AF pangolinfo-ai-serp
Use when: 用户要"Google 搜一下" / "看 AI Overview / SGE 怎么说" / "抓搜索结果带引文来源" / "监控某关键词的 SERP" / "做站外需求/舆情调研" / "Reddit/Quora 上大家怎么吐槽 X" / "我的内容会不会被 AI 搜索引用" / "compare A vs B 的热度趋势" / "search Google for X" / "AI mode 多轮追问". Covers: 程序化抓 Google SERP + AI Overviews(SGE)，两种模式 overview / ai_mode（多轮 followups），可选截图；外加 Google Trends 关键词热度对比（时间序列 + 地区热力 + Breakout 上升词）。给 Agent 做"消除幻觉的实时联网感知层"，引文可回溯。 NOT for: Amazon 站内搜索（用 pangolinfo-amazon-scraper 的 search_amazon）/ 深爬某网站内页（本 skill 只做 Google SERP）/ Amazon 类目利基筛选（用 pangolinfo-amazon-niche）。
As a process F 39/100 · Will not run — References files that are not bundled: url
How to improve
- The text references files that are not there: add them or drop the references.
- 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 · 2
✓ No critical or high findings
Medium and low: 2
-
low Secrets in code
secret-high-entropy-tokenSKILL.md:297High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| `top5…max` | `top5…Max`（products 不是 brands）|
table -
low Secrets in code
secret-high-entropy-tokenSKILL.md:347High-entropy token-like string (may be an id, hash or a credential) (documentation table row)| filter_niches 入参 | 传 `categoryId` | 只认 `nicheId`/`nicheTitle`;0-1 小数字段(`top5…Max`/`retu…Max`)别传整数 |
table
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
missing-refreference to a missing file: url - note
frontmatter-keyunknown frontmatter key "mcp_tools_used" - note
frontmatter-keyunknown frontmatter key "applies_to"
Process rating: all ten parameters 39/100
- 0Tools and files. 1 referenced file(s) missing: url
- 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
- 50When it triggers. No condition that starts the skill
- 100Steps. 75 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3908 tokens
- 100Running it twice. No mutating operations
- low 18 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
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
- +3Output format is not stated: the model decides each time
- -238 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 11 example trigger phrases
- +4Description says when NOT to use the skill
- +3Description length 529: enough signal without eating the budget
- +4Structure: 39 headings
- +3Step-by-step instructions: 75 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.