self-improvement-loopslisted

# Self-Improvement Loops This skill covers systems where the harness is the artifact being optimized: an agent mines its own failures and edits its own scaffold, a meta-agent searches over harness code, a population of workflow candidates evolves against an evaluator, or the mechanism that produces context is itself versioned and improved. The design question shifts from "how do I control one loop" (harness-engineering) to "how do I let a loop rewrite parts of itself without corrupting the signal that steers it". The controlling constraint across every published system: the loop optimizes whatever signal it is given, including the signal's own weaknesses. Design the loop assuming the optimizer will find every gap between the metric and the intent. ## When to Activate Activate this skill when: - Building a loop where an agent proposes edits to its own harness, prompts, context playbook, or workflow based on mined failure patterns - Designing meta-level search over harness or scaffold code: meta-agent search, tree search over workflow graphs, evolutionary program search with an LLM mutation operator - Choosing acceptance criteria for any self-modifying agent system - Evolving the mechanism that manages context (a skill, playbook, or context function) rather than hand-editing the context artifact - Diagnosing a degenerating self-improvement loop: reward hacking, diversity collapse, context collapse, or silent stagnation - Deciding which level of the optimization ladder (pro