AC travel-planner-hy
智能旅行规划技能 — 多城市/多日游行程编排、景点检索、预算匹配、完整方案输出（每日详情/预算明细/住宿推荐/交通方案/物品清单/安全应急）。能力与副作用完整披露：(1) 通过 WebSearch/WebFetch 向公开景点官网、攻略站、地图服务发起出站 HTTP 请求获取公开信息；(2) 可选读取本地环境变量 AMAP_API_KEY（高德路线/天气，可选，未配置则跳过该部分）；不读取 TUNIU/Fliggy/其他第三方 API Key，不调用其预订/下单接口；(3) 本地生成行程 HTML 与二维码（qrcode 库），写入技能同级 output/ 目录；(4) 生成的二维码与"预订/支付"按钮仅指向第三方平台公开页面（如途牛待付款页），**仅作跳转，本技能不创建订单、不提交预订、不处理支付、不代付**；用户须在第三方平台自行完成预订与支付；(5) 不收集、不上传用户姓名/身份证号/手机号/银行账号等 PII，任何 PII 由用户直接向第三方平台提供。触发词：旅行规划、行程规划、旅游攻略、行程编排、travel-plan-hy。Use when the user asks for 旅行规划、行程安排、旅游攻略、每日行程编排、景点推荐、travel-plan-hy。
智能旅行规划技能 — 多城市/多日游行程编排、景点检索、预算匹配、完整方案输出（每日详情/预算明细/住宿推荐/交通方案/物品清单/安全应急）。能力与副作用完整披露：(1) 通过 WebSearch/WebFetch 向公开景点官网、攻略站、地图服务发起出站 HTTP 请求获取公开信息；(2) 可选读取本地环境变量…
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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
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 (web) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 55 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1936 tokens
- 100Running it twice. No mutating operations
- low 10 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
- -212 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +3Description length 544: enough signal without eating the budget
- +4Structure: 33 headings
- +3Step-by-step instructions: 55 items
- +4Has examples (11 code blocks)
- +4Reference files are cited in the instructions (6 of 6)
- +3All 3 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.