Use this to capture user feedback on LLM outputs (thumbs up/down, edits, corrections, implicit signals) and feed it back into observability and evals. Trigger on "add thumbs up/down", "collect feedback on responses", "how do I know if users like the answers", "improve from real usage", "human feedback loop". Turn real user signal into your best source of eval data.
$npx -y skills add contextjet-ai/awesome-llm-observability --skill collect-user-feedbackInstalls into the current project.
Run `npx skills use "https://github.com/contextjet-ai/awesome-llm-observability" --skill "contextjet-ai/awesome-llm-observability/collect-user-feedback"` 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.