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AD exam-ocr-rebuilder

Exam paper OCR rebuilder with LLM audit. Supports 11 question types, tencent-docs OCR integration, and interactive HTML report generation. Triggers: 试卷OCR, 试卷识别, 试卷重建, 试卷录入, PDF试卷, exam OCR, exam paper rebuild

ClawHub Claude Code author: bettermen v1.0.2 MIT-0 11 files body ≈ 1 646 tokens Open the sourceclawhub.ai↗ analyzed 34 h ago

Exam paper OCR rebuilder with LLM audit.

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerLearningAI and agentsPersonal productivitytype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
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: 11. 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")
  • note frontmatter-key unknown frontmatter key "description_zh"

Process rating: all ten parameters 43/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 82 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1646 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 209: enough signal without eating the budget
  • +4Structure: 38 headings
  • +3Step-by-step instructions: 82 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (3 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: clean
This skill appears to perform its stated exam OCR and report-generation workflow, with privacy considerations for external OCR and LLM processing.
LLM: benign (high) · VirusTotal: · 30 Jun 2026