BC invoice-ocr
增值税发票识别技能：自动识别 PDF（单页/多页）或各种常见图片格式（PNG/JPG等）的发票，调用百度云增值税发票 OCR API 提取关键信息，输出结构化 Excel 报告。适用于以下场景： 用户上传发票文件并要求识别、提取、转换信息时；需要批量处理发票并生成 Excel 汇总表时； 需要对发票进行检测、内容识别和自动质量评估时；提及"发票识别"、"增值税发票"、"OCR识别"、 "invoice recognition"、"百度OCR" 等关键词时。 即使用户只是简单指示"帮我识别这张发票"，也应触发本技能。 输出包含三个核心处理流：发票预检测、结构化字段提取、质量评分评估，最终落地为直观的 Excel 文件。
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.
The files contain someone else's key or token. If it is live, your agent will call third-party services under a stranger's identity; if it was revoked, the skill's scripts simply fail. Such a key often arrives with the author's whole workspace, personal data included.
The key is visible to everyone who downloaded the skill and has likely been copied by catalog-scanning bots already. Revoke it now, check bills and access logs, then reissue.
How to improve
- Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
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high Secrets in code
meta-credential-filesscripts/.envCredential / dotenv files bundled with the skill (1)scripts/.env
Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 53/100
- 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
- 100Tools and files. No external tools needed
- 100Steps. 26 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 565 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
- +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
- +5Description quotes 2 example trigger phrases
- +3Description length 312: enough signal without eating the budget
- +4Structure: 13 headings
- +3Step-by-step instructions: 26 items
- +4Has examples (1 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.