ML Experiment Evaluation

What it does

When your agent uses it

• Deciding between offline evaluation
• Online A/B tests
• Multi-armed bandits
• Model filtering for ranking

Example prompts

• “/ml-experiment-evaluation”

Requirements

• Compatibility (from SKILL.md): Codex, Claude Code, and other Agent Skills-compatible clients.

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1State the model change and product decision.
2. 2Identify the user harm or trust risk if a poor model reaches production.
3. 3Choose the lowest-cost evaluation that can filter bad candidates.
4. 4Check offline metrics and whether they correlate with online outcomes.
5. 5Use interleaving when ranker comparison needs high sensitivity with fewer
6. 6Escalate to online A/B testing or adaptive testing only when live evidence is

What it can do on your machine

Read from SKILL.md and the folder at commit 78497e5. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Codex, Claude Code, and other Agent Skills-compatible clients.

  From compatibility in the SKILL.md frontmatter.

Context cost

ML Experiment Evaluation loads about 801 tokens when it runs, and up to ~2.8k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 253 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~75

When it runs · the whole SKILL.md, loaded when a task matches

~801

With references · SKILL.md plus every file in references/, read only if the agent opens them

~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from hashgraph-online/awesome-codex-plugins at commit 78497e5, republished under its MIT licence (© hashgraph-online). 253 words, ~801 tokens.

name

ml-experiment-evaluation

description

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.

compatibility

Codex, Claude Code, and other Agent Skills-compatible clients.

license

MIT

metadata.version

0.1.0

metadata.displayName

ML Experiment Evaluation

metadata.category

Product Management

metadata.tags

practical-ab-testing,next-level-ab-testing,ab-testing,experimentation,machine-learning

# 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:

© hashgraph-online, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in plugins/LVTD-LLC/skills/skills/ml-experiment-evaluation of hashgraph-online/awesome-codex-plugins.

• SKILL.md
• guidelines.md
• references/core/examples.md
• references/core/knowledge.md
• references/core/rules.md
• workflows/choose-ml-evaluation-strategy.md

Open the folder on GitHubat commit 78497e5