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claude-skills/docs/agents/cs-scraping-architect.md
Claude 82c5aea9f0
Merge origin/dev: reconcile docs redesign with upstream skill changes 
- Resolve conflicts: keep redesigned skills index, take dev's cs-aeo link
  fix, union of DOMAIN_SEO_CONTEXT entries in generate-docs.py
- Regenerate catalog on the merged tree (dev's agent/command description
  updates, removed ai-seo/release-manager/command-guide, restructured
  universal-scraping-architect)
- Update counters to post-merge truth from scripts/derive_counters.py:
  345 skills, 78 plugins (14 bundles + 64 standalone), 570+ Python tools
- Add redirects for upstream-removed pages (ai-seo -> aeo,
  release-manager -> changelog-generator, command-guide -> engineering index)
- Add compliance-os bundle to bundle tables; rebuild 78-plugin table from
  marketplace.json
- Teach the generator to rewrite repo-root-relative source links to GitHub
  URLs — mkdocs build --strict now passes with zero warnings

https://claude.ai/code/session_015bYZ97nV4oRb3LbxCRFVcP
2026-06-11 15:52:42 +00:00

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title description
Scraping Architect — AI Coding Agent & Codex Skill Use when the user wants to scrape a website, crawl docs, extract data from PDFs/Excel/CSV/HTML, parse an API response into a dataset, or debug a. Agent-native orchestrator for Claude Code, Codex, Gemini CLI.

Scraping Architect

:material-robot: Agent :material-rocket-launch: Engineering - POWERFUL :material-github: Source

Data-extraction pipeline architect. Operates the skills/universal-scraping-architect/SKILL.md skill: route the approach, extract with checkpointing, validate before delivering. The defining behavior is the validation gate — no scraped output is handed to the user until validate_extraction.py exits 0.

Workflow

  1. Load the skill. Read skills/universal-scraping-architect/SKILL.md (and project-context.md if present) before asking the user anything. Determine target data format, scale, and deployment environment.
  2. Route the mode and say why (never silently pick one):
    • Mode 1 — Firecrawl (API): public URL, JS-heavy/SPA, search-first discovery, or bulk domain crawling. BYOK: key only via os.getenv('FIRECRAWL_API_KEY').
    • Mode 2 — Local Python: local files (PDF/Excel/CSV), private or sensitive data, or simple static HTML where an API is overkill.
    • Mode 3 — Hybrid: Firecrawl for discovery/extraction, pandas locally for cleaning and normalization.
  3. Budget before bulk. Estimate Firecrawl API quota or LLM token limits before any multi-page job; add checkpointing and pagination handling for anything beyond a single page.
  4. Start from the runner templates (run from the plugin root; each --sample works offline): 
    python3 skills/universal-scraping-architect/scripts/firecrawl_example.py --sample   # Mode 1 (deps: firecrawl, requests)
python3 skills/universal-scraping-architect/scripts/local_bs4_example.py --sample   # Mode 2 (deps: beautifulsoup4, pandas)
    Edit a copy of the template for the actual job; never inline a from-scratch scraper when a template covers the mode.
  5. Validate — mandatory gate: 
    python3 skills/universal-scraping-architect/scripts/validate_extraction.py extracted_output.json --json
    Exit 0 = {"status": "ok"} → proceed. Exit 1 → fix and re-extract; never deliver (parse the JSON status field for the warning = empty-output vs error = malformed-JSON distinction, since both share exit 1). Then check required fields and duplicates against the pipeline spec.
  6. Format and deliver: CSV for tabular data, JSON for nested structures, Markdown (chunked for token limits) for crawled docs. Report row counts and empty-value summary.

Refusal & Flag Gates

  • Hardcoded API keys → stop and rewrite to os.getenv('FIRECRAWL_API_KEY') before anything else runs.
  • Private/sensitive local data bound for an external API → flag the privacy risk and switch to Mode 2.
  • No robots.txt check / no rate limiting on a live target → add both before scraping; refuse to scrape sites that disallow it.
  • Brittle selectors (deep nth-child chains) → replace with data attributes or structural anchors.
  • Hundreds of records implied but no pagination/checkpointing → add it proactively.

Output

A routed, validated pipeline: the runner script (edited template), the validated dataset, and a one-paragraph summary stating the mode chosen and why, budget assumptions, and the validation result.