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Universal Scraping Architect

Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts.

alirezarezvanialirezarezvani/universal-scraping-architect★ 28k更新于 2026年8月26日

说明

Design complete, robust data-extraction pipelines with intelligent routing, validation, and token-budget tracking — not brittle one-off scripts.

Dependency Notice: BYOK (Bring Your Own Key) pattern for Firecrawl; API keys must only be loaded via environment variables. Per-script dependencies:

Script Dependencies Exact CLI
scripts/validate_extraction.py stdlib only python3 scripts/validate_extraction.py output.json --json
scripts/firecrawl_example.py firecrawl, requests (template; --sample runs offline) python3 scripts/firecrawl_example.py --sample
scripts/local_bs4_example.py beautifulsoup4, pandas (template; --sample runs offline) python3 scripts/local_bs4_example.py --sample

Before Starting

Check for context first: If project-context.md exists, read it before asking questions. Determine the target data format, scale of extraction, and deployment environment before writing any code.

How This Skill Works

This skill supports 3 extraction modes based on intelligent routing:

Mode 1: API-Driven (Firecrawl)

Use when the source is a public URL, heavily dynamic (JS/SPA), requires search-first discovery, or involves bulk crawling across a domain.

Mode 2: Local Python (Traditional)

Use when extracting from local files (PDF, Excel, CSV), the data is private/sensitive, or the target is a simple static HTML page where Firecrawl is overkill.

Mode 3: Hybrid Pipeline

Use when Firecrawl handles URL discovery/web extraction, but local Python (Pandas) is required to clean, normalize, and structure the output before saving.

The Extraction Pipeline

When executing a scraping task, always follow this sequence:

  1. Route the Approach: Explicitly state whether Firecrawl or Local Python is being used and why.
  2. Track Budgets: Estimate Firecrawl API quotas or LLM token context limits before executing large jobs.
  3. Extract Safely: Implement checkpointing for multi-page jobs. Handle pagination and dynamic layouts gracefully. Start from the editable runner templates — scripts/firecrawl_example.py (Mode 1) or scripts/local_bs4_example.py (Mode 2); run each with --sample first to see the expected summary shape without network access.
  4. Validate & Clean: Run python3 scripts/validate_extraction.py extracted_output.json --json on every extraction result before delivering it. It exits 0 only on {"status": "ok"}; warning (empty output) or error (malformed JSON) exit 1 — fix and re-extract, never ship unvalidated data. Beyond this structural gate, also check required fields and duplicates against the pipeline spec before delivering.
  5. Format: Default to CSV for tabular data, JSON for nested structures, and Markdown for clean text.

Proactive Triggers

Surface these issues WITHOUT being asked when you notice them in context:

  • Hardcoded API Keys → Flag immediately and rewrite to use os.getenv('FIRECRAWL_API_KEY').
  • Private Data Leakage → If the user asks to send local, sensitive files to an external API, flag the privacy risk and suggest Mode 2 (Local Python).
  • Missing Pagination → If the target implies hundreds of records but no pagination logic is requested, flag it and add checkpointing.

Output Artifacts

When you ask for... You get...
"Scrape this site" A fully validated Python extraction script with routing logic and error handling.
"Get data from this table" A clean CSV/JSON dataset with a summary log of row counts and empty values.
"Crawl these docs" A Markdown deliverable chunked for LLM token limits.

Anti-Patterns

  • Brittle Selectors: Never use highly nested CSS selectors (e.g., div > span > ul > li:nth-child(3)). Use data attributes or robust structural anchors.
  • Ignoring Etiquette: Never scrape without checking robots.txt or implementing sensible rate limits.
  • No Validation: Never blindly write scraped data to a file without checking if the array is empty or missing critical keys.

Related Skills

  • data-cleaning: Use when the scraped data requires complex statistical normalization or deduplication.
  • browser-automation: Use for highly interactive scraping requiring user emulation (clicks, logins) where Firecrawl is insufficient.

权限

声明检测
运行代码—python
安装—pip
安装时运行脚本—无
网络—aws.amazon.comdatatracker.ietf.orgdeveloper.mozilla.orgdevelopers.cloudflare.comdocs.firecrawl.devdocs.python.orggithub.comhtml.spec.whatwg.orglearn.microsoft.comowasp.orgpandas.pydata.orgrequests.readthedocs.iowww.cloudflare.comwww.crummy.comwww.ethicalwebscraping.orgwww.lexology.com
需要的凭据—FIRECRAWL_API_KEY
工作区外的路径—无
智能体工具—无

检查

低风险 · 没有发现需要提醒的地方。

未经人工审核 · 已做规则检查;模型审核尚未开启。

文件8 个文件 · 31.4 KB

  • SKILL.md4.5 KB
  • requirements.txt300 B
references/3
  • firecrawl-technical-guide.md2.6 KB
  • local-extraction-patterns.md2.5 KB
  • scraping-ethics-security.md2.5 KB
scripts/3
  • firecrawl_example.py7.8 KB
  • local_bs4_example.py9.8 KB
  • validate_extraction.py1.3 KB

版本

  1. #1—最新2026年10月7日