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skills/meta/swarm-optimization-skill.md

name: "Swarm Architecture & Prompt Engineering" description: "Principles for rewriting Agent Personas and Skill files to improve coordination and logic."

Core Philosophy: "Code is Cheap, Context is King"

To fix a behavioral bug, you rarely need to change the Python code first. You usually need to change the Agent's Instructions.

Debugging Agent Failure Modes

1. Hallucination: The agent tries to use a tool it doesn't have.
   • Fix: Edit the tools list in rules/X.md OR restrict the instruction to available tools.
2. Looping/Indecision: The agent keeps asking for clarification.
   • Fix: Add a "Default Action" protocol. (e.g., "If unsure, Default to Safe Mode").
3. Lazy Execution: The agent gives a summary instead of the code.
   • Fix: Add a "Constraint": "You must output the full file content. No placeholders."

Role Optimization Strategies

• The "Separation of Concerns" Patch:
  • If an agent is overwhelmed, split their duties.
  • Example: If @Quant is failing to log trades because it's too busy calculating signals, move the Logging duty to @MLEng.
• The "Explicit Handoff" Patch:
  • If agents are ignoring each other, hardcode the communication.
  • Example: Update @Frontend: "You strictly listen to ws://localhost:8000. You do not calculate your own indicators."

Prompt Refinement Patterns

When rewriting System Instructions in agent files:

• Use Negatives Constraints: "Do NOT use time.sleep()."
• Use Chain-of-Thought Triggers: "Before answering, list 3 potential risks."
• Use Artifact Mandates: "Your output must always be a JSON block."