Execute→Track→Evaluate→Optimize→Verify→Deploy, six phases auto-flowing in a complete self-evolution loop.

Each phase has clear inputs and outputs, with the previous phase's output automatically feeding the next, without manual intervention.

1)    Execute phase: AI agent executes user tasks, recording the complete execution process and results.

2)    Track phase: records key metrics during execution, including response time, accuracy, user satisfaction.

3)    Evaluate phase: multi-dimensional quantitative evaluation of execution effectiveness, comparing with historical versions and baselines.

4)    Optimize phase: auto-adjusts parameters and strategies based on evaluation results, generating optimized new versions.

5)    Verify phase: A/B testing comparing new and old versions to ensure the new version is actually better.

6)    Deploy phase: auto-deploys the verified new version, retaining old version for rollback.

Nightly optimization pipeline runs during low-traffic periods without affecting daytime operations.

The system collects the day's execution data at night, runs the optimization pipeline, generates and verifies improved versions.

1)    Auto data collection: automatically collects all AI call execution and effectiveness data from the day.

2)    Optimization pipeline: auto-runs evaluate, optimize, verify pipeline to generate improved versions.

3)    A/B verification: new and old versions compared in shadow environment to ensure improvement.

4)    Auto deployment: deploys verified new version, auto-rolls back to previous stable version on failure.

5)    Optimization reports: each optimization generates detailed reports of improvements and effectiveness gains.

Multi-dimensional quantitative evaluation, deviation analysis, success pattern recognition, anti-pattern identification.

Not simple accuracy statistics, but multi-dimensional quantification of AI execution effectiveness.

1)    Multi-dimensional quantification: evaluates accuracy, response time, token consumption, user satisfaction.

2)    Deviation analysis: analyzes deviations between AI output and expected output, identifying systematic biases.

3)    Success pattern recognition: extracts common patterns from successful executions, reinforcing effective strategies.

4)    Anti-pattern identification: extracts common patterns from failed executions, avoiding ineffective strategies.

5)    Trend analysis: tracks effectiveness trends over time, evaluating optimization direction.

External skill absorption, skill package sharing and reuse, skill health dashboard.

AI skills are not closed. The system scans external skill sources via ecosystem radar, auto-adapting and absorbing.

1)    Ecosystem radar: auto-scans external MCP servers and skill marketplaces for available skills.

2)    Skill format adapters: supports ECC, DeerFlow, SKILL.md and other skill formats with auto-adaptation.

3)    Skill package publishing and installation: skills can be packaged and published, others can install and reuse.

4)    Skill health dashboard: monitors call count, success rate, average effectiveness for all skills.

5)    Skill versioning: skills have version numbers, with rollback to any historical version.

Monitors all skills' running status, quantifies skill quality, identifies skills needing optimization.

Comprehensively evaluates each skill's health through call count, success rate, average effectiveness.

1)    Call count: statistics on each skill's call count, identifying high and low frequency skills.

2)    Success rate: statistics on each skill's execution success rate, identifying unstable skills.

3)    Average effectiveness: quantifies each skill's average execution effectiveness, identifying inefficient skills.

4)    Skill ranking: ranks skills by comprehensive metrics, focusing optimization priorities.

5)    Alert mechanism: auto-alerts when skill success rate falls below threshold or effectiveness drops.