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BD baidu-doc-vlm-parser 百度文档解析(PaddleOCR-VL)

调用百度PaddleOCR-VL大模型API解析文档。基于PaddleOCR-VL-1.6多模态大模型，支持PDF、Word、PPT、图片等格式，精准识别印刷文本、手写文本、表格、公式、图表、印章等复杂元素，支持100+种语言，可处理不规则布局和长文档跨页解析。触发词：文档解析、VLM解析、大模型OCR、PaddleOCR、多模态文档、手写识别、公式识别、复杂版面。

ClawHub Agent Skills author: Maglanyulan v1.0.4 MIT-0 7 files body ≈ 2 411 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

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

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
89/100
safety, quality, tests
Safety 60%
98
Quality 40%
75
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token scripts/baidu_doc_vlm_parser.py:214
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    result = client.query_task("task…XaJ")
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:132
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "result": { "task_id": "task…01S" }
    quoted

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (baidu-doc-vlm-parser 百度文档解析(PaddleOCR-VL)) differs from the folder (baidu-doc-vlm-parser)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 40 steps
  • 100Execution cost. Instruction body is 2411 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • low 18 top-level sections: this looks like several domains in one skill

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
  • +2Single-language instructions
  • +3Description length 184: enough signal without eating the budget
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 40 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)
  • +3All 1 scripts are documented
  • +1License stated

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

External checks

ClawHub: suspicious
The skill is a Baidu cloud document parser, but its shipped CLI ignores user-supplied files and queries a hard-coded Baidu task while using user credentials.
LLM: suspicious (high) · VirusTotal: · 10 Jul 2026