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BF super-ocr

Production-grade OCR with intelligent engine selection. Tesseract (lightweight, fast) and PaddleOCR (high accuracy, Chinese-optimized). Use when extracting text from images, processing Chinese documents, needing confidence scores, or working with mixed Chinese/English content.

ClawHub Agent Skills author: Nima Chu v0.1.0 22 files body ≈ 1 505 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 46/100 · Will not run — References files that are not bundled: scripts/engine_selector.py, scripts/tesseract_ocr.py, scripts/paddle_ocr.py

ProcedureInfrastructuretype 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
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
F
46/100
Will not run
References files that are not bundled: scripts/engine_selector.py, scripts/tesseract_ocr.py, scripts/paddle_ocr.py
Tools and files w 18
0
Inputs and preconditions w 11
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: 21. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/engine_selector.py
  • warning missing-ref reference to a missing file: scripts/tesseract_ocr.py
  • warning missing-ref reference to a missing file: scripts/paddle_ocr.py

Process rating: all ten parameters 46/100

Will not run. References files that are not bundled: scripts/engine_selector.py, scripts/tesseract_ocr.py, scripts/paddle_ocr.py
  • 0Tools and files. 3 referenced file(s) missing: scripts/engine_selector.py, scripts/tesseract_ocr.py, scripts/paddle_ocr.py
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 1 mutating operations with no state check
  • 50Failures and branches. 0 branches, has a failure section
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 45 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1505 tokens

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)
  • -43 reference files, but SKILL.md never points to them: the model will not open them
  • -31 of 4 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 277: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 45 items
  • +3Output format is stated explicitly
  • +4Has examples (8 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: clean
This is a local OCR skill whose risks are mainly disclosed setup and dependency risks, not hidden or malicious behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026