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BD rag-eval

Filesystem RAG benchmarks: corpus/, train.json, evaluate_rag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or evals outside this repo layout.

ClawHub Agent Skills author: NVIDIA 1 file body ≈ 2 010 tokens Open the sourceclawhub.ai↗ analyzed 14 d ago

Filesystem RAG benchmarks: corpus/, train.json, evaluaterag.py (RAGAS quality). Not for prod monitoring, latency/throughput benchmarking (use rag-perf), or…

As a process D 47/100 · Unfinished process — References files that are not bundled: references/benchmark-execution.md, references/dataset-and-conversion.md, references/benchmark-execution.md

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
D
47/100
Unfinished process
References files that are not bundled: references/benchmark-execution.md, references/dataset-and-conversion.md, references/benchmark-execution.md
Tools and files w 18
0
Result and completion w 14
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/benchmark-execution.md
  • warning missing-ref reference to a missing file: references/dataset-and-conversion.md
  • warning missing-ref reference to a missing file: references/benchmark-execution.md
  • warning missing-ref reference to a missing file: references/evaluate-rag-cli.md
  • warning missing-ref reference to a missing file: references/result-analysis.md
  • warning missing-ref reference to a missing file: references/benchmark-execution.md
  • warning missing-ref reference to a missing file: scripts/eval/evaluate_rag.py
  • warning missing-ref reference to a missing file: scripts/rag-perf
  • warning missing-ref reference to a missing file: scripts/eval/README.md

Process rating: all ten parameters 47/100

Will not run. References files that are not bundled: references/benchmark-execution.md, references/dataset-and-conversion.md, references/benchmark-execution.md
  • 0Tools and files. 9 referenced file(s) missing: references/benchmark-execution.md, references/dataset-and-conversion.md, references/benchmark-execution.md
  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 6 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 25 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2010 tokens
  • low 10 top-level sections: this looks like several domains in one skill

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +3Output format is not stated: the model decides each time
  • -2localhost URLs: will not work for another user
  • +2Single-language instructions
  • +4Description says when NOT to use the skill
  • +3Description length 188: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 25 items
  • +4Has examples (2 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.