Self-Evolving Skill pattern validation: 5-round experiment results

Experiment setup and results
A developer conducted a 5-round experiment to validate the Self-Evolving Skill design pattern for Claude Code, which was previously shared. The experiment used a MySQL database with 29 tables and 590MB of data from a smart building management system.
The rounds followed this progression: structure exploration → data queries → rule discovery → complex investigation → repeat verification.
Key findings
- Five-Gate rejection rate: 63.6% — most interactions produced no knowledge change
- Incremental convergence: +75 → +46 → +12 → +21 → +1
- Gate 2 self-correction: The pattern caught and fixed 2 erroneous rules that the Skill had written in earlier rounds
- Round 5: Zero exploration steps, direct template reuse
- Accuracy: 100% — no incorrect knowledge survived the process
An unexpected finding was that tool usage pitfalls were captured as a high-value byproduct — issues the developer didn't design for but the Five Gates caught anyway.
The developer has a second experiment in progress on a larger telecom billing database. Full data with per-round diffable snapshots is available on GitHub.
📖 Read the full source: r/ClaudeAI
👀 See Also

Membase: External Memory Layer for AI Assistants Across Tools
Membase is an external memory layer that extracts and stores conversation context in a knowledge graph, then injects relevant memories into new chats across Claude, ChatGPT, Cursor, Gemini, and other AI tools. It's currently in private beta with all features free.

Fino: Open-Source MCP Server for Personal Finance Analysis with Claude
Fino is a free, open-source MCP server that connects Claude to bank accounts through Plaid, stores transaction data locally in SQLite, and provides Claude with tools for financial analysis.

ClaudeOrb: Chrome Extension Monitors Claude API Usage in Real-Time
A developer built ClaudeOrb, a free Chrome extension that displays Claude session percentage, weekly limits, countdown timers, Claude Code costs, and 7-day spending trends. The tool was created using Claude Code after hitting rate limits without warning.

Rival-Review: A Cross-Model Review Loop for AI Agent Plans
Rival-review is an MIT-licensed tool that uses a second AI model to audit plans from a primary AI coding agent before execution, catching issues like flawed rollback plans, security holes, and stale-state decisions.