agentsop-regression-gate
CommunityBlock LLM regressions with CI-eval gates.
Authoragentsope
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill prevents LLM prompt, model, and retriever changes from silently shipping behavior regressions by enforcing a held-out evaluation gate in CI.
Core Features & Use Cases
- Held-out eval gate: builds (then freezes) a golden eval set, runs it on every change, and blocks merges on score drops.
- Metric + threshold enforcement: consumes a calibrated metric and applies an absolute floor and/or relative no-regression delta above measured noise.
- Cross-framework CI wiring: assembles the discipline across common eval stacks (e.g., LlamaIndex, DSPy, promptfoo, LangSmith) so teams can implement a consistent regression gate.
Quick Start
Activate regression-gate for every prompt/model/retriever change by asking the AI to design your eval set, select a calibrated metric, set a noise-aware threshold, and produce a CI wiring plan that fails the build on regressions.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: agentsop-regression-gate Download link: https://github.com/agentsope/SkillAlchemy/archive/main.zip#agentsop-regression-gate Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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