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Fleet Agent

fleet-agent

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

SKILL.md

Full skill instructions

Fleet Agent

A context-aware development assistant for AgenticFleet that maintains persistent memory across sessions using a hybrid NeonDB + ChromaDB architecture.

Memory Architecture

Dual Storage

  • ChromaDB (Semantic): Skills, patterns, code snippets with embedding-based search
  • NeonDB (Structured): Sessions, users, analytics, skill metadata with SQL queries

Context Layers

  1. Core Memory (.fleet/​context/​core/): Always loaded

    • project.md: Architecture, conventions, tech stack
    • human.md: User preferences, communication style
    • persona.md: Agent guidelines, tone
  2. Topic Blocks (.fleet/​context/​blocks/): Loaded on demand

    • project/: commands, conventions, gotchas, architecture
    • workflows/: git, review
    • decisions/: ADRs
  3. Skills (ChromaDB + NeonDB): Semantic + structured patterns

Usage Examples

Learn a Pattern

/​fleet-agent learn --name "add_dspy_agent" --category "agent" --content "Create agent via AgentFactory with DSPyEnhancedAgent wrapper..."

Recall Information

/​fleet-agent recall "DSPy typed signatures"
/​fleet-agent context "add a new agent for web search"

Analyze Code

/​fleet-agent analyze src/​agents/​coordinator.py

Session Management

/​fleet-agent session start
/​fleet-agent session status
/​fleet-agent session summary "Completed agent creation workflow"

Commands

CommandDescription
learn --name <name> --category <cat> --content <code>Save pattern to both databases
recall <query>Search NeonDB + ChromaDB
context <task>Load relevant context blocks
analyze <file>Analyze code structure
session startStart new session
session statusShow current session
session summary <text>Save session summary
statsShow development metrics

Auto-Learning

Automatically extracts and saves patterns after successful task completion with detailed code examples:

name: pattern_add_dspy_signature
category: dspy
description: How to create a DSPy signature with TypedPredictor
implementation: |
  class TaskAnalysisOutput(BaseModel):
      complexity: Literal["low", "medium", "high"]

  class TaskAnalysis(dspy.Signature):
      task: str = dspy.InputField(desc="Task to analyze")
      analysis: TaskAnalysisOutput = dspy.OutputField()

Implementation

Main script: .fleet/​context/​scripts/​fleet_agent.py

Invocation: uv run python .fleet/​context/​scripts/​fleet_agent.py <command>

Dependencies: neon_memory.py, chroma_driver.py, memory_loader.py

See Also

  • memory-system-guide.md: Complete memory system documentation
  • .fleet/​context/​MEMORY.md: Memory hierarchy and commands

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