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skill md SKILL.md 3,976 B

docs SUMMARY.md 194 B

History

Apr 27, 2026 First seen on skills.sh

Sep 9, 2026 First recorded snapshot · 1 installs

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

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

- project.md: Architecture, conventions, tech stack - human.md: User preferences, communication style - persona.md: Agent guidelines, tone

Topic Blocks (.fleet/context/blocks/): Loaded on demand

- project/: commands, conventions, gotchas, architecture - workflows/: git, review - decisions/: ADRs

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:

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: neonmemory.py, chromadriver.py, memory_loader.py

fleet-agent

Installation

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