trace-based-testing SKILL.md skillA
trace-based-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/trace-based-testing@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/trace-based-testing/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.4a58130, committed Jul 5, 2026
- Size
- 3,355 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to turn real production traces into a regression test suite, so bugs you saw once never come back. Trigger on "turn traces into tests", "capture this bug as a test", "build tests from production data", "my eval set is out of date", or after debugging a production incident. This is the loop that connects observability to 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
# Trace-based testing Observability records what really happened; evaluation checks whether it was good. Trace-based testing is the bridge: promote real production traces into repeatable test cases. Done consistently, your eval set stays representative on its own and every incident makes the app more robust. ## The loop 1. **A trace shows a problem** (a bad answer, a hallucination, a tool misfire, a slow/expensive run). You find it via alerts (`set-up-drift-alerts`) or debugging (`debug-agent-from-traces`). 2. **Capture the trace's input as a test case.** The input, the retrieved context, the expected-correct output (write the gold answer, or a rubric). Redact PII first (`redact-pii-for-tracing`). 3. **Add it to the eval set** (`build-eval-dataset`), tagged with the incident so you know why it exists. 4. **Fix the bug**, confirm the new case passes. 5. **CI runs it forever** (`eval-driven-development`) so the bug cannot silently return. ## Make it systematic, not manual …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/trace-based-testing@sha256:813f9b34362449d1The 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_bvxsxjfci2u6n7pl)
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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 (3355 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_bvxsxjfci2u6n7pl GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/trace-based-testing GET https://markdownregistry.com/api/v1/blob/813f9b34362449d1598f709326ebaffbcb4b00b2b48178fcf514bbe947a0cf0c
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.