OpenClaw skill reduces accessibility tree tokens from 600K to 1.3K for ad-heavy sites
Token reduction for modern web tasks
A developer on r/openclaw shared benchmark data showing significant token variance in OpenClaw's default accessibility tree across different websites. The data reveals that ad-heavy sites generate massive token counts, while simpler sites remain manageable.
Benchmark data from source
- slickdeals: 24,567 elements → ~598K tokens
- ycombinator: 681 elements → ~16K tokens
- httpbin: 34 elements → ~1.5K tokens
The developer noted that "ad-heavy sites are brutal" with 600K tokens required just to observe pages filled with tracking pixels and ad iframes.
ML-based pruning solution
To address this, they built an OpenClaw skill that uses machine learning-based element ranking to prune the accessibility tree before sending it to the LLM. The approach keeps only the top ~50 actionable elements (configurable), which brings slickdeals down from ~598K tokens to ~1.3K tokens.
Available resources
The skill is available as:
- OpenClaw Skill: https://clawhub.ai/rcholic/predicate-snapshot
- GitHub repository: https://github.com/PredicateSystems/openclaw-predicate-skill
The developer is seeking feedback on alternative approaches to accessibility tree pruning for OpenClaw.
📖 Read the full source: r/openclaw
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