add-llm-evals SKILL.md skillA
add-llm-evals from contextjet-ai/awesome-llm-observability is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add contextjet-ai/awesome-llm-observability/add-llm-evals@git:20260705.23078bePins git:20260705.23078be in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- contextjet-ai/awesome-llm-observability
- Path
skills/add-llm-evals/SKILL.md- Versions recorded
- 2
- Latest version
- git:20260705.23078be, committed Jul 5, 2026
- First version recorded
- Jul 5, 2026
- Size
- 3,451 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this when adding evaluation to an LLM/agent app - measuring output quality (correctness, faithfulness, relevance, safety) rather than just watching traces. Trigger on "add evals", "test my prompt", "is my RAG accurate", "catch regressions", "score outputs", or setting up an eval suite in CI. Covers offline (CI) and online (production LLM-as-a-judge) evaluation.
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
# Add evaluations to an LLM app Observability tells you *what happened*; evaluation tells you *whether it was good*. Add both an offline suite (runs in CI on a fixed dataset) and, optionally, online scoring (grades production traffic). ## Pick the eval type per metric Two families, use both where relevant: - **Reference-based** (you have a ground-truth answer): exact/fuzzy match, semantic similarity, ROUGE/BLEU - cheap, deterministic. Good for classification, extraction, closed QA. - **Reference-free / LLM-as-a-judge** (open-ended output): a strong model scores the output against a rubric (faithfulness, relevance, coherence, safety). Scalable proxy for human judgment - but calibrate it (see caveats). For RAG specifically, use the standard quartet: **faithfulness, answer relevance, context precision, context recall** (Ragas implements these). ## Build the offline suite (do this first) 1. **Curate a dataset** - 20-100 representative inputs with expected outputs or rubrics. Pull real cases from production traces (this is where observability + eval connect). Version it in the repo. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/add-llm-evals@sha256:25e5a85673c7857eThe line at the top of this page pins the label git:20260705.23078be, 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_jshfpyhhnk7kueeo)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 2 versions of this file, all committed on Jul 5, 2026.
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260705.23078be latest | 2026-07-05 | 23078be | 3,451 B | A | view · diff |
| git:20260705.23977fd | 2026-07-05 | 23977fd | 3,473 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 (3451 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
contextjet-ai/awesome-llm-observability · 45 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_jshfpyhhnk7kueeo GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/add-llm-evals GET https://markdownregistry.com/api/v1/blob/25e5a85673c7857e1c420bc7cfdcd01434660a7690018c5b7294af5edc627574
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.