Progressive Disclosure Triples Resource Touches — Pass Rate Moves Just 4 Points

Notes on SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior (arXiv:2606.11543) — Zhiyu Chen, Zihan Guo, Bo-Sheng Huang, Bingwei Lu, Jianghao Lin, Yuanjian Zhou, Weinan Zhang · June 2026

Note published July 25, 2026 · written by SkillFed’s research pipeline from the paper above · how these notes are made

AI-assisted notes · reviewed by SkillFed Agentic benchmarks

SkillJuror isolates Skill organization as a variable independent of content — a distinction most Agent Skill benchmarks conflate. It starts from a shared source Skill bundle and builds two knowledge-matched variants: a flattened Baseline that keeps every command, threshold, and helper contract in one file, and a Progressive Disclosure (PD) version that reorganizes the same content behind a short root SKILL.md pointing to on-demand references. A three-tier construction audit — deterministic file and path gating, an LLM rubric check across 968 items, and human adjudication on the 3 that failed — confirms the pair differs only in layout, not in task knowledge. Both variants then run through the same Codex/GPT-5.4 harness across 82 SkillsBench tasks, five trials per condition, 1,230 trials total, with every trajectory logged for downstream analysis.

Reorganization changes behavior before it changes outcomes. Under PD, agents touch 3.85 distinct Skill resources per trajectory versus 1.18 under the flat Baseline, and Effective Resource Uptake — resource interactions the agent actually turns into implementation, validation, or repair steps, judged by an LLM auditor — rises from 1.33 to 3.92 events per trajectory. Skill-related work shifts later in the trajectory too: Baseline puts 69.5% of its skill steps in the first third, PD only 59.4%, consistent with agents returning to references mid-task instead of front-loading everything. That behavioral shift maps to a real but modest outcome gain: PD reaches a 46.1% verifier pass rate against Baseline's 42.0%, 17 additional passes out of 410 matched trials (+4.1%, CI half-width ±6.0%). The gain is uneven — PD wins on 23 of 82 tasks, ties on 44, loses on 15 — and splits by task type: PD helps when supporting resources feed an iterative implement-check-repair loop, but pays a fanout tax when success hinges on exact numeric tolerances, output schemas, or long artifact pipelines.

Key numbers

Pass rate, PD vs Baseline (82 tasks)	46.1% vs 42.0%
Additional verifier passes from PD	+17 of 410 trials (+4.1%)
Distinct skill resources touched per trajectory	3.85 vs 1.18
Effective Resource Uptake events per trajectory	3.92 vs 1.33
Tasks where PD underperforms Baseline	15 of 82 tasks

What this means for skill authors

The real contribution here is methodological discipline: separating a claim ("progressive disclosure works") from the two things that usually get bundled with it — better task knowledge and better authorial polish. Building PD as a constrained rewrite of the Baseline, rather than as an independent artifact, means any measured effect is attributable to layout, not to a rewritten Skill that happens to read more clearly. That's the right control for a narrower, more useful question than "is progressive disclosure good": does this reorganization change what an agent does, for this kind of task.

For Skill authors the actionable signal isn't the aggregate 4.1% — it's the split by task type. Progressive disclosure pays off when a task rewards iteration: an agent hits friction, opens a reference, closes the loop. It costs more than it earns when the task's failure mode is a rigid output contract — an exact tolerance, a schema, an ordering rule — because spreading that constraint across files makes it easier to miss without changing whether the agent ultimately satisfies it. That argues against treating progressive disclosure as a default style rule, and for matching Skill layout to how unforgiving the task's verifier is.

It's also an argument for vetting Skills on more than structural compliance. A catalog check that only confirms "root file is short, links to references" — the kind of box a linter could tick — would pass a Skill regardless of whether its resources actually close an implement-verify-repair loop. That's the same gap dual-tool scanning is built to catch on the content side: structure alone doesn't tell you whether a Skill's supporting material is load-bearing or decorative for the task it targets.

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Related notes

References

Chen, Guo, Huang, Lu, Lin, Zhou & Zhang (2026). SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior. arXiv:2606.11543.
Li et al. (2026). SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks. arXiv:2602.12670.
Anthropic (2025). Equipping Agents for the Real World with Agent Skills.