build-eval-dataset SKILL.md skillA
build-eval-dataset 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/build-eval-dataset@git:20260705.4a58130Pins git:20260705.4a58130 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/build-eval-dataset/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.4a58130, committed Jul 5, 2026
- Size
- 3,565 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to build a good evaluation dataset for an LLM app, the part everyone underestimates. Trigger on "make an eval set", "what should I test my LLM on", "I don't have test data for my prompt", "build a golden dataset", or before setting up evals. A great eval set beats a great metric; garbage-in means your evals lie to you.
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
# Build an eval dataset Evals are only as good as the dataset behind them. A metric run over toy inputs gives you confident, wrong signal. This is how to build a set that actually reflects your app and catches real regressions. ## What makes a dataset good - **Representative** of real usage, not made-up easy cases. The distribution should match production. - **Covers the hard cases** you already know about: edge cases, ambiguous inputs, adversarial ones, the bug reports. - **Has a clear success definition** per item: a reference answer, or a rubric a judge can apply consistently. - **Small enough to iterate, big enough to trust.** Start at 20-50 items; grow toward a few hundred as the app matures. You do not need thousands to start. - **Versioned** in the repo next to the code it tests. ## Where to get the data (best to worst) 1. **Real production traffic** (best). Pull real inputs from your traces (this is the payoff of having observability). Sample across the distribution, and deliberately include failures you saw. 2. **Beta / internal usage.** Dogfood inputs before you have prod traffic. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/build-eval-dataset@sha256:07d42bc17e0e9257The line at the top of this page pins the label git:20260705.4a58130, 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_evq5ne3qkqyltta2)
1 badge image fetches in 30 days. What the badge shows.
Versions
The registry has recorded 1 version of this file, committed on Jul 5, 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 (3565 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_evq5ne3qkqyltta2 GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/build-eval-dataset GET https://markdownregistry.com/api/v1/blob/07d42bc17e0e9257d4c6375fe6453de9dc9cbd43d1b6a81ac6ef7d1cd68a52d7
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.