lvtd-llc/skills

experiment-verification-monitoring

Verify and monitor running experiments for operational quality. Use when designing prelaunch QA, spot-check tooling, experiment canaries, A/A tests, leakage checks, interference monitoring, active experiment dashboards, alerts, or an experimentation quality roadmap.

First seen Jul 12, 2026

Installation

$ npx skills add lvtd-llc/skills --skill experiment-verification-monitoring

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Declared
Cursor Not declared
Codex Declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 1
License LICENSE
Default branch main
Open issues 0
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Version0.1.0
LicenseMIT
CompatibilityCodex, Claude Code, and other Agent Skills-compatible clients.
Declared agents claude-code codex
More metadata
version
0.1.0
displayName
Experiment Verification Monitoring
category
Product Management
tags
practical-ab-testing,next-level-ab-testing,ab-testing,experimentation,quality-assurance

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,082 B
  • docs SUMMARY.md 308 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Experiment Verification Monitoring

Use this skill to prevent misconfigured or unhealthy experiments from producing bad evidence. It focuses on verification before launch, canaries, A/A tests, leakage and interference checks, active monitoring, and quality roadmap metrics.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 5 on experiment effectiveness, prelaunch verification, QA tooling, canaries, A/A tests, spillover effects, and active monitoring.

Related skills:

  • ab-test-design-brief for planning an experiment before verification.
  • experimentation-throughput-strategy for monitoring overlap conflicts.
  • trustworthy-experiment-insights for statistical credibility after results.

Reference Routing

Need Read
Verification concepts references/core/knowledge.md
QA, canary, A/A, and monitoring rules references/core/rules.md
Failure scenarios and examples references/core/examples.md
Step-by-step quality roadmap workflows/create-experiment-quality-roadmap.md

Workflow

  1. Define the experiment quality risks the platform must catch.
  2. Add prelaunch verification for assignment, targeting, exposure, treatment,

metrics, and user experience.

  1. Add early launch canaries and active monitoring for misconfiguration.
  2. Run periodic A/A tests to validate infrastructure health.
  3. Track quality metrics and update the experimentation playbook.
  4. Define owners and escalation paths for active experiment issues.

Output Format

# Experiment Verification And Monitoring Plan

## Quality Risks
[What errors or trust failures this plan should prevent.]

## Prelaunch Checks
| Check | Method | Owner | Pass/Fail Criteria |
|-------|--------|-------|--------------------|

## Active Monitoring
- Canary:
- Dashboards:
- Alerts:
- Leakage/interference checks:

## Platform Health
- A/A test cadence:
- Quality metrics:
- Review process:

## Escalation Rules
- Pause if:
- Restart if:
- Investigate if:

Quality Bar

  • Do not rely on manual QA alone when the platform has recurring setup errors.
  • Do not launch experiments without verifying assignment, exposure, and metrics.
  • Do not treat A/A tests as one-time setup checks; use them as periodic health

checks when platform trust matters.

  • Do not monitor only final results; active tests need early health signals.