How do I install the Ml Experiment Evaluation skill?

Run npx skillmds@latest add lvtd-llc/ml-experiment-evaluation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ml Experiment Evaluation safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ml Experiment Evaluation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ml Experiment Evaluation free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Ml Experiment Evaluation?

LVTD-LLC (@lvtd-llc) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ml Experiment Evaluation by lvtd-llc

Name: Ml Experiment Evaluation
Rating: 4.2 (5 reviews)
Author: LVTD-LLC

Ml Experiment Evaluation

Plan evaluation strategies for machine-learning product changes. Use when deciding between offline evaluation, interleaving, online A/B tests, multi-armed bandits, or model filtering for ranking, recommendation, search, personalization, or other ML-powered user experiences.

LVTD-LLC aeb7d8a 6 files · 15.1 KB Updated Sep 10, 2026

File contents

ML Experiment Evaluation

Use this skill to choose how to evaluate machine-learning product changes before they consume live experiment traffic or affect users. It focuses on offline evaluation, offline-online correlation, interleaving, model filtering, and when classic A/B testing or adaptive strategies are justified.

Source Traceability

Primary source: Next-Level A/B Testing by Leemay Nassery. Guidance is transformed and paraphrased from Chapter 4 on offline evaluation, offline-online correlation, multi-armed bandits, and interleaving for rankers.

Related skills:

experiment-sensitivity-optimization for reducing live variants and traffic.
adaptive-experimentation-strategy for bandits and dynamic allocation.
ab-test-design-brief for standard online A/B test planning.

Reference Routing

Need	Read
ML evaluation concepts	`references/core/knowledge.md`
Selection and validation rules	`references/core/rules.md`
Evaluation strategy examples	`references/core/examples.md`
Step-by-step evaluation plan	`workflows/choose-ml-evaluation-strategy.md`

Workflow

State the model change and product decision.
Identify the user harm or trust risk if a poor model reaches production.
Choose the lowest-cost evaluation that can filter bad candidates.
Check offline metrics and whether they correlate with online outcomes.
Use interleaving when ranker comparison needs high sensitivity with fewer users.
Escalate to online A/B testing or adaptive testing only when live evidence is needed and infrastructure can support it.

Output Format

# ML Evaluation Strategy

## Model Decision
[What model or ranking decision must be made.]

## Recommended Evaluation Path
[Offline only | Offline then A/B | Interleaving | A/B test | Adaptive strategy]

## Why
- Product risk:
- Offline signal available:
- Online evidence needed:
- Traffic or capacity constraint:

## Metrics
| Metric | Offline/Online | Role | Concern |
|--------|----------------|------|---------|

## Implementation Notes
- Data needed:
- Logging needed:
- Correlation check:
- Rollout guardrails:

Quality Bar

Do not send poor offline candidates to live users just to get online evidence.
Do not trust offline metrics until their relationship to online outcomes is understood.
Do not use interleaving unless the product has a ranking or choice context where attribution can be logged.
Do not recommend adaptive methods without checking data freshness, observability, and operational ownership.

---name: ml-experiment-evaluationdescription: Plan evaluation strategies for machine-learning product changes. Use when deciding between offline evaluation, interleaving, online A/B tests, multi-armed bandits, or model filtering for ranking, recommendation, search, personalization, or other ML-powered user experiences.license: MIT---# ML Experiment EvaluationUse this skill to choose how to evaluate machine-learning product changes beforethey consume live experiment traffic or affect users. It focuses on offlineevaluation, offline-online correlation, interleaving, model filtering, and whenclassic A/B testing or adaptive strategies are justified.## Source TraceabilityPrimary source: *Next-Level A/B Testing* by Leemay Nassery. Guidance istransformed and paraphrased from Chapter 4 on offline evaluation, offline-onlinecorrelation, multi-armed bandits, and interleaving for rankers.Related skills:- `experiment-sensitivity-optimization` for reducing live variants and traffic.- `adaptive-experimentation-strategy` for bandits and dynamic allocation.- `ab-test-design-brief` for standard online A/B test planning.## Reference Routing| Need | Read ||------|------|| ML evaluation concepts | `references/core/knowledge.md` || Selection and validation rules | `references/core/rules.md` || Evaluation strategy examples | `references/core/examples.md` || Step-by-step evaluation plan | `workflows/choose-ml-evaluation-strategy.md` |## Workflow1. State the model change and product decision.2. Identify the user harm or trust risk if a poor model reaches production.3. Choose the lowest-cost evaluation that can filter bad candidates.4. Check offline metrics and whether they correlate with online outcomes.5. Use interleaving when ranker comparison needs high sensitivity with fewer users.6. Escalate to online A/B testing or adaptive testing only when live evidence is needed and infrastructure can support it.## Output Format```markdown# ML Evaluation Strategy## Model Decision[What model or ranking decision must be made.]## Recommended Evaluation Path[Offline only | Offline then A/B | Interleaving | A/B test | Adaptive strategy]## Why- Product risk:- Offline signal available:- Online evidence needed:- Traffic or capacity constraint:## Metrics| Metric | Offline/Online | Role | Concern ||--------|----------------|------|---------|## Implementation Notes- Data needed:- Logging needed:- Correlation check:- Rollout guardrails:```## Quality Bar- Do not send poor offline candidates to live users just to get online evidence.- Do not trust offline metrics until their relationship to online outcomes is understood.- Do not use interleaving unless the product has a ranking or choice context where attribution can be logged.- Do not recommend adaptive methods without checking data freshness, observability, and operational ownership.

Frequently asked questions