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BytesAgain β€Ί Skills β€Ί LiteBrowse
πŸ¦€ ClawHub

LiteBrowse

by @agitalent

Extracts and summarizes the most relevant webpage passages for focused, low-token research without loading or summarizing the full page.

Versionv0.1.1
Downloads365
#crypto-defi#research#legal#productivity
TERMINAL
clawhub install litebrowse

πŸ“– About This Skill

LiteBrowse Skill

Direct access:

  • https://agitalent.github.io/LiteBrowse.md
  • https://github.com/agitalent/agitalent.github.io

    • Purpose

    LiteBrowse is an OpenClaw skill for low-token webpage research.

    Use it when:

  • the user wants facts from a specific webpage
  • the page is long or cluttered
  • token cost matters
  • you need the most relevant passages first instead of full-page dumps

    • Core Rule

    Do not load or summarize the full page first.

    Always run the local extractor before reasoning on webpage content:

    python3 ./scripts/web_relevance_extract.py "" ""

    The extractor returns only the most relevant blocks under a fixed character budget. Use that compact output as the default context for answering.

    Required Workflow

    1. Restate the information target as a short query string. 2. Run: 

       python3 ./scripts/web_relevance_extract.py "" "" --top-k 5 --max-chars 2400 --format json
    3. Read only the returned blocks. 4. Answer from those blocks if they are sufficient. 5. Only if recall is clearly insufficient, rerun with one controlled expansion: - increase --top-k - or increase --max-chars - or narrow / refine the query 6. Do not jump to raw-page scraping unless the extractor failed.

    Budget Discipline

  • Prefer --max-chars 1200 to 2400 for narrow fact lookup.
  • Keep --top-k between 3 and 6 unless the user explicitly asks for breadth.
  • Narrow the query instead of widening the token budget when possible.
  • If the first run already contains the answer, stop there.

    • Output Discipline

    When answering:

  • cite which returned block supports the answer
  • say when the extractor output is incomplete or ambiguous
  • distinguish extracted text from your inference
  • do not claim the full page was reviewed unless it actually was

    • Examples

    Find pricing details from a long page:

    python3 ./scripts/web_relevance_extract.py "https://example.com/pricing" "pricing tiers api limits enterprise" --max-chars 1600 --top-k 4 --format text

    Find job requirements from a careers page:

    python3 ./scripts/web_relevance_extract.py "https://example.com/jobs/ml-engineer" "requirements python llm retrieval location" --max-chars 1800 --top-k 5 --format json

    Use a saved HTML file:

    python3 ./scripts/web_relevance_extract.py "/tmp/page.html" "refund policy cancellation deadline" --max-chars 1200

    Failure Handling

    If the page cannot be fetched or parsed:

  • report the fetch or parse failure directly
  • ask for a local HTML copy if network access is blocked
  • do not fabricate an answer from URL guesses

    • πŸ’‘ Examples

    Find pricing details from a long page:

    python3 ./scripts/web_relevance_extract.py "https://example.com/pricing" "pricing tiers api limits enterprise" --max-chars 1600 --top-k 4 --format text

    Find job requirements from a careers page:

    python3 ./scripts/web_relevance_extract.py "https://example.com/jobs/ml-engineer" "requirements python llm retrieval location" --max-chars 1800 --top-k 5 --format json

    Use a saved HTML file:

    python3 ./scripts/web_relevance_extract.py "/tmp/page.html" "refund policy cancellation deadline" --max-chars 1200