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# 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: 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.