Agent skill

fleet-agent

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

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/development/fleet-agent-qredence-agentic-fleet

SKILL.md

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

Command Description
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 start Start new session
session status Show current session
session summary <text> Save session summary
stats Show development metrics

Auto-Learning

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

yaml
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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