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Found 5,623 Skills
The foundational knowledge distillation pattern for building and maintaining an AI-powered Obsidian wiki. Based on Andrej Karpathy's LLM Wiki architecture. Use this skill whenever the user wants to understand the wiki pattern, set up a new knowledge base, or needs guidance on the three-layer architecture (raw sources → wiki → schema). Also use when discussing knowledge management strategy, wiki structure decisions, or how to organize distilled knowledge. This is the "theory" skill — other skills handle specific operations (ingesting, querying, linting).
When the user wants to apply, document, or enforce brand guidelines for any product or company. Also use when the user mentions 'brand guidelines,' 'brand colors,' 'typography,' 'logo usage,' 'brand voice,' 'visual identity,' 'tone of voice,' 'brand standards,' 'style guide,' 'brand consistency,' or 'company design standards.' Covers color systems, typography, logo rules, imagery guidelines, and tone matrix for any brand — including Anthropic's official identity.
Manage NuGet packages in .NET projects/solutions. Use this skill when adding, removing, or updating NuGet package versions. It enforces using `dotnet` CLI for package management and provides strict procedures for direct file edits only when updating versions.
Complete API for Google NotebookLM - full programmatic access including features not in the web UI. Create notebooks, add sources, generate all artifact types, download in multiple formats. Activates on explicit /notebooklm or intent like "create a podcast about X"
Full-featured Agent Skills management: Search 35+ skills, install locally, star favorites, update from sources. Use when looking for skills, installing new skills, or managing your skill collection.
Fast, low-cost exploration of Robonet trading resources. Browse 8 data tools to explore available trading pairs, technical indicators, Allora ML topics, existing strategies, and backtest results. All tools execute in <1 second with minimal cost (free to $0.001). Use this skill first before building or testing strategies to understand what resources are available.
Neural web search and content extraction using x402-protected APIs. Better than WebSearch for deep research and WebFetch for blocked sites. USE FOR: - Deep web research and investigation - Finding similar pages to a reference URL - Extracting clean text from web pages - Scraping sites that block standard fetchers - Getting direct answers to factual questions - Research requiring multiple sources TRIGGERS: - "research", "investigate", "deep dive", "find sources" - "similar to", "pages like", "more like this" - "scrape", "extract content from", "get the text from" - "blocked site", "can't access", "paywall" - "what is", "explain", "answer this" Use `npx agentcash fetch` for stableenrich.dev endpoints. Prefer Exa for semantic/neural search, Firecrawl for direct scraping.
Extract and parse article content from web sources. Retrieves text, metadata, and structured information from articles while preserving formatting and context.
Analyzes and enforces security protocols on the skill ecosystem. Operates via Audit, Guard, and Trust modes to prevent malicious commands, PII leakage, and excessive permissions.
Enforces an opinionated UI baseline to prevent AI-generated interface slop.
For users needing to conduct systematic literature reviews, literature reviews, related work, or literature research: AI automatically generates search terms, performs multi-source retrieval → deduplication → AI reads and scores each paper one by one (1–10 points for semantic relevance and sub-topic grouping) → selects papers based on high-score priority ratio → automatically generates word budget for the review (70% cited sections + 30% non-cited sections, average of three samplings) → free writing in the style of senior domain experts (fixed sections: abstract, introduction, sub-topics, discussion, future outlook, conclusion), with strict verification of main text word count and number of references, and mandatory export to PDF and Word. Supports multilingual translation and intelligent compilation (en/zh/ja/de/fr/es).
Multi-language code quality standards and review for TypeScript, Python, Go, and Rust. Enforces type safety, security, performance, and maintainability. Use when writing, reviewing, or refactoring code. Includes review process, checklist, and Python PEP 8 deep-dive.