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arxiv: 2606.11543 · v1 · pith:KMTY5WOVnew · submitted 2026-06-10 · 💻 cs.AI · cs.SE

SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior

Zhiyu Chen , Zihan Guo , Bo Huang , Bingwei Lu , Jianghao Lin , Yuanjian Zhou , Weinan Zhang This is my paper

Pith reviewed 2026-06-27 10:09 UTC · model grok-4.3

classification 💻 cs.AI cs.SE
keywords LLM agentsagent skillsprogressive disclosureskill organizationruntime behaviorSkillsBenchevaluation frameworktrajectory analysis
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The pith

Progressive Disclosure organizes agent skills so agents touch more resources and pass more trials than flat files.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that the structure of procedural knowledge in LLM agent skills affects intermediate runtime actions more immediately than final success rates. Through SkillJuror, which creates matched variants that keep task content fixed while varying only organization, it compares a Progressive Disclosure approach—root files that point to supporting resources when needed—against a flat baseline. In 82 tasks the structured version raised distinct resources touched per trajectory and effective uptake events, producing a small rise in verified successes. A sympathetic reader cares because this demonstrates that how skills are presented is not neutral formatting but actively shapes what agents look for and apply during execution.

Core claim

Progressive Disclosure changes runtime behavior before aggregate outcomes: distinct Skill resources touched per trajectory rise from 1.18 to 3.85, effective uptake events rise from 1.33 to 3.92, and it yields 17 additional verifier-passing trials out of 410 matched trials (+4.1%) over the normalized flat baseline. The benefit is task-dependent. Progressive Disclosure helps when supporting resources guide implementation, checking, or repair, but is weaker when success hinges on exact output conventions, numerical thresholds, or long artifact-generation pipelines.

What carries the argument

Progressive Disclosure, a skill-writing method in which a concise root file directs agents to supporting resources on demand, measured against a normalized flat baseline inside the SkillJuror framework that uses semantically controlled variants and trajectory logging.

If this is right

  • Runtime behavior changes appear before any measurable gain in final task outcomes.
  • Progressive Disclosure improves results mainly on tasks where supporting files can guide implementation, checking, or repair steps.
  • Skill organization directly influences the search and application patterns agents follow during execution.
  • Outcome improvements require that the newly exposed resources are actionable for the given task.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Skill writers could test modular disclosure first on tasks that require dynamic guidance rather than fixed output formats.
  • Benchmarks that record only final answers will miss most of the behavioral effect shown here.
  • The same organization contrast could be run on non-LLM agents to test whether the uptake pattern depends on language-model reasoning.
  • Task-dependent results suggest hybrid skill files that adapt their disclosure depth to the problem type.

Load-bearing premise

The controlled variants truly separate organization effects from differences in the knowledge itself, and the logs correctly record when an agent has used a resource rather than merely opened the file.

What would settle it

A matched-trial replication that finds no rise in the number of distinct resources touched or uptake events under Progressive Disclosure would show that organization does not change runtime behavior.

Figures

Figures reproduced from arXiv: 2606.11543 by Bingwei Lu, Bo Huang, Jianghao Lin, Weinan Zhang, Yuanjian Zhou, Zhiyu Chen, Zihan Guo.

Figure 1
Figure 1. Figure 1: From entangled Skill comparisons to controlled runtime evidence. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the SkillJuror method pipeline. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Example controlled Skill-bundle transformation. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Per-task pass-count deltas for Progressive Disclosure relative to Baseline. [PITH_FULL_IMAGE:figures/full_fig_p011_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Skill-step timing in the Baseline–Progressive Disclosure comparison. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Task-type labeling schema for the SkillsBench instantiation. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Process–outcome alignment across selected task strata. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
read the original abstract

Agent Skills augment large language model (LLM) agents with procedural knowledge at inference time, but current benchmarks rarely distinguish what a Skill says from how it is organized. We study this distinction through Progressive Disclosure, where a concise root file points agents to supporting resources on demand, and compare it with a normalized flat baseline. We present SkillJuror, a framework for evaluating Skill writing paradigms through semantically controlled variants, matched multi-trial evaluations, and trajectory evidence while holding task knowledge fixed. In an 82-task SkillsBench study, Progressive Disclosure changes runtime behavior before aggregate outcomes: distinct Skill resources touched per trajectory rise from 1.18 to 3.85, and effective uptake events rise from 1.33 to 3.92. It also yields 17 additional verifier-passing trials out of 410 matched trials (+4.1%) over the normalized flat baseline. The benefit is task-dependent. Progressive Disclosure helps when supporting resources guide implementation, checking, or repair, but is weaker when success hinges on exact output conventions, numerical thresholds, or long artifact-generation pipelines. These results show that Skill organization is not mere presentation: it can change how agents search and apply procedural knowledge, while outcome gains depend on whether the exposed resources are actionable for the task. Code is available at https://github.com/zhiyuchen-ai/skill-juror.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces SkillJuror, a framework using semantically controlled variants, matched multi-trial evaluations, and trajectory logging to compare Progressive Disclosure (concise root file pointing to resources on demand) against a normalized flat baseline on an 82-task SkillsBench. It reports that Progressive Disclosure increases distinct Skill resources touched per trajectory from 1.18 to 3.85 and effective uptake events from 1.33 to 3.92, yielding 17 additional verifier-passing trials out of 410 matched trials (+4.1%), with task-dependent benefits (stronger when resources guide implementation/checking/repair).

