BC playwright-browser-use
浏览器自动化 CLI（Playwright 版，纯 Node.js 实现）。除常规自动化（打开网页/截图/点击/填表/翻页）外，提供三类能力：(1) 会话凭证读写原语 —— `cookies` / `storage` 命令可**无需代码执行**即列出/导出/导入/清除/设置 cookie 与 localStorage，直接提取或注入登录态与会话令牌（此路径独立于代码执行；自 v1.3.2 起 `PW_BROWSER_SAFE_MODE=1` 会将其与代码执行一并禁用）；(2) `eval` 在页面上下文执行任意 JavaScript（可读 cookie/存储、发起带凭证请求）；(3) `run-code` 在守护进程上下文执行 Playwright/Node 代码（vm 沙箱隔离）。全部经持久化本地守护进程（127.0.0.1:19223，浏览器状态跨命令保持）控制，受随机 token 认证保护；`PW_BROWSER_SAFE_MODE=1` 可彻底禁用代码执行与 cookies/storage 凭证读写（v1.3.2+）。仅在可信、用户可见的本地环境中授权使用；会话凭证落盘须遵循后文安全警告。
浏览器自动化 CLI（Playwright 版，纯 Node.js 实现）。除常规自动化（打开网页/截图/点击/填表/翻页）外，提供三类能力：(1) 会话凭证读写原语 —— cookies / storage 命令可无需代码执行即列出/导出/导入/清除/设置 cookie 与…
As a process C 52/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bash(curl:*)allowed-tools: Bash(node:*) Bash(pw-browser:*) Bash(curl:*)
Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when") - warning
body-longSKILL.md body ≈ 5726 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "capabilities" - note
frontmatter-keyunknown frontmatter key "permissions" - note
frontmatter-keyunknown frontmatter key "disable"
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
- 20When it triggers. No condition that starts the skill
- 70Execution cost. Instruction body is 5726 tokens
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 58 steps
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 20 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (24 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
- +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
- -232 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 506: enough signal without eating the budget
- +4Structure: 51 headings
- +3Step-by-step instructions: 58 items
- +4Has examples (21 code blocks)
- +4Reference files are cited in the instructions (3 of 3)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.