LLM Evaluation Engineer AI Skill - Ingrid
Evaluate LLM apps: golden sets, LLM-as-judge, RAG and agent metrics, and a CI gate that blocks regressions.
Claude skill package instant downloadingrid-llm-evaluation-engineer.skill
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How it works
Ask it something hard.
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Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com
// the file itself
The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.
You are Ingrid, a Senior LLM Evaluation Engineer who builds the golden set, calibrates an LLM-as-judge against human labels, and reports a delta with a confidence interval. You have been activated to prove a change is genuinely better.
// what's inside
Teams shipping prompt and model changes blind, who need a real eval gate before release.
// two ways to get it
$9/mo cancel anytime, or $59 a year
$19 one payment
Already on Library or Both? This one is included, no need to buy the file.
// ask your assistant
Go to kissmyskills.com/pages/connector and set up the "LLM Evaluation Engineer AI Skill - Ingrid" skill (handle: ingrid-llm-evaluation-engineer) for me
Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →
Drop Ingrid into Claude and get a senior LLM evaluation engineer whose rule is simple: no ship without an eval gate.
Ingrid evaluates LLM apps and agents: building golden datasets (coverage, edge cases, no leakage, versioning), metric selection, LLM-as-judge (rubric design, pairwise vs pointwise, bias and position effects with mitigations, calibration against human labels), RAG metrics (faithfulness, context and answer relevance), agent trajectory eval (tool-call correctness, task completion), offline harnesses and CI regression gates, online eval (A/B, guardrail metrics, human review sampling), and statistical rigor (confidence intervals, significance). Tools like Ragas, promptfoo, DeepEval, LangSmith and Braintrust, without lock-in. Always evaluate on a held-out set before shipping.
What you get
How to install
Download the .skill package → open Claude → paste SKILL.md into your Project Instructions or system prompt → describe your requirement → Ingrid builds the answer. Includes a full worked example so you see exactly what you get.
After checkout, the download link lands in your inbox. Save the .skill file: Claude takes it as it is.
Any AI chat you already use: Claude, ChatGPT, Gemini, Grok, Copilot or another.
Claude: Settings → Capabilities → Skills, upload the .skill file. ChatGPT, Gemini and others: open the .skill file like a .zip archive and paste SKILL.md into custom instructions or Project instructions.
Your AI is now configured as a specialist. Ask it anything inside its domain.
No technical knowledge required.
With Library there is nothing to download. Connect your AI once, then just ask: "Load LLM Evaluation Engineer AI Skill from KissMySkills."
Evaluate LLM apps: golden sets, LLM-as-judge, RAG and agent metrics, and a CI gate that blocks regressions. Load it once into Claude Projects and you get a configured LLM Evaluation Engineer without re-explaining context at the start of every session. Build and evaluate against a held-out test set in a development or staging environment, add guardrails, cost and rate limits, and human review, and roll out behind evals and monitoring before serving agents or LLM features in production.
Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.
Works with Claude (recommended), ChatGPT, Gemini, Perplexity and Copilot, and any AI chat that accepts system prompts. Claude Projects gives the best results.
Without a skill file your AI starts every session as a general assistant. With Ingrid loaded it applies LLM Evaluation Engineer methodology from the first message, with consistent quality every time. Build and evaluate against a held-out test set in a development or staging environment, add guardrails, cost and rate limits, and human review, and roll out behind evals and monitoring before serving agents or LLM features in production.
This one and 2,500+ others, inside Claude, ChatGPT, Claude Code, Codex or Cursor. Cancel anytime.