Significance. If the measurements hold, the work provides concrete empirical evidence that Skill organization affects agent search/apply behavior and modestly improves outcomes independently of content, with code release aiding reproducibility. This distinguishes organization effects from content and highlights when disclosure helps versus when exact conventions or long pipelines dominate.

major comments (2)
  1. [Trajectory logging subsection (Methods)] Trajectory logging subsection (Methods): the definitions of 'distinct Skill resources touched' and 'effective uptake events' must specify the parser criteria. If counters increment on any file reference/read rather than requiring evidence of content integration into subsequent reasoning, code, or outputs, the reported rises (1.18→3.85; 1.33→3.92) may reflect navigation encouraged by the root pointer rather than genuine organizational effects on knowledge application. This directly threatens the claim that organization changes runtime behavior while holding task knowledge fixed.
  2. [Results on matched trials (Section 4 or equivalent)] Results on matched trials (Section 4 or equivalent): the +4.1% lift (17/410) lacks reported error bars, confidence intervals, or exclusion criteria for the 410 matched trials. Without these, it is impossible to assess whether post-hoc task filtering or logging definitions affect the central outcome comparison.
minor comments (2)
  1. The abstract and results should explicitly state whether statistical significance testing was performed on the behavioral deltas and outcome lift.
  2. Clarify in the variant construction how semantic control was verified to ensure organization is isolated from content differences.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below and will revise the manuscript to improve clarity and statistical reporting while defending the core claims on the basis of our trajectory analysis.

read point-by-point responses

  1. Referee: [Trajectory logging subsection (Methods)] Trajectory logging subsection (Methods): the definitions of 'distinct Skill resources touched' and 'effective uptake events' must specify the parser criteria. If counters increment on any file reference/read rather than requiring evidence of content integration into subsequent reasoning, code, or outputs, the reported rises (1.18→3.85; 1.33→3.92) may reflect navigation encouraged by the root pointer rather than genuine organizational effects on knowledge application. This directly threatens the claim that organization changes runtime behavior while holding task knowledge fixed.

    Authors: We agree that the parser criteria require explicit specification. The current manuscript describes the metrics at a high level but omits the precise implementation details. In revision we will expand the Trajectory logging subsection to include the full parser rules and pseudocode. Distinct Skill resources touched counts only unique files whose content is subsequently referenced in agent reasoning, code, or outputs (via keyword/phrase matching from the resource in later steps). Effective uptake events further require evidence of integration, such as using the disclosed resource to guide implementation, checking, or repair actions. Counters are not incremented on bare file reads or navigation alone. This design isolates organizational effects on knowledge application rather than mere pointer following, consistent with the task-dependent outcome patterns reported. revision: yes

  2. Referee: [Results on matched trials (Section 4 or equivalent)] Results on matched trials (Section 4 or equivalent): the +4.1% lift (17/410) lacks reported error bars, confidence intervals, or exclusion criteria for the 410 matched trials. Without these, it is impossible to assess whether post-hoc task filtering or logging definitions affect the central outcome comparison.

    Authors: We acknowledge the omission of statistical details. In the revised manuscript we will report Wilson score confidence intervals around the per-condition success rates and the 17/410 difference. The 410 matched trials are defined as all trials in which both conditions completed without execution or logging failures; we will explicitly list the exclusion criteria (incomplete trajectories, verifier crashes, etc.) and confirm that no post-hoc task filtering beyond these objective criteria was applied. We will also note the modest overall effect size and its concentration in specific task categories, allowing readers to evaluate robustness directly. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical comparison with explicit baseline

full rationale

The paper reports direct experimental measurements (resources touched 1.18→3.85, uptake events 1.33→3.92, +4.1% verifier passes) from matched trials on SkillsBench using Progressive Disclosure vs. normalized flat baseline. No equations, fitted parameters renamed as predictions, self-citations as load-bearing premises, or ansatzes appear in the abstract or described claims. Trajectory logging and semantic controls are presented as measurement methods, not derivations that reduce to their own inputs. The evaluation is self-contained against the stated flat baseline.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical model or new entities; the work rests on the empirical assumption that the benchmark tasks and verifier are representative and that trajectory logs faithfully reflect skill uptake.

pith-pipeline@v0.9.1-grok · 5785 in / 1141 out tokens · 22012 ms · 2026-06-27T10:09:39.551337+00:00 · methodology

discussion (0)

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Reference graph

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