Search Skills

Manages AWS authentication via Atmos, covering ATMOS_PROFILE setup, SSO login, and automatic identity assumption per stack to resolve permission issues.

Handles cross-component dependency wiring in stack YAML using Terraform state syntax, avoiding remote-state lookups.

Guides creation and modification of Terraform/OpenTofu components with file structure, configuration, and naming conventions guidance.

Debugs Atmos configuration, deployment, and stack resolution issues through logging, error interpretation, and component analysis.

Provides CSS styles, color themes, and visual conventions for Cloud Posse documentation site, including component and mermaid diagram styling.

Automates the build, rendering, and deployment of technical documentation for library projects using Docusaurus.

Provides tools and guidance for deploying and configuring infrastructure components using Atmos stacks, including YAML configuration, inheritance, and component wiring.

Essential guide to the AgenticFleet memory system architecture and usage, serving as the foundational reference for implementation and understanding.

Reviews Python backend code for complexity, duplicates, anti-patterns, and best practice violations to improve code quality.

Analyzes session content to consolidate key learnings, supporting continuous improvement and knowledge retention.

Ingests procedural knowledge (skills, patterns, documents) into vector databases for AI-driven retrieval and processing.

Context-aware development assistant for AgenticFleet, managing workflows with intelligent context via dual memory (NeonDB and ChromaDB).

Automates creation of customer-friendly release notes from git commit history, transforming technical details into clear, user-facing changelogs.

Initializes and verifies configuration of an AI agent's memory system, ensuring proper setup for subsequent operations.

Provides a guide for integrating DSPy with Microsoft Agent Framework in AgenticFleet, covering typed signatures, assertions, and agent handoffs.

Provides structured initialization for AI agent memory and project context, enabling effective codebase learning and workflow configuration.

Enables semantic search over stored memory contexts using Chroma Cloud to retrieve solutions, patterns, and context for AI-driven applications.

Quick reference for DSPy agent framework integration patterns including typed signatures, assertions, routing cache, and agent handoffs.

Promotes clean, self-explanatory code structures to improve maintainability, review efficiency, and long-term development quality.

Provides automated testing and systematic debugging capabilities to ensure software correctness and reliability.

Enables end-to-end development of web applications by integrating user interfaces with server logic and database systems.

Guides developers in implementing secure coding practices to prevent vulnerabilities and safeguard user data throughout the development lifecycle.

Enables developers to build data-driven features and integrate AI/ML capabilities through data analytics and machine learning expertise.

Creates clear technical documentation and code comments to convey code behavior and context, enabling effective team knowledge sharing.