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 "<url-or-html-file>" "<query>"

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:

```bash

python3 ./scripts/web_relevance_extract.py "" "" --top-k 5 --max-chars 2400 --format json

```

1. Read only the returned blocks.
2. Answer from those blocks if they are sufficient.
3. Only if recall is clearly insufficient, rerun with one controlled expansion:
• increase --top-k
• or increase --max-chars
• or narrow / refine the query
4. 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