memory-management
Uses AgentDB and HNSW vector search to persist patterns and retrieve similar solutions from prior work. Supports learning across tasks, sharing knowledge between agents, and building knowledge bases.
Your agent can do more. Find the skill that makes it happen.
Find your next superpower If you are an agent, refer to our llms.txt for full access.Uses AgentDB and HNSW vector search to persist patterns and retrieve similar solutions from prior work. Supports learning across tasks, sharing knowledge between agents, and building knowledge bases.
Installs the Shopify CLI, authenticates, creates a custom app, stores its access token, and verifies access. Also troubleshoots Shopify authentication and Admin API token issues.
Handles academic papers, web articles, reports, and documentation for non-developer users. Extracts key findings, compares multiple sources, and produces structured research summaries with formatted citations.
Requires writing a test before implementation and observing its failure. Then guides writing minimal code to pass, ensuring the test checks behavior.
Sets up vLLM or SGLang serving on Jetson. Uses upstream vLLM on Thor and Orin with JetPack 7.2 or later, and NVIDIA-AI-IOT vLLM on older Orin systems.
Generates Product Requirements Documents for feature planning and task orchestration in ralph-tui. Includes user stories that can be converted into beads issues or prd.json for automated execution.
Selects a serving stack and memory configuration for LLM or VLM workloads on NVIDIA Jetson. Covers vLLM, SGLang, llama.cpp, and TensorRT Edge-LLM runtime flags.
Provides browser automation and web testing guidance using Selenium WebDriver. Covers element location, waits, and test organization.
Provides a performance optimization agent skill. The description does not specify the target systems or optimization techniques.
Organizes a company's brand portfolio through parent-brand relationships, hierarchies, and naming systems. Supports decisions about branded houses, houses of brands, and extensions under existing brands.
Applies a typed 80-item CORE-EEAT profile to assess content quality and publishing readiness. Examines evidence coverage and veto checks, then produces a plan for improvements.
Structures planning before implementation through explicit requirements, technical design, and task breakdowns. Suits work spanning multiple modules, unclear acceptance criteria, or substantial architectural decisions.
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