collect-user-feedback SKILL.md skillA
collect-user-feedback 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/collect-user-feedback@git:20260705.6b497b1Pins git:20260705.6b497b1 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/collect-user-feedback/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.6b497b1, committed Jul 5, 2026
- Size
- 3,536 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
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.
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
# Collect user feedback Your users are running the best eval you have, for free, every day. Capturing their reactions and attaching them to the trace turns production into a continuous source of labeled data. Most teams either skip this or collect it and never use it. ## Capture both explicit and implicit signals **Explicit** (ask directly): - Thumbs up/down on a response (the simplest, highest-signal control). - A short reason on thumbs-down (dropdown: wrong / unhelpful / unsafe / other). - Accept / edit / reject on a suggested output (great for copilot-style apps). **Implicit** (infer from behavior, no extra UI): - Did the user retry, rephrase, or immediately give up? (dissatisfaction). - Did they copy/use the output, or continue the task? (satisfaction). - Did a downstream action succeed (the code ran, the ticket resolved)? Implicit signals are noisier but you get them on 100% of traffic instead of the small % who click thumbs. ## Wire it to the trace (this is the key step) …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/collect-user-feedback@sha256:0852df0b4c373e30The line at the top of this page pins the label git:20260705.6b497b1, 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_a3q4utmqfntlaefa)
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 (3536 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_a3q4utmqfntlaefa GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/collect-user-feedback GET https://markdownregistry.com/api/v1/blob/0852df0b4c373e30bc831dc859f4a77624ac0af4af8e5000311ce60dc3239fd5
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.