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BB local-image-ocr-aipc

Image OCR, text recognition, extract text from image, scan document, read image text, invoice OCR, receipt OCR, contract recognition, table extraction, business card OCR, ID recognition, screenshot text extraction, document digitization. Runs locally on Windows using the GLM-OCR model, supports mixed Chinese/English text, prioritizes Intel iGPU inference, no cloud API calls.

ClawHub Agent Skills author: Crystal Liu v2.0.0 MIT-0 5 files body ≈ 4 228 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

As a process B 71/100 · Nearly there — weak spots: when it triggers, inputs and preconditions, running it twice

IntegrationGitHubInfrastructureFinancetype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
B
71/100
Nearly there
Inputs and preconditions w 11
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 71/100

  • 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. 3 mutating operations with no state check
  • 70Execution cost. Instruction body is 4228 tokens
  • 85Steps. 19 steps, 1 vague phrases
  • 100Tools and files. Tools declared in frontmatter
  • 100Result and completion. Output format and completion criterion are stated
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • low 10 top-level sections: this looks like several domains in one skill
  • 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)
  • -4Absolute local paths (C:\Users, /home/…): not portable
  • -231 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 377: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 19 items
  • +3Output format is stated explicitly
  • +4Has examples (20 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This appears to be a legitimate local OCR skill, but its setup modifies the Python environment and runs downloaded components in ways users should review first.
LLM: suspicious (medium) · 28 May 2026