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BD vlm-ocr-alice

Runs the GPU part of a vlm-ocr pipeline on Leiden University's ALICE cluster. Decides whether a corpus needs ALICE, picks a GPU partition for the model, sets up storage, a pinned vLLM environment and offline weights, ships code and scans, runs a gate job and chained bulk jobs with the bundled SLURM template, and brings the page records home for post-processing. Use with vlm-ocr when OCRing scans on ALICE or deciding whether to.

scdenney/open-science-skills Claude Code author: scdenney NOASSERTION 1 file body ≈ 2 992 tokens Open the sourcegithub.com↗ analyzed 7 d ago

Runs the GPU part of a vlm-ocr pipeline on Leiden University's ALICE cluster.

As a process D 40/100 · Unfinished process — References files that are not bundled: scripts/serve_and_ocr.slurm, scripts/ocr_client.py

ProcedureSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw 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
40/100
Unfinished process
References files that are not bundled: scripts/serve_and_ocr.slurm, scripts/ocr_client.py
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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: scripts/serve_and_ocr.slurm
  • warning missing-ref reference to a missing file: scripts/ocr_client.py

Process rating: all ten parameters 40/100

Will not run. References files that are not bundled: scripts/serve_and_ocr.slurm, scripts/ocr_client.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/serve_and_ocr.slurm, scripts/ocr_client.py
  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 100Steps. 43 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2992 tokens
  • 100Progress reporting. Reports progress
  • low The response is described with custom markup (6 tags): a typed call is more reliable

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
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 431: enough signal without eating the budget
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 43 items
  • +4Has examples (0 code blocks)

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