers content analysis, readability scoring, focus keyphrase optimization, schema/structured data output, redirect manager, internal linking suggestions, AI-generated meta descriptions, llms.txt for AI search, REST API for headless WordPress, and WooCommerce product SEO. Use when Yoast readability or SEO score won't turn green, schema markup isn't showing in Google Search Console, your site slowed down after installing Yoast, you need to set Yoast fields via REST API for headless publishing, Yoast update caused a critical error, or you want to configure Yoast for Shopify. Do NOT use for general SEO strategy without Yoast context (use /seo-audit or /sales-semrush).
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
Fine-tune any HuggingFace CV / VLM / LLM model on local NVIDIA GPUs inside an NGC PyTorch container. Use when the user wants to fine-tune a HuggingFace model (full or LoRA), train a vision / VLM / LLM model end-to-end, generate a reproducible HF training pipeline, smoke-test a HuggingFace model locally before scale-up, push a fine-tuned model to the HF Hub with a model card, or emit a self-contained rerun skill for an existing HuggingFace finetune. Supports image classification, object detection, semantic / instance / panoptic segmentation, depth estimation, image-text-to-text VLM (SFT / LoRA), and LLM SFT / DPO / GRPO. Six-step workflow: inspect and qualify, hardware and NGC image, research, generate and smoke, train + eval + infer, push and emit rerun skill.
Build MCP servers with TypeScript on Cloudflare Workers. Covers tools, resources, prompts, tasks, authentication (API keys, OAuth, Zero Trust), and Cloudflare service integrations. Prevents 20 documented errors. Use when exposing APIs to LLMs or troubleshooting export syntax errors, transport leaks, server instance reuse bugs, CORS misconfigurations, or task validation errors.
Comprehensive guide for writing modern Neo4j Cypher read queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, and Quantified Path Patterns (QPP) for efficient graph traversal.
Query the ExoPriors Scry API -- SQL-over-HTTPS search across 229M+ entities spanning forums, papers, social media, government records, and prediction markets. Includes cross-platform author identity resolution (actors, people, aliases), OpenAlex academic graph navigation (authors, citations, institutions, concepts), shareable artifacts, and structured agent judgements. Use when the task involves: Scry API, ExoPriors, /v1/scry/query, scry.search, scry.entities, materialized views, corpus search, epistemic infrastructure, 229M entities, lexical search, BM25, structured agent judgements, scry shares, cross-corpus analysis, who is this person, cross-platform identity, OpenAlex, citation graph, coauthor graph, academic papers, author lookup. NOT for: semantic/vector search composition or embedding algebra (use scry-vectors), LLM-based reranking (use scry-rerank), or the user's own local Postgres / non-ExoPriors data sources.
Use this when the user explicitly requests to "verify/optimize in-text citations of the `{topic}_review.tex` review" or to "run check-review-alignment". Use the host AI's semantic understanding to verify each citation against the literature content one by one. **Only when fatal citation errors are found**, make minimal rewrites to the "sentences containing citations", and reuse the rendering script of `systematic-literature-review` to output PDF/Word (the script does not directly call the LLM API locally). Core principle: **Do not modify for the sake of modifying**. When it is uncertain whether it is a fatal error, keep the original content and issue a warning in the report. ⚠️ Not applicable in the following cases: - The user only wants to generate the main body of a systematic review (should use systematic-literature-review) - The user only wants to add/verify BibTeX entries (should use a dedicated bib management process)
Technical documentation discovery via context7 and web search. Capabilities: library/framework docs lookup, topic-specific search. Keywords: llms.txt, context7, documentation, library docs, API docs. Use when: searching library documentation, finding framework guides, looking up API references.
Use Claude Code's full tool system with any OpenAI-compatible LLM — GPT-4o, DeepSeek, Gemini, Ollama, and 200+ models via environment variable configuration.
A prompt repetition technique for improving LLM accuracy. Achieves significant performance gains in 67% (47/70) of 70 benchmarks. Automatically applied on lightweight models (haiku, flash, mini).