eval-llm-judge SKILL.md skillA
eval-llm-judge from calibre-labs/reforge-ai-evals is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add calibre-labs/reforge-ai-evals/eval-llm-judge@git:20261102.9ec9f47Pins git:20261102.9ec9f47 in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- calibre-labs/reforge-ai-evals
- Path
skills/eval-llm-judge/SKILL.md- Versions recorded
- 3
- Latest version
- git:20261102.9ec9f47, committed Nov 2, 2026
- First version recorded
- Oct 3, 2026
- Size
- 13,989 bytes
- Repository stars
- 94
- License
- MIT
Write LLM-as-judge evaluator prompts for AI products. Use this skill whenever you need to evaluate semantic properties of AI outputs that cannot be checked programmatically — output quality, tone, factual accuracy, edge case handling, reference alignment. Also use when auditing existing judge prompts for bias, vagueness, or compound criteria. Output is a judge prompt and a runner in the project's own scorer format (a Python runner when the project has none), saved where the project keeps its…
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
# LLM Judge Evaluator Skill An LLM-as-judge evaluator uses a language model to score another model's output against a defined criterion. It handles semantic properties that code-based checks cannot: is the ranking defensible? Did the agent handle ambiguity gracefully? Does the output align with the reference? The tradeoff: judges are slower, more expensive, and non-deterministic. They require calibration against human labels before you can trust their scores. Use them only after code-based checks have covered the structural requirements. ## When to use this skill Write an LLM judge when the property requires reasoning that a human reviewer would apply: - **Output correctness**: are the results correct and in a defensible order or format? - **Edge case handling**: did the agent appropriately flag ambiguity or decline gracefully? - **Reference alignment**: does the output agree with a gold-standard expected response? - **Tone and framing**: is the hedging appropriate for a speculative or high-stakes claim? - **Faithfulness**: does the output stay within what the cited sources actually say? Do NOT write an LLM judge for: …
Read the whole file at its exact version.
How to install
mdr add calibre-labs/reforge-ai-evals/eval-llm-judge@sha256:59a72cb012064927The line at the top of this page pins the label git:20261102.9ec9f47, 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_ct6fl2qh6geqmmbq)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 3 versions of this file, between Oct 3, 2026 and Nov 2, 2026, about one every 15 days.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20261102.9ec9f47 latest | 2026-11-02 | 9ec9f47 | 13,989 B | A | view · diff |
| git:20261016.c78dbc2 | 2026-10-16 | c78dbc2 | 10,043 B | A | view · diff |
| git:20261003.9a39ab5 | 2026-10-03 | 9a39ab5 | 9,730 B | A | view |
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 (13989 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
calibre-labs/reforge-ai-evals · 94 stars · license MIT · pushed 2026-11-05 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_ct6fl2qh6geqmmbq GET https://markdownregistry.com/api/v1/resolve?ref=calibre-labs/reforge-ai-evals/eval-llm-judge GET https://markdownregistry.com/api/v1/blob/59a72cb012064927605f96b28dee2a8b5bb496d493c96749ffd0f2454ad1251a
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.