BC qingping-ai-skill
青萍 AI 图片生成工具。通过 API 生成高质量图片并自动下载到本地。使用场景：(1) 用户需要生成 AI 图片，(2) 提到"青萍"、"qingping"、"生成图片"、"AI生图"等关键词，(3) 需要快速生成设计素材或创意图片。支持多种模型、尺寸和比例配置，默认生成 16:9 比例图片。
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.
Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.
How to improve
- 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 · 3
✓ No critical or high findings
Medium and low: 3
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medium Dangerous commands
cmd-shell-rcREADME.md:26Writes to a shell startup fileecho 'export QINGPING_API_KEY="your-api-key-here"' >> ~/.zshrc
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medium Dangerous commands
cmd-shell-rcSKILL.md:34Writes to a shell startup fileecho 'export QINGPING_API_KEY="your-api-key-here"' >> ~/.zshrc
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low Dangerous commands
cmd-shell-rcscripts/generate_image.py:94Writes to a shell startup file (string literal in code, not executed)print(" echo 'export QINGPING_API_KEY=\"your-api-key-here\"' >> ~/.zshrc")code literal
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Unexpected scalar at node end at line 2, column 80: …片并自动下载到本地。使用场景：(1) 用户需要生成 AI 图片，(2) 提到"青萍"、"qingping"、"生成图片"、"AI生图"等关键词，(3) 需要… ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - note
frontmatter-keyunknown frontmatter key "homepage" - note
frontmatter-keyunknown frontmatter key "repository" - note
frontmatter-keyunknown frontmatter key "authors" - note
frontmatter-keyunknown frontmatter key "requirements"
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. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 717 tokens
- 100Running it twice. No mutating operations
- low 11 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 148: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 23 items
- +4Has examples (4 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 63.