evals-start
Entry point for evals. Use when the user asks for help with evals, does not know where to begin, or asks for something no other skill in this plugin matches. Do NOT use when a more specific skill in this plugin already matches; load that skill directly.
- Installs
- 786
- Last 24h
- +9
- Trending rank
- #9166
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/ai-evals-course/evals-skills --skill evals-start- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads evals-start automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Evals Start
This plugin splits eval work into targeted skills. Your job here is small: find the row below that matches the user's situation, tell the user which skill you are loading and why, then load that skill and follow its workflow from start to finish instead of improvising your own version of it.
| Situation | Skill to load |
|---|---|
| Has traces, wants to find failure modes, no established taxonomy yet | error-discovery |
| Has an existing eval pipeline and wants to know if it can be trusted | eval-audit |
| Has a known failure mode that code can check (e.g., format, schema, regex, execution) | write-code-eval |
| Has a known failure mode and wants an LLM judge for it | write-judge-prompt |
| Has an LLM judge or evaluator and wants to check its quality | validate-evaluator |
| Has no traces to review yet | generate-synthetic-data, then error-discovery |
| Wants a custom annotation interface for some other labeling task | build-review-interface |
| Wants to evaluate a RAG pipeline | evaluate-rag |
Most requests that mention error analysis with traces in hand mean error-discovery. New users with an existing pipeline usually need eval-audit first. This file holds only routing. When in doubt about which row fits, ask the user instead of guessing. The workflow lives in the targeted skill.
FAQ
What does the evals-start skill do?
Entry point for evals. Use when the user asks for help with evals, does not know where to begin, or asks for something no other skill in this plugin matches. Do NOT use when a more specific skill in this plugin already matches; load that skill directly.
How do I install evals-start?
Run npx skills add https://github.com/ai-evals-course/evals-skills --skill evals-start in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/evals-start/ (personal) or .claude/skills/evals-start/ (project).
Which AI agents can use evals-start?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is evals-start safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains evals-start?
It is published by ai-evals-course in ai-evals-course/evals-skills. It has 786 recorded installs.
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Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with unit tests per write-code-eval).
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Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use write-code-eval. To validate an existing judge, use validate-evaluator.
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Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists. Do NOT use when the goal is to build a new evaluator from scratch (use error-discovery, write-judge-prompt, or validate-evaluator instead).
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Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
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Build a custom browser-based annotation interface tailored to your data for reviewing LLM traces and collecting structured feedback. Use when you need to build an annotation tool, review traces, or collect human labels.
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