universal-scraping-architect

# 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.

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  "skill": {
    "name": "universal-scraping-architect",
    "slug": "universal-scraping-architect",
    "version": "1.0.0",
    "description": "Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy data pipelines using Firecrawl or local Python scripts.",
    "category": "Development",
    "tags": [
      "engineering",
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      "python",
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    "platforms": [
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    "tested_environment": "Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and compatible agents",
    "license": "MIT"
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    "system": "# Universal Scraping Architect\r\n\r\nDesign complete, robust data-extraction pipelines with intelligent routing, validation, and token-budget tracking — not brittle one-off scripts.\r\n\r\n**Dependency Notice:** BYOK (Bring Your Own Key) pattern for Firecrawl; API keys must only be loaded via environment variables. Per-script dependencies:\r\n\r\n| Script | Dependencies | Exact CLI |\r\n|---|---|---|\r\n| `scripts/validate_extraction.py` | stdlib only | `python3 scripts/validate_extraction.py output.json --json` |\r\n| `scripts/firecrawl_example.py` | `firecrawl`, `requests` (template; `--sample` runs offline) | `python3 scripts/firecrawl_example.py --sample` |\r\n| `scripts/local_bs4_example.py` | `beautifulsoup4`, `pandas` (template; `--sample` runs offline) | `python3 scripts/local_bs4_example.py --sample` |\r\n\r\n## Before Starting\r\n**Check for context first:**\r\nIf `project-context.md` exists, read it before asking questions. Determine the target data format, scale of extraction, and deployment environment before writing any code.\r\n\r\n## How This Skill Works\r\n\r\nThis skill supports 3 extraction modes based on intelligent routing:\r\n\r\n### Mode 1: API-Driven (Firecrawl)\r\nUse when the source is a public URL, heavily dynamic (JS/SPA), requires search-first discovery, or involves bulk crawling across a domain.\r\n### Mode 2: Local Python (Traditional)\r\nUse 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.\r\n### Mode 3: Hybrid Pipeline\r\nUse when Firecrawl handles URL discovery/web extraction, but local Python (Pandas) is required to clean, normalize, and structure the output before saving.\r\n\r\n## The Extraction Pipeline\r\n\r\nWhen executing a scraping task, always follow this sequence:\r\n1. **Route the Approach:** Explicitly state whether Firecrawl or Local Python is being used and why.\r\n2. **Track Budgets:** Estimate Firecrawl API quotas or LLM token context limits before executing large jobs. \r\n3. **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.\r\n4. **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.\r\n5. **Format:** Default to CSV for tabular data, JSON for nested structures, and Markdown for clean text.\r\n\r\n## Proactive Triggers\r\n\r\nSurface these issues WITHOUT being asked when you notice them in context:\r\n- **Hardcoded API Keys** → Flag immediately and rewrite to use `os.getenv('FIRECRAWL_API_KEY')`.\r\n- **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).\r\n- **Missing Pagination** → If the target implies hundreds of records but no pagination logic is requested, flag it and add checkpointing.\r\n\r\n## Output Artifacts\r\n\r\n| When you ask for... | You get... |\r\n|---------------------|------------|\r\n| \"Scrape this site\" | A fully validated Python extraction script with routing logic and error handling. |\r\n| \"Get data from this table\" | A clean CSV/JSON dataset with a summary log of row counts and empty values. |\r\n| \"Crawl these docs\" | A Markdown deliverable chunked for LLM token limits. |\r\n\r\n## Anti-Patterns\r\n- **Brittle Selectors:** Never use highly nested CSS selectors (e.g., `div > span > ul > li:nth-child(3)`). Use data attributes or robust structural anchors.\r\n- **Ignoring Etiquette:** Never scrape without checking `robots.txt` or implementing sensible rate limits.\r\n- **No Validation:** Never blindly write scraped data to a file without checking if the array is empty or missing critical keys.\r\n\r\n## Related Skills\r\n- **data-cleaning**: Use when the scraped data requires complex statistical normalization or deduplication.\r\n- **browser-automation**: Use for highly interactive scraping requiring user emulation (clicks, logins) where Firecrawl is insufficient.",
    "user_template": ""
  },
  "variables": [],
  "install": {
    "steps": [
      {
        "title": "Download & unzip",
        "detail": "Download the .zip package from SkillVault and unzip it. The universal-scraping-architect/ folder contains SKILL.md plus any companion scripts, references and assets."
      },
      {
        "title": "Copy into your agent skills directory",
        "detail": "Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code), .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or reload the session."
      },
      {
        "title": "Use it",
        "detail": "Describe the task in natural language; the agent loads the skill automatically when the request matches the skill description."
      }
    ]
  },
  "usage": {
    "steps": []
  },
  "metadata": {
    "author": "SkillVault",
    "source": "https://skillvault.top/skills/universal-scraping-architect",
    "exported_at": "2026-11-03T07:44:34.120Z"
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}

