annotate-traces-for-review SKILL.md skillA
annotate-traces-for-review 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/annotate-traces-for-review@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/annotate-traces-for-review/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.95f5cea, committed Jul 5, 2026
- Size
- 3,416 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to set up human review and annotation of LLM traces, so people (often domain experts) can label outputs, do error analysis, and build a trustworthy golden dataset. Trigger on "review my LLM outputs", "have an expert label these", "error analysis", "annotate traces", "build a golden dataset", or when automated evals are not enough for a high-stakes or specialized domain. Looking at your data is the highest-ROI thing you can do.
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
# Annotate and review traces Automated metrics are downstream of one thing: a human deciding what "good" means. For specialized or high-stakes domains (finance, health, legal), and for early-stage apps, structured human review of real traces is the single highest-ROI activity. It produces the golden labels every other eval depends on, and it surfaces failure modes you did not know to look for. ## Set up the review loop 1. **Pull a sample of traces** to review. Stratify (by topic, difficulty, low online-eval score, thumbs-down) so reviewers see the interesting cases, not 100 easy ones. 2. **Give reviewers the full context** the model had: input, retrieved docs, tool results, output. Redact PII first for regulated data (`redact-pii-for-tracing`). 3. **Use a simple, consistent schema:** pass/fail (or a small rubric score) + a **free-text failure reason** + a category tag. The free-text is where you discover new failure modes; the categories let you count them. 4. **Use the tooling** rather than spreadsheets where possible: annotation queues exist in Langfuse, Phoenix, Opik, LangSmith and let annotations attach to the trace. ## Do error analysis (not just labeling) …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/annotate-traces-for-review@sha256:d5d1ea3b7a59ea4eThe 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_7vcxx3nc7cemh3vt)
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 (3416 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_7vcxx3nc7cemh3vt GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/annotate-traces-for-review GET https://markdownregistry.com/api/v1/blob/d5d1ea3b7a59ea4e8e93e737a0e71ff68a714e4f37deb385f55efd3c1f1bff56
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.