---
name: llm-iterative-scrape
description: "Iterative compliant scraping workflow: plan -> bounded browser step -> evaluate -> refine until goal satisfied or safety/budget stops."
---
# llm-iterative-scrape

Use this skill for iterative, goal-driven scraping on authorized public or user-owned sites.

Loop:
1) Translate the natural-language goal into a minimal set of CSS selectors and a short plan for ONE bounded browser step.
2) Execute exactly one small browser step with a polite wait/rate limit via the host agent's primitives.
3) Evaluate observations vs. the goal. Refine minimally. Stop when satisfied or when any safety/budget condition triggers.

Hard safety constraints:
- Never solve or route around CAPTCHAs/anti-bot.
- Never log in or pass paywalls.
- Restrict pagination to same-origin as the start URL by default; also respect provided allowed_domains.
- When `respect_robots` is true, treat any robots.txt disallow or failure-to-check as a stop condition.

Selectors guidance:
- Use compact CSS selectors. Suffix conventions: `::text`, `::html`, `::attr(name)`.
- Use one pagination field: `pagination_next` or `next` for the next-page `href`.
- Prefer domain-agnostic patterns like `a[rel='next']::attr(href)` or `li.next a::attr(href)`.

Planning output contract:
Return strictly compact JSON only: {"plan_summary": str, "selectors": {str:str}, "continue": bool, "notes": str}. Keep under ~600 chars when possible.

Evaluation guidance:
- If extracted data matches the requested fields and count threshold, set continue=false and summarize.
- If pagination is needed for more pages and a next link exists, keep continue=true.
- On any safety trigger (captcha/login/paywall/robots), set continue=false and summarize the stop reason.