drift-detection
✓Statistical and quality drift detection for LLM applications. Use when monitoring model quality degradation, input distribution shifts, or output pattern changes over time.
Installation
SKILL.md
Monitor LLM quality degradation and input/output distribution shifts in production.
| Statistical method | PSI for production (stable), KS for small samples | | Threshold strategy | Dynamic (95th percentile of historical) over static | | Baseline window | 7-30 days rolling window | | Alert priority | Performance metrics > distribution metrics | | Tool stack | Langfuse (traces) + Evidently/Phoenix (drift analysis) |
| < 0.1 | No significant drift | Monitor | | 0.1 - 0.25 | Moderate drift | Investigate | | >= 0.25 | Significant drift | Alert + Action |
Statistical and quality drift detection for LLM applications. Use when monitoring model quality degradation, input distribution shifts, or output pattern changes over time. Source: yonatangross/orchestkit.
Facts (cite-ready)
Stable fields and commands for AI/search citations.
- Install command
npx skills add https://github.com/yonatangross/orchestkit --skill drift-detection- Source
- yonatangross/orchestkit
- Category
- </>Dev Tools
- Verified
- ✓
- First Seen
- 2026-02-03
- Updated
- 2026-02-18
Quick answers
What is drift-detection?
Statistical and quality drift detection for LLM applications. Use when monitoring model quality degradation, input distribution shifts, or output pattern changes over time. Source: yonatangross/orchestkit.
How do I install drift-detection?
Open your terminal or command line tool (Terminal, iTerm, Windows Terminal, etc.) Copy and run this command: npx skills add https://github.com/yonatangross/orchestkit --skill drift-detection Once installed, the skill will be automatically configured in your AI coding environment and ready to use in Claude Code or Cursor
Where is the source repository?
https://github.com/yonatangross/orchestkit
Details
- Category
- </>Dev Tools
- Source
- skills.sh
- First Seen
- 2026-02-03