Self-Iterating Agent Skill

The Limitation of Traditional Optimization

A core bottleneck in developing long-term interactive AI Agents is that optimizing their behavioral rules and underlying code logic relies heavily on manual developer intervention. Whenever an Agent demonstrates flawed logic, redundancy, or failure to adapt to new scenarios during conversations, human developers must manually review, rewrite, or modify rules and redeploy code. This cycle is slow, costly, and fails to consistently translate every interaction into incremental capability improvements for the Agent. The Self-Iteration skill is built to address this "optimization gap" by creating an automated, closed-loop evolutionary pipeline.

How the Skill Implements Self-Iteration

This skill employs a four-module closed-loop system that automates the entire "observe-analyze-optimize-feedback" cycle.

Core Capabilities and Workflow

1. Rule Iteration (Rule Generation): This is the foundation of iteration. The system automatically reads the most recent 15 turns of conversation history, analyzes its strengths and issues, and generates 3-6 new behavioral rules. These new rules are automatically saved to the Agent's long-term memory and injected into the system prompt via the beforeChat hook, taking effect immediately in subsequent conversations. You can use the 查看迭代规则 (Check Iteration Rules) command to review currently active rules and their versions.
2. Automatic Code Modification (Code Optimization): During each iteration, the skill reads its own Skill code, identifies areas for optimization (such as syntax errors or redundant logic), and generates replacement code snippets ready for direct substitution. A key security mechanism: the original code is automatically backed up on first run, and the system can automatically restore this backup if an optimization fails, preventing the skill from crashing due to code errors. Optimizations aim to enhance stability without altering core functionality.
3. Closed-Loop Auto-Iteration (Automation): Once enabled via the 持续迭代 (Continuous Iteration) command, the system runs as a background daemon. It checks the conversation turn count every 3 seconds. Automatically, every 20 turns of dialogue, it triggers a complete iteration cycle (review → scoring → optimization → implementation). This allows the Agent to evolve continuously without manual oversight.
4. Iteration Scoring System (Quality Assurance): After each iteration, the AI self-scores on a scale of 1-10 based on "rule rationality" and "practicality of code optimization." If the score is below 6, the system automatically triggers a second optimization, forming a quality assurance loop. You can use 查看迭代评分 (Check Iteration Scores) to trace the entire history and effectiveness of the evolution at any time.

Usage Boundaries and Considerations

• Iteration Trigger Relies on Conversation History: Rule generation requires at least 15 turns of conversation as analysis material. Therefore, this skill is more suitable for long-term, multi-turn interaction scenarios rather than short, one-off tasks.
• Conservative Code Modifications: Automatic code optimization is designed to fix errors and simplify logic; it does not undertake major architectural refactoring. This means it may not solve problems stemming from high-level design flaws.
• Subjectivity of Scoring: The iteration score is determined by the AI itself. While the low-score automatic optimization mechanism exists, the evaluation criteria may still be influenced by the model's own subjective biases.
• Resource Usage and Stability: The continuous iteration mode runs in the background and consumes certain computational resources. It is advisable to use the 停止迭代 (Stop Iteration) command appropriately in environments with strict resource constraints. While robust error-handling and backup mechanisms ensure stability, extreme cases of code modification failure can still occur.