agentic-fleet

Complete guide to the AgenticFleet memory system. Read this first.

Comprehensive guide to integrating DSPy with Microsoft Agent Framework in AgenticFleet, covering typed signatures, assertions, routing cache, GEPA optimization, and agent handoffs.

Quick reference card for DSPy + Agent Framework integration patterns: typed signatures, assertions, routing cache, and agent handoffs.

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

Comprehensive guide for initializing or reorganizing agent memory and project context. Use when setting up a new project, when the user asks you to learn about the codebase, or when you need to create effective memory blocks for project conventions, preferences, and workflows.

Expert Python backend code reviewer that identifies over-complexity, duplicates, bad optimizations, and violations of best practices. Use when asked to review Python code quality, check for duplicate code, analyze module complexity, optimize backend code, identify anti-patterns, or ensure adherence to best practices. Ideal for preventing AI-generated code from creating unnecessary files instead of imports, finding repeated validation logic, and catching over-engineered solutions.

Context-aware development assistant for AgenticFleet with auto-learning and dual memory (NeonDB + ChromaDB). Handles development workflows with intelligent context management.

Initialize or hydrate the agent's memory system and verify configuration.

Ingest new procedural memory (skills, patterns, docs) into the vector database.

Semantic search for memory. Use to find solutions, patterns, or context from Chroma Cloud.

Analyze the current session and consolidate learnings. Use at the end of a session or task.

Expert Python backend code reviewer that identifies over-complexity, duplicates, bad optimizations, and violations of best practices. Use when asked to review Python code quality, check for duplicate code, analyze module complexity, optimize backend code, identify anti-patterns, or ensure adherence to best practices. Ideal for preventing AI-generated code from creating unnecessary files instead of imports, finding repeated validation logic, and catching over-engineered solutions.