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.
How to install annotate-traces-for-review?
contextjet-ai/awesome-llm-observability/annotate-traces-for-review
$npx -y skills add contextjet-ai/awesome-llm-observability --skill annotate-traces-for-reviewInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/contextjet-ai/awesome-llm-observability" --skill "contextjet-ai/awesome-llm-observability/annotate-traces-for-review"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/contextjet-ai/awesome-llm-observability" that are relevant to the current task. Run `npx skills add "https://github.com/contextjet-ai/awesome-llm-observability"` and select the relevant skills, then follow their instructions.
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