AD network-request-twister
当用户想要观察、拦截或修改浏览器网络请求和响应时使用此 skill。Use when the user wants to observe, intercept, or modify browser network requests — monitor HTTP traffic, mock API responses, block analytics/tracking, modify request/response headers, rewrite response bodies, inject content into pages, or test web behavior under modified network conditions. Trigger even when the user doesn't use precise technical terms — 「帮我看这个网站发了什么请求」「把这个 API 的返回值改成假的」「屏蔽谷歌统计」「让页面显示不同数据」are all valid triggers. Keywords: 拦截 intercept mock 修改请求 修改响应 改包 抓包 network monitor CDP fake backend 替换返回值 注入 inject script 改 header 阻止 block request.
当用户想要观察、拦截或修改浏览器网络请求和响应时使用此 skill。Use when the user wants to observe, intercept, or modify browser network requests — monitor HTTP traffic, mock API…
As a process D 49/100 · Unfinished process — 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: 0. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "displayName"
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 10 mutating operations with no state check
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 100Steps. 12 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1455 tokens
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
- +1No license
- +2Single-language instructions
- +3Description length 594: enough signal without eating the budget
- +4Structure: 12 headings
- +3Step-by-step instructions: 12 items
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (2 of 2)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.