self-improvement-loops

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 (prompt, context, workflow, harness code, optimizer code) a recurring failure should be fixed at

Do not activate this skill for adjacent work owned by other skills:

• Governance of a single autonomous loop that does not modify itself: locked and editable surfaces, durable logs, rollback, novelty gates, PR preparation, and human approval boundaries belong to harness-engineering. That skill defines the control surfaces; this skill defines what happens when the surfaces themselves become the optimization target.
• Building the evaluator, regression suite, or quality gates that score candidates: evaluation.
• LLM-as-judge design, pairwise comparison, and bias mitigation for candidate scoring: advanced-evaluation.
• One-shot token efficiency, masking, or caching without an improvement loop: context-optimization.
• Remote sandboxes and background execution infrastructure for running the loop: hosted-agents.
• Whether to build the loop at all, pipeline shape, and cost estimation: project-development.