evaluating-ai-output SKILL.md skillA
evaluating-ai-output from cristhianzl/claude-skills-czl is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add cristhianzl/claude-skills-czl/evaluating-ai-output@git:20260625.6520aecPins git:20260625.6520aec in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- cristhianzl/claude-skills-czl
- Path
configs/agnostic/skills/evaluating-ai-output/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260625.6520aec, committed Jun 25, 2026
- Size
- 3,670 bytes
- Repository stars
- 5
- License
- MIT
Evaluate non-deterministic LLM/AI output with evals instead of one-shot "it worked" — define expected behavior first, measure pass@k / pass^k, and grade with code / model / human graders. Use when building or changing an AI/LLM feature, an agent, a prompt, a RAG pipeline, or a classifier, where a single good run is not proof of correctness. Complements writing-tests (deterministic logic) and developing-features-tdd.
Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.
What the file says
# Evaluating AI output
Code is deterministic; LLM output isn't. A feature that "worked once" can fail the next call on the same input. **Evals are the unit tests of AI work** — they measure how *often* and how *well* the output meets the bar, not just that it can.
## Read first (always)
List `learnings/` and read anything relevant — provider quirks, rubric calibration, and known-flaky cases for this project belong there.
## Define expected behavior BEFORE you implement
Write the eval first: the inputs, what a good output looks like, and what must never happen. If you can't state how you'd grade it, you don't yet understand the feature.
## Measure across repeated trials
Run each case **k times** (LLM output varies) and report:
- **pass@k** — *at least one* of k attempts succeeds. Measures **capability** ("can it do this at all?"). Typical target: pass@3 > 90%.
- **pass^k** — *all* k attempts succeed. Measures **stability/reliability** ("does it do this every time?"). Use for **critical paths** (auth, money, irreversible actions). pass^3 means 3 consecutive clean runs.
…Read the whole file at its exact version.
How to install
mdr add cristhianzl/claude-skills-czl/evaluating-ai-output@sha256:347be76034c7130eThe line at the top of this page pins the label git:20260625.6520aec, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
[](https://markdownregistry.com/a/art_mxb2okee2dks3utg)
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Versions
The registry has recorded 1 version of this file, committed on Jun 25, 2026.
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (3670 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
cristhianzl/claude-skills-czl · 5 stars · license MIT · pushed 2026-09-24 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_mxb2okee2dks3utg GET https://markdownregistry.com/api/v1/resolve?ref=cristhianzl/claude-skills-czl/evaluating-ai-output GET https://markdownregistry.com/api/v1/blob/347be76034c7130ef8b06eea62675e5ddbb3d622a5d367c72e2085e577a15679
Your AI assistant does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.
Guides: the SKILL.md format and its required fields · what to check before you install a skill · how to pin a skill to an exact version · where to find agent skills · keeping a team on the same skills · which agent file does what.
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