This skill transforms Claude Code agents into continuously learning systems that improve through self-reflection, user corrections, and pattern recognition. Created by Ivan G. Davila, it maintains a structured, tiered memory system on the local filesystem that grows more valuable over time. Instead of repeating the same mistakes or asking the same clarifying questions, the agent builds a persistent knowledge base that compounds with every interaction.

Key Features

Self-Reflection Engine: The agent evaluates its own outputs after completing tasks, identifying areas where it could have performed better and logging improvement patterns
Tiered Memory Architecture: Organizes learned patterns across HOT (frequently used, max 100 lines), WARM (project and domain-scoped, max 200 lines each), and COLD (archived) storage tiers for efficient retrieval
Automatic Pattern Promotion: When a correction or pattern appears 3 or more times within 7 days, it automatically promotes to HOT memory for immediate application
Project-Scoped Learning: Maintains separate learning files per project and domain (code, writing, communication), so patterns from one project do not interfere with another
Correction Logging: Captures explicit user corrections and extracts reusable lessons that prevent the same issue from recurring
Proactive Suggestions: An optional companion feature that offers improvements based on accumulated patterns, with user consent required before any network access

Use Cases

Consistent Code Style: The agent learns project-specific conventions (naming patterns, architecture preferences, testing approaches) and applies them automatically in future sessions
Error Prevention: After being corrected on a mistake -- such as using the wrong API pattern or missing an edge case -- the agent remembers and avoids the same error going forward
Team Knowledge Capture: Corrections and preferences from multiple team members accumulate into a shared knowledge base that benefits everyone using the agent
Domain Specialization: Over weeks of use, the agent develops deep familiarity with specific domains like frontend development, data engineering, or DevOps based on actual interaction patterns

How It Works

The skill initializes a

~/self-improving/

directory structure containing tiered memory files. During and after tasks, the agent writes observations to

corrections.md

, project-specific files under

projects/

, and domain files under

domains/

. Patterns that remain unused for 30+ days are automatically demoted to WARM or COLD storage to keep active memory lean and relevant.

Getting Started

Install the skill from ClawHub by searching for
```
ivangdavila/self-improving
```
Run the included
```
setup.md
```
to initialize the
```
~/self-improving/
```
directory structure
No API keys or credentials are needed -- all state is stored locally on the filesystem
The agent begins learning immediately from corrections and self-reflection during regular use

Self-Improving + Proactive Agent

AI Skill Market Insights

Be Part of the 0+ Developer Community

Key Features

Use Cases

How It Works

Getting Started

License

Quick Start

Manual Installation

TEAR & SHARE

Tags

Agent Evaluation Frameworks

CEO Advisor Agent

Product Manager Agent

Demand Generation Specialist Agent

Content Creator Agent

Channels

Learn

Compare

Company