Menú
Complete guide to the AgenticFleet memory system. Read this first.
Instalar con Codex o Claude Copia este prompt, pégalo en Codex, Claude u otro asistente, y deja que revise la página de la skill y la instale por ti.
Basado en la clasificación ocupacional SOC
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.
| name | memory-system |
| description | Complete guide to the AgenticFleet memory system. Read this first. |
A two-tier memory architecture enabling agents to learn, remember, and improve over time.
Initialize (first time only):
uv run python .fleet/context/scripts/memory_manager.py init
Setup Chroma Cloud (after editing config with your API key):
uv run python .fleet/context/scripts/memory_manager.py setup-chroma
Verify Status:
uv run python .fleet/context/scripts/memory_manager.py status
Read Core Context (always do this first):
.fleet/context/core/project.md - Project architecture.fleet/context/core/human.md - User preferences.fleet/context/core/persona.md - Agent guidelinesSearch Memory when you need information:
uv run python .fleet/context/scripts/memory_manager.py recall "your query"
Create Skills after solving problems:
uv run python .fleet/context/scripts/memory_manager.py learn --file .fleet/context/skills/new-skill.md
Location: .fleet/context/core/
| Block | Purpose |
|---|---|
project.md | Architecture, tech stack, conventions |
human.md | User preferences, communication style |
persona.md | Agent role, tone, guidelines |
Location: .fleet/context/blocks/
| Category | Blocks |
|---|---|
project/ | commands, architecture, conventions, gotchas |
workflows/ | git, review |
decisions/ | ADR-style decision records |
Location: .fleet/context/skills/
Learned patterns and solutions. Indexed to Chroma for semantic search.
Collections: semantic, procedural, episodic
Enables fuzzy search across all indexed content.
/init # Initialize memory system
/learn # Learn a new skill
/recall # Search memory semantically
/reflect # Reflect on session, consolidate learnings
# Initialize system (creates local files)
uv run python .fleet/context/scripts/memory_manager.py init
# Setup Chroma Cloud collections
uv run python .fleet/context/scripts/memory_manager.py setup-chroma
# Check connection and collection status
uv run python .fleet/context/scripts/memory_manager.py status
# Semantic search across all collections
uv run python .fleet/context/scripts/memory_manager.py recall "query"
# Index skill to Chroma procedural collection
uv run python .fleet/context/scripts/memory_manager.py learn --file <path>
# Archive session to episodic collection
uv run python .fleet/context/scripts/memory_manager.py reflect
All memory blocks use Letta-style frontmatter:
---
label: block-name
description: What this block contains and when to use it.
limit: 5000 # Character limit
scope: core|project|workflows|decisions
updated: 2024-12-29
---
# Content here...
/recall to search for relevant skillshuman.md if you learn user preferences/reflect to consolidate learnings/learn.fleet/context/
├── SKILL.md # This file (entry point)
├── MEMORY.md # Detailed documentation
├── core/ # Core memory blocks
├── blocks/ # Topic-scoped blocks
│ ├── project/
│ ├── workflows/
│ └── decisions/
├── skills/ # Learned skills
├── system/ # Agent skill definitions
├── scripts/ # Python memory engine
└── .chroma/ # Chroma Cloud config
MEMORY.md - Detailed setup and architectureskills/README.md - How to create skillsskills/SKILL_TEMPLATE.md - Skill templateblocks/decisions/001-memory-system.md - Architecture decision record