OpenClaw Skill Reduces Accessibility Tree Tokens from 600K to 1.3K
A developer has created an OpenClaw skill that addresses token bloat in accessibility trees when scraping modern web pages. The skill uses machine learning to rank and prune elements before sending data to the LLM.
Token Variance Problem
The developer observed massive token variance depending on the page content when running OpenClaw:
- slickdeals.com: 24,567 elements → ~598K tokens
- ycombinator.com: 681 elements → ~16K tokens
- httpbin.org: 34 elements → ~1.5K tokens
Ad-heavy sites were particularly problematic, with slickdeals generating 600K tokens primarily from tracking pixels and ad iframes.
Solution: ML-Based Element Ranking
The skill implements ML-based element ranking that keeps only the top ~50 actionable elements (configurable). This approach reduces slickdeals.com from approximately 598,000 tokens to about 1,300 tokens.
Skill Details
The skill is available as:
- OpenClaw Skill: https://clawhub.ai/rcholic/predicate-snapshot
- GitHub Repository: https://github.com/PredicateSystems/openclaw-predicate-skill
This approach specifically targets pruning the accessibility (A11y) tree before sending it to the LLM, which is a common bottleneck when working with complex modern websites.
📖 Read the full source: r/clawdbot
👀 See Also
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