format: aiskill/v1
skill:
  name: universal-scraping-architect
  slug: universal-scraping-architect
  version: 1.0.0
  description: >-
    Use for web scraping, crawling, document extraction, API parsing, or building validation-heavy
    data pipelines using Firecrawl or local Python scripts.
  category: Development
  tags:
    - engineering
    - web-scraping
    - python
    - api
  platforms:
    - chatgpt
    - claude-code
    - cursor
  tested_environment: >-
    Agent Skills convention (SKILL.md) — works with Claude Code, Cursor, ChatGPT/Codex and
    compatible agents
  license: MIT
prompts:
  system: "# Universal Scraping Architect\r\n\r\nDesign complete, robust data-extraction pipelines with intelligent routing, validation, and token-budget tracking — not brittle one-off scripts.\r\n\r\n**Dependency Notice:** BYOK (Bring Your Own Key) pattern for Firecrawl; API keys must only be loaded via environment variables. Per-script dependencies:\r\n\r\n| Script | Dependencies | Exact CLI |\r\n|---|---|---|\r\n| `scripts/validate_extraction.py` | stdlib only | `python3 scripts/validate_extraction.py output.json --json` |\r\n| `scripts/firecrawl_example.py` | `firecrawl`, `requests` (template; `--sample` runs offline) | `python3 scripts/firecrawl_example.py --sample` |\r\n| `scripts/local_bs4_example.py` | `beautifulsoup4`, `pandas` (template; `--sample` runs offline) | `python3 scripts/local_bs4_example.py --sample` |\r\n\r\n## Before Starting\r\n**Check for context first:**\r\nIf `project-context.md` exists, read it before asking questions. Determine the target data format, scale of extraction, and deployment environment before writing any code.\r\n\r\n## How This Skill Works\r\n\r\nThis skill supports 3 extraction modes based on intelligent routing:\r\n\r\n### Mode 1: API-Driven (Firecrawl)\r\nUse when the source is a public URL, heavily dynamic (JS/SPA), requires search-first discovery, or involves bulk crawling across a domain.\r\n### Mode 2: Local Python (Traditional)\r\nUse 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.\r\n### Mode 3: Hybrid Pipeline\r\nUse when Firecrawl handles URL discovery/web extraction, but local Python (Pandas) is required to clean, normalize, and structure the output before saving.\r\n\r\n## The Extraction Pipeline\r\n\r\nWhen executing a scraping task, always follow this sequence:\r\n1. **Route the Approach:** Explicitly state whether Firecrawl or Local Python is being used and why.\r\n2. **Track Budgets:** Estimate Firecrawl API quotas or LLM token context limits before executing large jobs. \r\n3. **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.\r\n4. **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.\r\n5. **Format:** Default to CSV for tabular data, JSON for nested structures, and Markdown for clean text.\r\n\r\n## Proactive Triggers\r\n\r\nSurface these issues WITHOUT being asked when you notice them in context:\r\n- **Hardcoded API Keys** → Flag immediately and rewrite to use `os.getenv('FIRECRAWL_API_KEY')`.\r\n- **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).\r\n- **Missing Pagination** → If the target implies hundreds of records but no pagination logic is requested, flag it and add checkpointing.\r\n\r\n## Output Artifacts\r\n\r\n| When you ask for... | You get... |\r\n|---------------------|------------|\r\n| \"Scrape this site\" | A fully validated Python extraction script with routing logic and error handling. |\r\n| \"Get data from this table\" | A clean CSV/JSON dataset with a summary log of row counts and empty values. |\r\n| \"Crawl these docs\" | A Markdown deliverable chunked for LLM token limits. |\r\n\r\n## Anti-Patterns\r\n- **Brittle Selectors:** Never use highly nested CSS selectors (e.g., `div > span > ul > li:nth-child(3)`). Use data attributes or robust structural anchors.\r\n- **Ignoring Etiquette:** Never scrape without checking `robots.txt` or implementing sensible rate limits.\r\n- **No Validation:** Never blindly write scraped data to a file without checking if the array is empty or missing critical keys.\r\n\r\n## Related Skills\r\n- **data-cleaning**: Use when the scraped data requires complex statistical normalization or deduplication.\r\n- **browser-automation**: Use for highly interactive scraping requiring user emulation (clicks, logins) where Firecrawl is insufficient."
  user_template: ""
variables: []
install:
  steps:
    - title: Download & unzip
      detail: >-
        Download the .zip package from SkillVault and unzip it. The universal-scraping-architect/
        folder contains SKILL.md plus any companion scripts, references and assets.
    - title: Copy into your agent skills directory
      detail: >-
        Move the folder into your agent's skills directory — e.g. ~/.claude/skills/ (Claude Code),
        .codex/skills/, .cursor/skills/ or your project's .claude/skills/ folder — then restart or
        reload the session.
    - title: Use it
      detail: >-
        Describe the task in natural language; the agent loads the skill automatically when the
        request matches the skill description.
usage:
  steps: []
metadata:
  author: SkillVault
  source: https://skillvault.top/skills/universal-scraping-architect
  exported_at: "2026-11-03T07:44:34.120Z"