| name | universal-scraping-architect |
| description | Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts. |
Universal Scraping Architect
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:
- Route the Approach: Explicitly state whether Firecrawl or Local Python is being used and why.
- Track Budgets: Estimate Firecrawl API quotas or LLM token context limits before executing large jobs.
- 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.
- 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.
- 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.
