Best use case
LiteBrowse Skill is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Direct access:
Teams using LiteBrowse Skill should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/litebrowse/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How LiteBrowse Skill Compares
| Feature / Agent | LiteBrowse Skill | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Direct access:
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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: ```bash 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 "<source>" "<query>" --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: ```bash 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: ```bash 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: ```bash 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
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