set-up-ab-testing SKILL.md skillA
set-up-ab-testing 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/set-up-ab-testing@git:20260705.95f5ceaPins git:20260705.95f5cea 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/set-up-ab-testing/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.95f5cea, committed Jul 5, 2026
- Size
- 3,551 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to test an LLM change (new prompt, new model, new retrieval) on real traffic before rolling it out to everyone. Trigger on "A/B test my prompt", "roll out a new model safely", "compare two prompts in production", "canary this change", "does this actually improve things for real users". Measure impact on real users, gated, before a full switch.
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
# A/B test LLM changes in production Offline evals tell you a change *should* be better; a production experiment tells you it *is*, on real users and real traffic. For anything user-facing, run a controlled rollout instead of flipping the switch for everyone. ## When to use this vs offline evals - **Offline evals** (`add-llm-evals`): fast, cheap, run in CI, catch regressions before deploy. Do these first, always. - **A/B / online experiment:** use when the change is user-facing and the offline metric does not fully capture success (helpfulness, engagement, task completion, downstream conversion). Offline says "passes"; A/B says "users actually do better." ## Set it up 1. **Pick one variable.** New prompt OR new model OR new retrieval, not all at once, or you cannot attribute the result. 2. **Define the metric up front.** The primary success metric (task completion, thumbs-up rate, downstream action) plus guardrail metrics (cost, latency, error rate) that must not regress. Decide the win condition before you start. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/set-up-ab-testing@sha256:b45a194fc55cc79eThe line at the top of this page pins the label git:20260705.95f5cea, 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_ib37kh74o7o2qjck)
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 (3551 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_ib37kh74o7o2qjck GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/set-up-ab-testing GET https://markdownregistry.com/api/v1/blob/b45a194fc55cc79e0e7f599eacf2e81c97d9d804c6f3d44192fe944222a44d6f
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.