rag-evaluation SKILL.md skillA
rag-evaluation from andreibesleaga/gabbe is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.
mdr add andreibesleaga/gabbe/rag-evaluation@git:20261014.de0823cPins git:20261014.de0823c in mdr.lock. Pin the fingerprint instead.
- Audit grade
- A, 17 of 17 checks passed
- Kind
- SKILL.md skill
- Repository
- andreibesleaga/gabbe
- Path
plugins/gabbe-ai/skills/rag-evaluation/SKILL.md- Versions recorded
- 1
- Latest version
- git:20261014.de0823c, committed Oct 14, 2026
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- a license file GitHub does not identify
Evaluate retrieval-augmented-generation pipelines with reference-free metrics to detect silent retrieval and grounding failures. Use when: evaluate rag pipeline, measure faithfulness, context precision recall, detect rag hallucination, reference-free rag metrics, score retrieval quality.
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
# RAG Evaluation
## Goal
Evaluate retrieval-augmented-generation (RAG) pipelines using *reference-free* metrics so you can
detect **silent failures** — cases where the system returns a fluent, confident answer that is
actually ungrounded, off-topic, or built on the wrong retrieved chunks. Reference-free means you
score the (query, context, answer) triple itself without needing a pre-written gold answer, which
is what makes evaluation possible over real production logs at scale.
## Steps
1. **Capture each RAG turn as a triple.**
- Record `(query, retrieved_contexts, answer)` for every turn — the user query, the exact
chunks the retriever returned, and the generated answer. This triple is the unit of analysis;
without the retrieved contexts you cannot diagnose whether a bad answer is a retrieval failure
or a generation failure.
2. **Score the 4 core metrics.**
- **Faithfulness** — is *every* claim in the answer grounded in the retrieved context? Low
faithfulness = hallucination (the model asserted something the context never supported). This
is the primary safety metric.
…Read the whole file at its exact version.
How to install
mdr add andreibesleaga/gabbe/rag-evaluation@sha256:b58d8b299ac4b589The line at the top of this page pins the label git:20261014.de0823c, 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_47sb5w4qtnmo6qig)
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Versions
The registry has recorded 1 version of this file, committed on Oct 14, 2026.
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- pass: No instruction to send local credentials anywhere
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Source
andreibesleaga/gabbe · 23 stars · license a license file GitHub does not identify · pushed 2026-11-02 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_47sb5w4qtnmo6qig GET https://markdownregistry.com/api/v1/resolve?ref=andreibesleaga/gabbe/rag-evaluation GET https://markdownregistry.com/api/v1/blob/b58d8b299ac4b5897d969e67e4c8a20481747a93e6ea327462f752a2d7bcd3a4
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