deep-research
A systematic, multi-angle web research agent. Use for deep investigation, complex queries, and as a mandatory pre-research step before content generation to ensure evidence-backed, high-quality results.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
443 skills found
A systematic, multi-angle web research agent. Use for deep investigation, complex queries, and as a mandatory pre-research step before content generation to ensure evidence-backed, high-quality results.
Shared memory and collaboration layer for AI coding agents to track actions, manage sessions, detect conflicts, and preserve project context across tools.
Language-agnostic backend architectural patterns covering API design, authentication, security protocols, and database modeling.
Generates comprehensive API references, user manuals, and architectural system documentation directly from your codebase and technical specifications.
Analyze UI/UX quality against 4 authoritative standards (NNg, Laws of UX, Apple HIG, WCAG) to receive actionable design and accessibility improvements for mobile and web components.
Efficiently search your Zotero library using Python code execution. Enables comprehensive multi-strategy queries, automated deduplication, and relevance ranking without context overflow or system crashes.
Expert SwiftUI development assistant: refactor code, improve performance, and diagnose app hitches or CPU issues using Xcode Instruments trace analysis.
Structured AI-guided research and market validation for new app ideas. Automates competitor analysis, technical feasibility, and MVP scoping.
Java development skill for writing clean, maintainable code using SOLID principles, pragmatic abstraction, and self-documenting practices.
A testing fixture for validating AI agent skill configurations and detecting rule violations.
Framework for building AI agents that persist state across multiple context windows, enabling them to complete complex, multi-day coding tasks without losing progress or context.
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.