learning-adaptation

name	learning-adaptation
description	Mechanisms for In-Context Reinforcement Learning, Meta-Learning, and Neuroplasticity.
context_cost	high
tools	["replace_file_content","write_to_file"]

Learning & Adaptation Skill

"Neurons that fire together, wire together." (Hebbian Learning)

1. Synaptic Plasticity (Rewiring)

This skill allows the system to "rewire" itself based on experience.

• Potentiation (Strengthening): If a prompt/tool works well, save it to a "Best Practices" bank.
• Depression (Weakening): If a tool fails often, add a warning or deprecate it.

2. Meta-Learning (Learning to Learn)

The agent should not just learn information; it should learn strategies.

Strategy Reflection Loop

After a task is complete, perform a "Post-Mortem":

1. Observation: "I hallucinated a library name."
2. Hypothesis: "I didn't check the docs first."
3. New Rule: ("ALWAYS check docs for library imports.")
4. Storage: Save to system_rules.md.

3. Reinforcement Learning (RL) Integration

• Actor: The Agent performing the task.
• Critic: A separate module (or human) that scores the outcome (Reward).
• Policy Update: Update the Few-Shot Context.
  • Old: Zero-shot prompt.
  • New: Prompt + 3 Successful Examples from learning-adaptation bank.

4. Episodic Consolidation (Dreaming)

Biological brains consolidate short-term memories into long-term structures during sleep.

Implementation: The "Nightly Build"

1. Compress: Run a summarization job on the day's logs.
2. Extract: Extract key facts and successful code patterns.
3. Consolidate: Update the Knowledge Graph and clear the raw logs.

References

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction.
• Hebb, D. O. (1949). The Organization of Behavior.