BC web-crawler
Use this skill whenever the user wants to create Python web crawlers/scrapers based on a given URL. Supports Vue, React, Next.js, Nuxt, Angular, Svelte and other frontend architectures by analyzing API calls, handling dynamic content with headless browsers. Main language is Python. Features: - Static & dynamic crawling (requests, httpx, playwright, selenium, drissionpage) - JS reverse engineering (webpack unpacking, AST analysis, hook injection) - Anti-bot bypass (Cloudflare, Akamai, reCAPTCHA, fingerprint spoofing) - Login & session management (cookie, JWT, OAuth, signature replay) - Encryption & signing (AES/RSA/MD5/SHA, custom signing algorithms) - Pagination strategies (page number, cursor, waterfall, infinite scroll) - Data export (CSV, XLSX, JSON, SQLite, MySQL, MongoDB, images, files) - Distributed crawling (Redis queue, Scrapy-Redis, task scheduling) - Anti-detection (UA rotation, proxy pool, fingerprint randomization, behavior simulation) - Compliance (robots.txt, rate limiting, terms of service, legal boundaries) Triggers: "写爬虫", "爬取", "抓取数据", "scraper", "crawler", "scrape", "spider", "Vue 爬虫", "React 爬虫", "动态网页", "反爬", "加密参数", "签名"
Supports Vue, React, Next.js, Nuxt, Angular, Svelte and other frontend architectures by analyzing API calls, handling dynamic content with headless browsers.…
As a process C 62/100 · Has gaps — weak spots: inputs and preconditions, progress reporting
How to improve
- Shorten the description to 1024 characters.
- 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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1191 chars, limit 1024 - warning
body-longSKILL.md body ≈ 5163 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 62/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 50Failures and branches. 0 branches, has a failure section
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5163 tokens
- 85Steps. 80 steps, 2 vague phrases
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- low 13 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
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1190: 120–800 characters recommended
- -242 emoji in the instructions: noise for the model
- +1No license
- +2Single-language instructions
- +5Description quotes 6 example trigger phrases
- +4Structure: 47 headings
- +3Step-by-step instructions: 80 items
- +3Output format is stated explicitly
- +4Has examples (17 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 52.