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
Relay: A Tool for Handing Off Claude Code Sessions to Other AI Agents
Relay is a Rust binary that extracts Claude Code's session context—including conversation history, tool calls, errors, and git state—and transfers it to other AI agents like Codex or Gemini when rate limits are hit. It supports 8 agents and can be installed via GitHub or npm.
Maggy: An Autonomous Engineering Platform on Claude Code with Cross-Session Memory and P2P Team Learning
Maggy sits at Level 4 of the AI coding tool spectrum: multi-model orchestration, cross-session memory, process intelligence from CI/reviews, and P2P team learning. Benchmarks show 83% reduction in Claude usage while catching 7 security issues missed by single-pipeline Claude Code.
AVP Protocol Enables LLM Agents to Share KV-Cache Instead of Text for Token Efficiency
AVP (Agent Vector Protocol) allows LLM agents to pass KV-cache directly between them instead of text, reducing token processing by 73-78% and achieving 2-4x speedups across Qwen, Llama, and DeepSeek models. The protocol works with HuggingFace and vLLM connectors and is available as a Python package.
Multi-Agent Career Mentor Built with Ollama and MCP for Local AI
A developer built a 5-agent AI system that analyzes resumes and generates career intelligence reports using Ollama with llama3 locally. The system chains agent outputs so each builds on previous context, with MCP handling tool integration.