Agent skill · product strategy · borghei
self-improving-agent
Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.
Why this skill is useful
Adds executable scripts for memory curation and feedback loops that enhance agent learning capabilities.
What it needs
Requires python installed locally. About 4k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/self-improving-agent