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Pdf Vision

v1.0.0

Extract text content from image-based/scanned PDFs using multiple vision APIs with automatic fallback. Supports Xflow (qwen3-vl-plus) and ZhipuAI (GLM-4.6V-F...

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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
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Purpose & Capability
The core files and SKILL.md align with the stated purpose: converting PDF pages to images and calling vision-capable models (Xflow / ZhipuAI). However, the repository also includes scripts unrelated to PDF extraction (scripts/create_github_repo.py) which try to locate/use a GITHUB_TOKEN (including by parsing ~/.bashrc) and instruct the user about pushing code to GitHub. That GitHub-oriented functionality is not described in the skill metadata or SKILL.md and is unnecessary for PDF extraction.
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Instruction Scope
SKILL.md and the main scripts only instruct reading your OpenClaw config (~/.openclaw/openclaw.json), converting PDFs to images (pypdfium2), and calling configured model endpoints—this is appropriate. But create_github_repo.py attempts to read environment variables and fallback to parsing ~/.bashrc to find a GITHUB_TOKEN, which is outside the documented scope. test_skill.sh also references a user-specific file path (/home/lpq/.openclaw/workspace/林佩权课表.pdf), indicating leftover developer-specific test artifacts. These extras expand the runtime surface beyond what the skill promises.
Install Mechanism
There is no install specification (instruction-only skill) and no remote download or archive extraction. The package contains local Python and shell scripts only. No network-installed code is fetched at install time by the skill itself.
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Credentials
The skill legitimately reads API keys from ~/.openclaw/openclaw.json for the vision providers, which is proportional. However, create_github_repo.py looks for GITHUB_TOKEN (env or inside ~/.bashrc) without that credential being declared or documented in SKILL.md. That is unexpected for an OCR skill and could lead to accidental use of a shell-stored token if the helper script is executed.
Persistence & Privilege
The skill does not request always:true and does not modify other skills or system-wide settings. The scripts create temporary files under /tmp (documented) and otherwise run on demand. There is no autonomous persistence or privilege escalation requested.
What to consider before installing
This skill's main OCR functionality appears coherent and reasonable: it converts PDF pages to images, reads your OpenClaw config for provider baseUrls/apiKeys, and posts image data to those endpoints. However, two red flags should be addressed before installing or running it: 1) Unrelated GitHub helper script: scripts/create_github_repo.py is unrelated to PDF extraction and will attempt to find and use a GITHUB_TOKEN (from the environment or by parsing ~/.bashrc) to call the GitHub API. Only run that script if you understand and trust it; otherwise remove or ignore it. Storing tokens in shell RC files is risky—prefer a dedicated credential store. 2) Residual test artifacts: test_skill.sh contains a hardcoded user-specific PDF path (author-local); review and update or delete it to avoid accidental execution that references your filesystem. Recommended actions before installation: - Inspect and (if not needed) delete or move scripts/create_github_repo.py from the skill directory. - Search the skill for any other helper scripts that access credentials or user files and remove or sandbox them. - Run the main extraction script in a sandboxed environment (isolated user account or container) first and verify it only reads ~/.openclaw/openclaw.json and /tmp files. - Ensure your OpenClaw config stores only the credentials you intend to use and is not world-readable. If the repository owner clarifies that the GitHub script is intentionally included (e.g., convenience for packaging) and documents it in SKILL.md, and if you plan to use it only in a controlled way, this lowers the concern. If you cannot get that clarification, treat the extra scripts as suspicious and remove them before use.

Like a lobster shell, security has layers — review code before you run it.

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License

MIT-0
Free to use, modify, and redistribute. No attribution required.

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