# hamelsmu/evals-skills

Skills for AI Evals to compliment the course: AI Evals For Engineers & PMs

Repository: https://github.com/hamelsmu/evals-skills
Canonical: https://ross.abutalabs.com/products/evals-skills
Homepage: https://maven.com/parlance-labs/evals?promoCode=evals-info-url
License: MIT
License Family: permissive
Archived: true
Last push: 2026-08-16T05:28:25+00:00

## Health v2 (maintenance only)
Score: 10/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 98, release rhythm 35, longevity 13
- inputs: {"age_days": 185, "days_push": 17, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, archived
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1664, forks 169 (observed 2026-08-28T04:05:18.942331+00:00)

## What it is
A collection of skills (prompts/instructions) that guide AI coding agents like Claude Code through building and auditing LLM evaluation pipelines. It includes skills for eval audits, error analysis, synthetic data generation, LLM-as-Judge prompt design, evaluator calibration, RAG evaluation, and annotation interfaces.

## Use cases
- audit my LLM eval pipeline for common mistakes
- categorize failures from LLM traces with error analysis
- generate diverse synthetic test inputs for evals
- design an LLM-as-Judge prompt for subjective quality criteria
- calibrate an LLM judge against human labels
- evaluate retrieval and generation quality in a RAG pipeline
- build an annotation interface for human trace review

## When to choose
- you use Claude Code or a skills-compatible coding agent and want expert guidance on LLM evals
- you are building or improving an eval pipeline and want to catch common mistakes
- you need structured workflows for error analysis, judge calibration, or RAG evaluation
- you are a student of the AI Evals course applying its methodology

## When to avoid
- you want a standalone eval framework or library to run in CI without a coding agent
- you need a general-purpose testing tool unrelated to LLM evaluation
- you don't use an AI coding agent that supports the skills/plugin format
- you want the actively maintained version - this repo is deprecated in favor of ai-evals-course/evals-skills

## Facets
- artifact type: plugin
- maturity: maintenance
- function: testing, llm-inference, agent-framework, prompt-engineering, rag, developer-tools
- domain: large-language-models, artificial-intelligence, developer-tools, testing, education
- platform: cli, cross-platform
- tags: llm-evals, claude-code-plugin, skills, llm-as-judge, error-analysis, synthetic-data, rag-evaluation, annotation-interface, deprecated-repo, nodejs

## Member repositories
- hamelsmu/evals-skills (main) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:18.942331+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:44:28.934048+00:00, confidence not recorded.
  - readme: https://github.com/hamelsmu/evals-skills (fetched 2026-08-28T04:05:18.942331+00:00, sha 6398333b9ece)
  - homepage: https://maven.com/parlance-labs/evals?promoCode=evals-info-url (fetched 2026-08-29T11:17:00.635455+00:00, sha 0121c63392fb)
  - site_page: https://maven.com/about (fetched 2026-08-29T11:17:00.647974+00:00, sha 1a76fdf030bf)
  - site_page: https://maven.com/parlance-labs/evals (fetched 2026-08-29T11:17:00.645538+00:00, sha 0121c63392fb)
- Data as of 2026-08-30T08:39:29.467469+00:00.