sickn33/agent-memory-mcp
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
npx skills add https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/agent-memory-mcpA hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).
This repo contains 20 individual skills — each has its own dedicated page.
Expert in building 3D experiences for the web - Three.js, React Three Fiber, Spline, WebGL, and interactive 3D scenes. Covers product configurators, 3D portfolios, immersive websites, and bringing depth to web experiences.
Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them.
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
Generate comprehensive, developer-friendly API documentation from code, including endpoints, parameters, examples, and best practices
API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.
Implement secure API design patterns including authentication, authorization, input validation, rate limiting, and protection against common API vulnerabilities
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
Transform audio recordings into professional Markdown documentation with intelligent summaries using LLM integration
Specialized skill for building production-ready serverless applications on AWS. Covers Lambda functions, API Gateway, DynamoDB, SQS/SNS event-driven patterns, SAM/CDK deployment, and cold start optimization.
Expert backend architect specializing in scalable API design, microservices architecture, and distributed systems.
You are a senior backend engineer operating production-grade services under strict architectural and reliability constraints. Use when routes, controllers, services, repositories, express middleware, or prisma database access.
Bash/Linux terminal patterns. Critical commands, piping, error handling, scripting. Use when working on macOS or Linux systems.
Bash scripting workflow for creating production-ready shell scripts with defensive patterns, error handling, and testing.
Use before creative or constructive work (features, architecture, behavior). Transforms vague ideas into validated designs through disciplined reasoning and collaboration.
Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, and anti-detection patterns.
Expert in building browser extensions that solve real problems - Chrome, Firefox, and cross-browser extensions. Covers extension architecture, manifest v3, content scripts, popup UIs, monetization strategies, and Chrome Web Store publishing.
BullMQ expert for Redis-backed job queues, background processing, and reliable async execution in Node.js/TypeScript applications.
Fast, modern JavaScript/TypeScript development with the Bun runtime, inspired by [oven-sh/bun](https://github.com/oven-sh/bun).
This skill ensures all code follows security best practices and identifies potential vulnerabilities. Use when implementing authentication or authorization, handling user input or file uploads, or creating new API endpoints.
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
vstack is a VS Code–native AI engineering workflow system. It provides structured skills — executable by GitHub Copilot in Agent Mode — for planning, reviewing, verifying, and releasing software.
Fetches and references LangGraph Python documentation to build stateful agents, create multi-agent workflows, and implement human-in-the-loop patterns. Use when the user asks about LangGraph, graph agents, state machines, agent orchestration, LangGraph API, or needs LangGraph implementation guidance.
Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.
Web dashboard to orchestrate RedEye autonomous dev sessions across multiple projects. Local-only.
Capture and replay every AI agent tool call, prompt, and response to trace sessions and debug complex workflows with full observability.