self-improvement

name	self-improvement
description	Evolutionary mechanisms for the agent to rewrite its own prompts (Neuroplasticity).
context_cost	very_high
tools	["replace_file_content","task_boundary"]

Self-Improvement Skill

"The software that writes itself."

1. Evolutionary Prompts (The Genome)

Treat the system_prompt and skill.md files as DNA.

• Genes: Individual instructions (e.g., "Always use TDD").
• Phenotype: The agent's actual behavior in a session.
• Fitness Function: Did the user accept the code? Did the tests pass?

2. The Mutation Cycle

When the agent encounters a novel failure or success:

1. Selection: Identify the "Gene" (Prompt Instruction) responsible.
2. Mutation: Rewrite the instruction.
   • Example: Change "Write clean code" -> "Write clean code adhering to AIRBNB style guiding."
3. Crossover: Combine two successful skills into a new hybrid skill.

3. Recursive Self-Editing

The agent has permission to edit its own skill files.

Protocol:

1. Trigger: "I keep failing to import this library correctly."
2. Analysis: "My knowledge base is outdated."
3. Action: calling replace_file_content on my-language.skill.md.
4. Commit: "Updated skill memory with correct import syntax."

4. Safety Guardrails (Homeostasis)

To prevent "Cancer" (Runaway bad mutations):

• Version Control: All skill edits must be git-committed.
• Revert: If success_rate drops after a mutation, auto-revert.
• Core Immutable: The "Prime Directives" (Safety, Obedience) cannot be mutated.

5. Implementation

def optimize_prompt(task_history):
    # 1. Analyze Failure
    failure_pattern = find_pattern(task_history, status="failed")
    
    # 2. Propose Mutation
    new_instruction = llm.generate_fix(failure_pattern)
    
    # 3. Simulate (Mental Sandbox)
    predicted_success = llm.simulate(new_instruction)
    
    # 4. Integrate
    if predicted_success > threshold:
        update_skill_file(new_instruction)