BD 爱图表-智能图表
AI智能图表生成。用户上传数据或粘贴表格，自动生成可视化图表（柱状图、折线图、饼图、词云图、桑基图、地图等40+种）。触发词：创建图表、做图表、可视化数据、用表格生成图表、柱状图、折线图、饼图、词云图、create chart、make a chart、visualize data。
As a process D 46/100 · Unfinished process — 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 asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
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
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Bash Read Write
-
low Secrets in code
secret-password-literalscripts/aitubiao-cli.sh:281Hard-coded password / key literal (may be an example) (placeholder value)echo " API_KEY: ${api_…:12}...${api_key: -4}"placeholder -
low Secrets in code
secret-password-literalscripts/aitubiao-cli.sh:289Hard-coded password / key literal (may be an example) (placeholder value)echo " API_KEY: ${API_…:12}...${API_KEY: -4}"placeholder
Files scanned: 3. 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: Map keys must be unique at line 4, column 1: license: MIT license: MIT ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 46/100
- 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
- 30Running it twice. 6 mutating operations with no state check
- 40Consistency. Frontmatter name (爱图表-智能图表) differs from the folder (aitubiao-ai-chart)
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 93 steps
- 100Execution cost. Instruction body is 3727 tokens
- 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
- -4Absolute local paths (C:\Users, /home/…): not portable
- -223 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 142: enough signal without eating the budget
- +4Structure: 30 headings
- +3Step-by-step instructions: 93 items
- +4Has examples (13 code blocks)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 55.