Self-Improving Agent - Autonomous Learning Patterns

Architectural patterns for AI agents that get better with use. Most agents are stateless -- they repeat mistakes because they cannot learn from their own execution. This skill closes that gap with patterns for feedback capture, memory curation, skill extraction, and regression detection. Key insight: auto-memory captures everything, but curation turns noise into knowledge.

Core Capabilities

• Memory curation — a layered memory stack (CLAUDE.md → MEMORY.md → session), review protocol, and promotion criteria for graduating learnings into enforced rules.
• Feedback loops — outcome classification, signal extraction, and a capture template that turn every task result into a structured learning.
• Regression detection — metrics, thresholds, and a response protocol that flags performance degradation within a few sessions.
• Skill extraction — criteria and a 4-step process to graduate proven patterns into standalone skill packages.
• Meta-learning — adaptive capture strategy and anti-pattern detection so the agent learns what is worth learning.
• Continuous calibration — confidence scoring and belief revision for resolving contradictions across learned knowledge.

When to Use

• Building agents intended to improve over time rather than stay stateless.
• Managing auto-memory (MEMORY.md) and deciding what to keep, promote, or retire.
• Designing self-correcting feedback loops and regression alarms for agent behavior.
• Graduating recurring solutions into reusable skill packages.

Clarify First

Before capturing or promoting learnings, settle the inputs below. When one is missing or vague and would change the result, ask about it; when it wouldn't, use a sensible default and say which one you used.

• Loop stage: remember / extract / promote / review (routes the sub-skill and the whole workflow).
• Source data: which session logs, MEMORY.md, and rules dir to operate on (the subject the tools read and write).
• Promotion bar: min occurrences / confidence threshold for graduating a learning into an enforced rule (--min-occurrences; decides what is kept vs discarded).

Ask about the two or three inputs that matter most for this request, not the whole list. If the user wants a result straight away, produce it and state your assumptions at the top.

Sub-Skills

Compound sub-skill architecture — each file in skills/ handles one step of the improvement loop:

Flow: Remember → Extract → Promote → Review, with Status providing visibility back into the cycle.

Tools

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• references/memory-curation-guide.md — the memory stack, review protocol, promotion criteria/targets, the Weekly Memory Health Check workflow, and the continuous-calibration (confidence scoring + belief revision) machinery. Read when curating MEMORY.md or promoting learnings to rules.
• references/feedback-loop-patterns.md — the core improvement-loop architecture and maturity levels, outcome classification + signal extraction, the capture template, regression metrics/response, the post-session and regression-investigation workflows, common pitfalls, troubleshooting, and the success-criteria bar. Read when designing feedback capture or diagnosing a regression.
• references/meta-learning-architectures.md — skill-extraction criteria and process, the adaptive capture strategy, and anti-pattern detection. Read when the agent should adapt its own learning strategy or extract a proven pattern into a skill.
• references/self-improvement-methodology.md — the five layers of agent learning, the confidence-scoring model, the promotion decision tree, the memory-curation checklist, anti-patterns, and the metrics/thresholds table. Read for the end-to-end methodology overview.

Scope & Limitations

This skill covers:

• Architectural patterns for building agents that learn from execution history and user feedback.
• Memory lifecycle management: capture, curation, promotion, and retirement of learned knowledge.
• Performance regression detection frameworks and response protocols for agent systems.
• Skill extraction methodology for graduating proven patterns into reusable, standalone packages.

This skill does NOT cover:

• Runtime agent orchestration or multi-agent coordination -- see agent-workflow-designer and agent-protocol.
• Prompt engineering, testing, or versioning of the prompts themselves -- see prompt-engineer-toolkit.
• Infrastructure-level observability (logging, tracing, alerting dashboards) -- see observability-designer.
• Initial agent architecture design, tool selection, or capability planning -- see agent-designer.

Integration Points