Skill UI

A comprehensive expert for deploying and scaling AI agents using AWS Bedrock AgentCore. This guide covers the full lifecycle of agent management, including service selection (Gateway, Runtime, Memory, Identity, etc.), deployment patterns, credential management, and advanced integration workflows required for enterprise-scale AI solutions.

This advanced engine acts as the memory layer for C-suite advisory sessions. It loads, manages, and enriches detailed company context from local files, automatically detecting context staleness. Crucially, it enforces strict anonymization protocols before any external API calls, ensuring sensitive data (like revenue or names) is protected while providing deep, specific insights for high-level business strategy.

This comprehensive guide provides advanced troubleshooting workflows for complex, microservices-based environments running on Kubernetes. It covers diagnosing issues like CrashLoopBackOff, cascade failures, resource pressure (CPU/Memory), and detailed Redis state analysis, ensuring systematic root cause identification across multiple services.

A two-layer memory architecture designed for managing corporate board meeting decisions. It separates raw, unfiltered transcripts (Layer 1) from founder-approved, canonical decisions and action items (Layer 2). This system ensures historical accuracy, tracks overdue tasks via commands like /cs:review, and actively detects topic contradictions and conflicts to maintain governance integrity.

A specialized review tool for identifying critical performance bottlenecks within Django applications. It focuses on validating issues such as N+1 queries, unbounded querysets, missing database indexes, and write loops. Use this when you suspect database slowdowns or memory exhaustion due to inefficient ORM usage, ensuring findings are provable and actionable.

This pattern addresses the fundamental limitation of context window size by treating the filesystem as a persistent, external memory. Agents can write large outputs, plans, or findings to files, offloading them from the active context. Instead of carrying all information, agents use targeted search and retrieval tools to load only the necessary context on demand, drastically reducing token bloat and improving performance over long, complex tasks.

This skill fetches unread GitHub Pull Requests (PRs) that require review from a specified team or were opened by a team member. It is ideal for users who need to check their team's review queue, find PRs needing attention, or automate the process of reviewing open code submissions without manually browsing GitHub notifications. Requires GitHub CLI authentication.

Diagnose and fix Odoo performance bottlenecks by tuning PostgreSQL queries, adjusting worker/memory limits, and applying profiling insights to achieve faster page loads and stable production behavior.

A comprehensive guide and methodology for identifying, measuring, and fixing performance bottlenecks across the entire stack. This skill covers optimizing database queries (e.g., N+1 problems, missing indexes), improving API efficiency (caching, parallel processing), and enhancing frontend performance (memoization, code splitting, image optimization). It emphasizes the critical 'Measure First' principle to ensure all optimizations are data-driven and effective.

This skill formalizes successful, recurring patterns discovered in auto-memory (MEMORY.md) and graduates them into permanent, enforced rules within the project's core instruction set (CLAUDE.md or scoped rules). It ensures that learned best practices become mandatory project conventions, significantly improving adherence and consistency across all future AI interactions and development cycles. Use this when a temporary note proves to be a vital, repeatable guideline.

This tool allows users to explicitly save critical, project-specific facts, conventions, or debugging insights to a dedicated auto-memory file. Use it when the knowledge is too important or specialized to rely on the AI's automatic context capture. It ensures persistent recall of architectural decisions, tool gotchas, or team preferences across multiple sessions and projects.

This agent enhances AI's auto-memory by analyzing raw learned patterns (from MEMORY.md). It allows users to promote proven knowledge into structured project rules (CLAUDE.md) or extract them into reusable skills. Use it to audit memory health, enforce best practices, and graduate temporary notes into permanent, actionable standards.