monitor-rag-quality SKILL.md skillA
monitor-rag-quality 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/monitor-rag-quality@git:20260705.23078bePins git:20260705.23078be 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/monitor-rag-quality/SKILL.md- Versions recorded
- 1
- Latest version
- git:20260705.23078be, committed Jul 5, 2026
- Size
- 3,090 bytes
- Repository stars
- 45
- License
- a license file GitHub does not identify
Use this to measure and monitor the quality of a RAG (retrieval-augmented generation) pipeline - whether it retrieves the right context and answers faithfully. Trigger on "my RAG gives wrong answers", "is my retrieval any good", "the chatbot makes things up", "evaluate my RAG", "improve RAG accuracy". Diagnose whether the failure is in retrieval or generation - they need different fixes.
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
# Monitor & improve RAG quality Most "the LLM is wrong" bugs in a RAG app are actually **retrieval** bugs - the model was handed the wrong context and did its best. Measure both halves separately. ## The 4 metrics that matter (RAG quartet) | Metric | Question | Which half | |---|---|---| | **Context precision** | Are the retrieved chunks relevant (not noise)? | Retrieval | | **Context recall** | Did retrieval find *all* the needed info? | Retrieval | | **Faithfulness** | Is the answer grounded in the retrieved context (no made-up facts)? | Generation | | **Answer relevance** | Does the answer actually address the question? | Generation | [Ragas](https://github.com/vibrantlabsai/ragas) implements all four; [DeepEval](https://github.com/confident-ai/deepeval) and most observability platforms (Langfuse, Phoenix, Opik) have RAG evaluators too. ## Diagnose: retrieval vs generation Read a failing trace (see `debug-agent-from-traces`): - **Low context recall/precision** → fix **retrieval**: chunking strategy, embedding model, top-k, reranking, query rewriting, metadata filters. No amount of prompt tuning fixes missing context. …
Read the whole file at its exact version.
How to install
mdr add contextjet-ai/awesome-llm-observability/monitor-rag-quality@sha256:f9f8d366fd6c2ec4The line at the top of this page pins the label git:20260705.23078be, 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_hnw4q2pdux3dezh3)
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 (3090 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_hnw4q2pdux3dezh3 GET https://markdownregistry.com/api/v1/resolve?ref=contextjet-ai/awesome-llm-observability/monitor-rag-quality GET https://markdownregistry.com/api/v1/blob/f9f8d366fd6c2ec49742ca062bfb5a271c609d162c2991ffe7d664e53578ab0f
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.