BC ad-insight-hub
面向广告投放与市场分析场景的结构化广告情报数据中枢。在AdMapix原始API之上叠加参数自然语言翻译、 端点依赖编排、结果缓存复用、估算数据可信度A/B/C分级标注四层核心能力。支持广告创意搜索/计数/分布、 应用与开发者画像、商店榜单查询、下载与收入估算（带可信度分级）、参数翻译与端点编排五大能力域. 适用于买量团队竞品创意监控、出海选品调研、广告素材趋势分析、开发者画像与SDK审计、跨地区投放策略制定. 内置40+行业码与200+国家码中文映射，端点依赖图自动并行化无依赖调用、串行化有依赖调用，单轮可编排5-10个端点.
面向广告投放与市场分析场景的结构化广告情报数据中枢。在AdMapix原始API之上叠加参数自然语言翻译、 端点依赖编排、结果缓存复用、估算数据可信度A/B/C分级标注四层核心能力。支持广告创意搜索/计数/分布、 应用与开发者画像、商店榜单查询、下载与收入估算（带可信度分级）、参数翻译与端点编排五大能力域.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 266 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
description-no-whenneither description nor a "## When to Use" section says when to use the skill - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName" - note
frontmatter-keyunknown frontmatter key "summary" - note
frontmatter-keyunknown frontmatter key "summary_zh" - note
frontmatter-keyunknown frontmatter key "tools" - note
frontmatter-keyunknown frontmatter key "homepage"
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. Tools declared in frontmatter
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2850 tokens
- 100Running it twice. No mutating operations
- low 15 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 266: enough signal without eating the budget
- +4Structure: 45 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (6 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.