Ledger Image Entry
v1.0.0通过图片识别 receipt、订单或发票内容,提取商家、日期、金额和商品明细, 并转换为可入账的结构化记录。
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by@shing19
MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (receipt→structured ledger entries) align with the instructions: extract merchant, date, amount, item details, classify and format records. No unrelated credentials, binaries, or config paths are requested.
Instruction Scope
SKILL.md stays on task: it describes how to extract fields from images, how to handle missing dates (use system date command), per-item record creation, classification rules, and user confirmation for unclear OCR. It does not instruct reading arbitrary files, harvesting unrelated environment variables, or sending data to unexpected external endpoints. The only system interaction is using date "+%Y-%m-%d" to fill a missing date, which is proportional to the purpose.
Install Mechanism
Instruction-only skill with no install spec and no code files—nothing is written to disk or downloaded. This is the lowest-risk install model and appropriate for the described functionality.
Credentials
No environment variables, credentials, or config paths are required. Requested access is minimal and matches the stated task. The SKILL.md references the platform image model (yunwu/gpt-5-mini) for image processing, which is expected for an OCR-like skill.
Persistence & Privilege
Skill is not marked always:true and does not request elevated or persistent system privileges. It is user-invocable and follows the platform default for model invocation; nothing indicates modification of other skills or global agent settings.
Assessment
This skill appears coherent for turning receipt images into ledger rows, but consider these practical points before installing:
- Privacy: receipt images can contain personal and financial data. Confirm how the platform's image model (yunwu/gpt-5-mini) processes and stores images (do they leave your environment, how long are they retained?).
- Accuracy: OCR and classification can be imperfect—review parsed records (amounts, dates, categories) before posting to your accounts. The skill defaults a missing date to the current system date; verify that behavior matches your bookkeeping rules.
- Confirm expectations for ambiguous items: the skill asks to prompt the user when uncertain; ensure your agent is configured to do that rather than auto-accepting uncertain data.
- Minimal system use: it runs a date command when the image lacks a date—this is harmless but note it invokes a simple system command.
- Testing: run the skill on representative sample receipts to confirm category mappings, currency formatting, and multi-item handling meet your needs.
If any of the above is a concern (especially data residency of image processing), seek clarification from the skill author or avoid sending sensitive receipts until you confirm handling policies.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.
