Unverified50% confidenceFactExact time
Progressive disclosure puts metadata first, compressing first-layer overhead to just dozens of tokens per skill.
1
Sources
50%
Confidence
Long-term
Relevance
10/24/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedIn multi-step agent tasks, input Token consumption grows approximately linearly with the number of steps, and a 70% drop in per-Token price will be amplified by the number of calls.69% similarUnverifiedVectorized execution processes data in batches (usually hundreds to thousands of rows), amortizing the interpretation overhead per row.68% similarUnverifiedInvoking an existing Skill consumes far fewer tokens than generating from scratch, and the success rate improves through repeated iteration.68% similarUnverifiedWhat JOLT's 6.5x token efficiency improvement mainly compresses is the large-scale repeated sampling cost paid to estimate relative advantages.68% similarUnverified对于10万行代码的项目，使用向量检索每次任务可能只需加载2%~5%的相关代码，Token消耗可降低20倍以上67% similar
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