BC ai-industry-infographic
Generates complete ChatGPT-ready prompt packages for AI industry infographics. This skill should be used when the user needs to create data-rich information graphics about AI industry topics (LLM landscape, embodied AI, chip supply chain, computing costs, industry history, etc.), especially following large industry events. It covers the full workflow: topic design, parallel web research, data verification (web-verify-protocol), structured prompt writing, social media copy. Productized output: 生图素材包（选题+核实数据+Prompt+文案），可独立售卖或订阅. Triggers: 做信息图, 生图素材包, 生成Prompt, AI产业链图, 行业信息图, WAIC信息图, 做个选题的生图包.
Generates complete ChatGPT-ready prompt packages for AI industry infographics.
As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
How to improve
- 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 · 0
✓ No critical or high findings
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: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Generates complete ChatGPT-ready prompt packages for AI industry i… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 52/100
- 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
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 70Execution cost. Instruction body is 4889 tokens
- 85Steps. 94 steps, 1 vague phrases
- 100When it triggers. States when to use and when not to
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- high The skill tells the model to perform an irreversible action with no human approval
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
- -223 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 599: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 94 items
- +4Has examples (6 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.