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BD invoice-ocr

发票 OCR 识别技能。扫描文件夹中的发票文件（PDF/图片），调用翔云 OCR API 识别发票信息。支持 40+ 种发票类型（增值税发票、火车票、出租车票、机票行程单、定额发票、机动车销售发票、过路过桥费发票等）。使用场景：(1) 用户提到"发票识别" (2) 用户需要批量处理发票 (3) 用户提到翔云 OCR 识别发票。**重要：首次使用必须先配置翔云凭证，主动向用户索要 netocr_key 和 netocr_secret，或引导用户运行 --config 命令自行配置。**

ClawHub Agent Skills author: liudengkui v2.0.0 MIT-0 4 files body ≈ 495 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationFinanceInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
Run on models
none yet
Process rating
D
49/100
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Secrets in code secret-high-entropy-token scripts/recognize_invoices.py:68
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    boundary = "----…0gW"
    quoted

Files scanned: 4. 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 49/100

  • 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
  • 40Consistency. Frontmatter name (invoice-ocr) differs from the folder (invoice-ocr-xy)
  • 100Tools and files. No external tools needed
  • 100Steps. 8 steps
  • 100Execution cost. Instruction body is 495 tokens
  • 100Running it twice. No mutating operations

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 244: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 8 items
  • +4Has examples (5 code blocks)
  • +3All 1 scripts are documented

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

External checks

ClawHub: suspicious
This invoice OCR skill is purpose-aligned, but it needs review because it handles sensitive invoices and API secrets with weak disclosure, storage, and dependency-install controls.
LLM: suspicious (high) · VirusTotal: · 29 May 2026