Individual skill
langfuse-experiment-guardian
Compare Langfuse experiment baseline and candidate results at case level using reviewed dataset, content and evaluator identities. Detect missing cases, evaluator errors and critical regressions before proposing a release. Use when reviewing these cases. Trigger with "Langfuse experiment passed on average", "candidate lost evaluation cases" or "can this experiment replace our reviewed baseline?".
plugins/saas-packs/langfuse-pack/skills/langfuse-experiment-guardian/SKILL.md
Source commit 0500b275e963c027f6b410e8c49c0446945c574e
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- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 12, 2026
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Repository context
- Repository stars (not skill ratings)
- 2,917
- Repository last updated
- Nov 12, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- ai Repository topic
- automation Repository topic
- claude-code Repository topic
- devops Repository topic
- mcp Repository topic
- ai-agents Repository topic
- anthropic Repository topic
- claude-code-plugins Repository topic
- developer-tools Repository topic
- llm Repository topic
- langfuse Declared skill tag
- experiments Declared skill tag
- evaluation Declared skill tag
- regression Declared skill tag
- release-review Declared skill tag
- engineering Derived category
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SKILL.mdcore · 6,216 bytesSKILL.mdcore · 6,216 byteseval-spec.yamlasset · 1,962 bytesreferences/receipt-contract.mddoc · 9,435 bytesscripts/compare.pyruns · 11,507 bytesscripts/project-result.mjsruns · 8,803 bytes
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# Langfuse Experiment Guardian
## Overview
Determine whether a candidate meets a reviewed case-level release policy against
an explicit baseline. An average score cannot hide missing cases, failed tasks,
evaluator omissions or a critical regression. The tool compares supplied
evidence; it does not run an experiment, judge model output or release anything.
## Prerequisites
- The reviewed baseline run identity and review reference; never choose the
newest run automatically.
- Project, dataset/version and required case manifest with content hashes and