WO

Evaluates WooAIAssistant using automated scenarios and AI-driven rubric scoring.

Install

mkdir -p .claude/skills/woo-ai-smoke && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/19031" && unzip -o skill.zip -d .claude/skills/woo-ai-smoke && rm skill.zip

Installs to .claude/skills/woo-ai-smoke

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.
363 charsno explicit “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Advanced

Key capabilities

  • →Evaluate WooAIAssistant against structured scenarios
  • →Apply hard invariants for deterministic failures
  • →Score responses using an LLM-as-judge rubric
  • →Store run records in JSONL format
  • →Compare runs against stored baselines
  • →Surface regressions in assistant performance

How it works

The main Claude dispatches a subagent to run the evaluation pipeline, which involves setting up scenarios, running tests against the WooAIAssistant, applying hard invariants, and scoring responses with an LLM rubric.

Inputs & outputs

You give it
Scenario suite, samples, mode, baseline, run label, head SHA, and branch
You get back
A markdown report summarizing evaluation results, including regressions and JSONL run path

When to use woo-ai-smoke

  • →Smoke-test AI assistant
  • →Evaluate response quality
  • →Detect regressions in assistant

About woo-ai-smoke

Runs evaluations against the demo store and evaluates responses using a rubric judged by an LLM. Writes JSONL run records and compares results against baselines to catch regressions.

Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated t

When not to use it

  • →When the main Claude instance should run the pipeline directly
  • →When re-judging or re-parsing raw smoke lines in the main context
  • →When the subagent should not have full tool access

Limitations

  • →Main Claude never runs the pipeline itself
  • →Only 'rest' mode is currently wired up
  • →Requires `~/.woo-ai-smoke/store.env` with five required keys

How it compares

This skill delegates the entire evaluation pipeline to a subagent, returning only a concise markdown report, which avoids context overload in the main session compared to running it directly.

Compared to similar skills

woo-ai-smoke side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
woo-ai-smoke (this skill)03moCautionAdvanced
agent-evaluation39moNo flagsAdvanced
phoenix-evals33moNo flagsAdvanced
mlops-validation29moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

You might also like

agent-evaluation

davila7

Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents achieve less than 50% on real-world benchmarks Use when: agent testing, agent evaluation, benchmark agents, agent reliability, test agent.

331

phoenix-evals

Arize-ai

Build and run evaluators for AI/LLM applications using Phoenix.

319

mlops-validation

fmind

Guide to implement rigorous validation layers including static analysis, automated testing, structured logging, and security scanning.

28

evaluating-machine-learning-models

jeremylongshore

Build this skill allows AI assistant to evaluate machine learning models using a comprehensive suite of metrics. it should be used when the user requests model performance analysis, validation, or testing. AI assistant can use this skill to assess model accuracy, p... Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.

13

create-eval

HolmesGPT

This skill should be used when the user asks to "create an eval", "write an eval test", "add a new eval", "create a test case", "write a test for Holmes", or discusses LLM evaluation tests, eval fixtures, or test_case.yaml files for the HolmesGPT project.

10

evaluate

surus-lat

Run benchy evaluations against models or systems. Covers the canonical smoke→full workflow, config selection, task filtering, exit policies, and reading run_outcome.json. Use when asked to evaluate, benchmark, or run benchy against a model or system config.

00

Search skills

Search the agent skills registry