eval-driven-development SKILL.md skillA
eval-driven-development 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/eval-driven-development@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/eval-driven-development/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.23078be, committed Jul 5, 2026
- Size
- 2,608 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to build or change an LLM feature the reliable way, by writing evals first and iterating against them, instead of tweaking prompts by vibes. Trigger on "how do I improve this prompt", "my changes keep breaking other things", "how do I know if this is better", "iterate on my agent", or any prompt/model/RAG change. This is test-driven development for LLMs.
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
# Eval-driven development (TDD for LLMs) Changing a prompt and eyeballing one output is how LLM apps quietly regress. Eval-driven development flips it: define what "good" means as a small test set, then iterate until you pass it. It is the single habit that separates apps that get more reliable over time from ones that drift. ## The loop 1. **Write the eval first.** Before touching the prompt, collect 10 to 30 real input cases and define what a good output looks like (a reference answer, or a rubric for LLM-as-a-judge). Include the failure cases you already know about. 2. **Run it against the current version.** This is your baseline score. Now you have a number, not a feeling. 3. **Make one change.** New prompt, different model, changed retrieval. One at a time so you know what moved the score. 4. **Re-run the evals.** Kept the score or improved it? Keep the change. Dropped it? Revert. No debate. 5. **Add every new bug as a case.** When something breaks in production, capture that input as a new eval case before you fix it. The suite grows into a regression net. ## Wire it into the workflow - Keep the eval set in the repo (versioned), next to the prompts it tests. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/eval-driven-development@sha256:245a0f873f0d6ea9The 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_fomlzvkaquv3b57x)
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 (2608 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_fomlzvkaquv3b57x GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/eval-driven-development GET https://markdownregistry.com/api/v1/blob/245a0f873f0d6ea9b802fed640d615c1b5035b3b1e24bd7588a0ea900ead4322
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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