القائمة
Context-aware development assistant for AgenticFleet with auto-learning and dual memory (NeonDB + ChromaDB). Handles development workflows with intelligent context management.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
| name | fleet-agent |
| description | Context-aware development assistant for AgenticFleet with auto-learning and dual memory (NeonDB + ChromaDB). Handles development workflows with intelligent context management. |
| focus | development, context-management, pattern-learning, code-analysis |
| triggers | ["add an agent","create a workflow","DSPy signature","test code","memory operations","code analysis","pattern extraction"] |
| capabilities | ["Context-aware block loading (keyword-based)","Dual database search (NeonDB structured + ChromaDB semantic)","Pattern extraction with detailed code examples","Basic code analysis (DSPy signatures, agents, workflows, tools)","Session tracking in NeonDB","Auto-learning enabled"] |
A context-aware development assistant for AgenticFleet that maintains persistent memory across sessions using a hybrid NeonDB + ChromaDB architecture.
Core Memory (.fleet/context/core/): Always loaded
project.md: Architecture, conventions, tech stackhuman.md: User preferences, communication stylepersona.md: Agent guidelines, toneTopic Blocks (.fleet/context/blocks/): Loaded on demand
project/: commands, conventions, gotchas, architectureworkflows/: git, reviewdecisions/: ADRsSkills (ChromaDB + NeonDB): Semantic + structured patterns
/fleet-agent learn --name "add_dspy_agent" --category "agent" --content "Create agent via AgentFactory with DSPyEnhancedAgent wrapper..."
/fleet-agent recall "DSPy typed signatures"
/fleet-agent context "add a new agent for web search"
/fleet-agent analyze src/agents/coordinator.py
/fleet-agent session start
/fleet-agent session status
/fleet-agent session summary "Completed agent creation workflow"
| 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 |
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()
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
memory-system-guide.md: Complete memory system documentation.fleet/context/MEMORY.md: Memory hierarchy and commands