AD deep-scraper
High-performance containerized web scraper (Docker + Crawlee + Playwright). Use when user mentions any of these: 爬虫, 爬取, 抓取, 采集, 数据采集, 爬数据, 抓数据, 获取数据, scrape, crawl, extract, fetch data, pull data, 亚马逊, Amazon, ASIN, BSR, Best Sellers, 畅销榜, 热销榜, 新品榜, 飙升榜, 排行榜, 选品, 竞品分析, 竞品调研, 市场调研, 品类分析, 类目分析, 产品调研, 月销量, bought in past month, 销量, 评论数, 价格对比, YouTube, 视频字幕, 转录, transcript, 网页内容, 网站数据, 页面抓取, 动态页面, TikTok, Twitter, X, 社交媒体数据, 帖子内容, 关键词搜索, 搜索结果, search results, 产品详情, 产品信息, listing数据, listing分析, top 100, top sellers, 热门产品, 爆款, 跑量款, 价格带, 评分分布, review分析, 评论分析
As a process D 49/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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 · 3
✓ No critical or high findings
Medium and low: 3
-
low Dangerous commands
cmd-privilegeassets/amazon_handler.js:24Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)launchContext: { launchOptions: { headless: true, args: ['--no-sandbox', '--disable-setuid-sandbox'] } },detectorcode literal -
low Dangerous commands
cmd-privilegeassets/main_handler.js:23Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)args: ['--no-sandbox', '--disable-setuid-sandbox'], // Required for Docker
detectorcode literal -
low Dangerous commands
cmd-privilegeassets/youtube_handler.js:10Privilege escalation / world-writable permissions (detector / deny-list definition; string literal in code, not executed)args: ['--no-sandbox', '--disable-setuid-sandbox'],
detectorcode literal
Files scanned: 6. 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 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
- 20When it triggers. No condition that starts the skill
- 40Consistency. Frontmatter name (deep-scraper) differs from the folder (deep-scraper-amazon)
- 100Tools and files. No external tools needed
- 100Steps. 21 steps
- 100Execution cost. Instruction body is 956 tokens
- 100Running it twice. No mutating operations
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 557: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 21 items
- +4Has examples (5 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.