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, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• 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)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Sub-Skills

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

Sub-Skill	File	Purpose
Remember	skills/remember.md	Capture errors and learnings from current session
Extract	skills/extract.md	Extract reusable patterns from completed work
Promote	skills/promote.md	Graduate proven patterns to permanent rules
Review	skills/review.md	Audit memory health, prune stale entries
Status	skills/status.md	Dashboard showing memory state and learning progress

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

Tools

Tool	Purpose	Command
pattern_extractor.py	Extract reusable patterns from session logs	python scripts/pattern_extractor.py --input sessions.jsonl --min-occurrences 3
memory_health_checker.py	Audit memory for line counts, stale, and promotable entries	python scripts/memory_health_checker.py --memory ./MEMORY.md --rules ./.claude/rules/
rule_promoter.py	Validate and apply promotions from memory to rules	python scripts/rule_promoter.py --memory ./MEMORY.md --list-candidates
feedback_analyzer.py	Analyze feedback logs for success rates and opportunities	python scripts/feedback_analyzer.py analyze
regression_detector.py	Compare baseline vs current performance metrics	python scripts/regression_detector.py compare
rule_manager.py	Manage a learned rules knowledge base with CRUD	python scripts/rule_manager.py list

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

Skill	Integration	Data Flow
context-engine	Controls what the agent sees per session; this skill decides what is worth remembering long-term	Promoted rules and curated memory feed context retrieval; context relevance metrics flow back for regression tracking
agent-designer	Defines the agent's architecture and capabilities; this skill layers learning infrastructure on top	Architecture constraints inform possible feedback loops; extracted skills feed back as new capabilities
prompt-engineer-toolkit	Prompts degrade as codebases evolve; this skill detects prompt regression via outcome tracking	Performance metrics flag underperforming prompts; prompt updates feed back as CLAUDE.md rule changes
observability-designer	Provides system-level metrics; this skill provides agent-behavior-level metrics	System telemetry enriches regression diagnosis; agent metrics export to observability dashboards
tech-debt-tracker	Stale rules and bloated memory are technical debt this can surface alongside code debt	Memory health metrics feed debt scoring; debt prioritization informs which stale rules to retire
agent-workflow-designer	Multi-step workflows benefit from per-step feedback capture and cross-workflow pattern extraction	Per-step outcome data flows into feedback loops; extracted optimizations update workflow definitions