Analyze runtime metric logs and select evolution plans with adaptive thresholds.
npx skills add https://github.com/isLinXu/under-one --skill xiushen-lu
Please help me install this Agent Skill. Skill: xiushen-lu Source: https://github.com/isLinXu/under-one/tree/main/underone/skills/xiushen-lu Command: npx skills add https://github.com/isLinXu/under-one --skill xiushen-lu
This Skill solves the problem of agent skill degradation over time by analyzing runtime performance metrics and proposing controlled self-improvements without breaking existing behavior.
Run the core evolution engine against your skills directory to generate an evolution report and update adaptive thresholds only when explicitly applied.
High-intent search queries and answers about installing and using this skill.
To stop LLM agent skills from degrading over time, you need runtime metric analysis to detect drift and apply controlled self-improvements. This Skill evaluates execution logs to propose parameter-level tuning and refactoring while maintaining existing behavior.
Adaptive threshold tuning works by detecting stability, error-proneness, volatility, and autonomy pressure from runtime metrics, then dynamically adjusting decision boundaries with guardrails. This ensures parameter-level tuning only triggers when validated conditions are met.
Safe rollback during skill evolution requires creating backups before applying parameter-level tuning or refactoring to scripts, validating outcomes post-change, and automatically reverting to the backup if validation fails. This prevents broken behavior from persisting.
Yes, you can share validated threshold tuning improvements across multiple agent skills using a cross-skill knowledge hub. This allows threshold migration so learned stability improvements propagate safely to other skills experiencing similar runtime drift.
You need deterministic tuning for production-style skill maintenance when repeated skill execution causes quality degradation, increased errors, or higher human intervention rates. It applies a validated core engine to ensure code evolution remains backed and reversible.
To detect bottlenecks causing error-proneness in agent skill scripts, the system classifies runtime metric logs against adaptive thresholds to identify volatility and autonomy pressure. This bottleneck classification directs targeted parameter-level tuning and refactoring.
LaurentPRAT-DBAnalyze Databricks metrics and logs to tune Spark configuration and caching.
rongaredeAutomate Makepad skill improvement through self-evolution, correction, and validation.
Izak90Audit team charters and extract recurring patterns into reusable skills.
christopherlouetDetect code smells and generate refactoring suggestions for JavaScript files.
