SkillJuror: Measuring How Agent Skill Organization Changes Runtime Behavior
Zhiyu Chen, Zihan Guo, Bo Huang, Bingwei Lu, Jianghao Lin, Yuanjian Zhou, Weinan Zhang
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.
AI Impact Assessments
(1 models)Scientific Impact Assessment: SkillJuror
1. Core Contribution
SkillJuror introduces a framework for isolating and measuring the effect of *how* procedural knowledge (Agent Skills) is organized—as distinct from *what* knowledge is contained—on LLM agent runtime behavior. The key insight is that the same task knowledge, when restructured from a flat monolithic document into a Progressive Disclosure (PD) format (concise root file with on-demand supporting resources), fundamentally alters agent trajectories even when task semantics are held constant.
The paper makes three concrete contributions: (1) the first controlled evaluation treating Skill organization as an isolated runtime variable, (2) the SkillJuror framework with its construction validation pipeline and multi-dimensional evaluation, and (3) empirical evidence from an 82-task study showing that organization changes behavior (resource fanout from 1.18→3.85, ERU from 1.33→3.92) more dramatically than it changes outcomes (+4.1% pass rate).
2. Methodological Rigor
The experimental design shows careful attention to confound control. The two-step transformation pipeline (source→Baseline→PD) ensures that the PD variant is a constrained reorganization of the Baseline rather than an independent generation, reducing attribution ambiguity. The three-tier validation (deterministic gating, rubric-based semantic auditing, human-in-the-loop adjudication) across 968 rubric items with only 3 requiring manual review demonstrates thorough quality control.
However, several methodological concerns merit attention:
The construction validation is more convincing than the outcome evaluation—the paper's strongest methodological contribution is the *framework design* rather than the specific empirical findings.
3. Potential Impact
Practical implications for Skill authoring: The finding that Progressive Disclosure shifts agents from "read-and-execute" to "implement-verify-repair" loops has direct implications for anyone writing procedural documentation for LLM agents. The task-dependent analysis (Table 7) provides actionable guidance: PD helps for code repair and security tasks but hurts for exact-artifact generation and numeric tolerance tasks.
Framework reusability: SkillJuror's design—controlled variant construction, matched multi-trial evaluation, multi-dimensional measurement—could be adapted for evaluating other Skill writing paradigms (scriptization, metadata design) or more broadly for any controlled comparison of agent-facing artifacts.
Conceptual contribution: The separation of "what a Skill says" from "how it is organized" is a clean conceptual distinction that could influence how the community thinks about prompt engineering, documentation design, and agent-facing artifacts more broadly. This connects to the broader prompt-sensitivity literature (Sclar et al., 2024) but extends it to structured, multi-file artifacts.
Limitations of impact scope: The paper is tightly scoped to one paradigm comparison on one benchmark with one model. The Agent Skills ecosystem is nascent (the specification and most cited papers are from 2025-2026), so the practical user base is currently small.
4. Timeliness & Relevance
The paper addresses a timely question as Agent Skills gain traction following Anthropic's 2025 guidelines and the emergence of SkillsBench, SWE-Skills-Bench, and related benchmarks. The shift from "do Skills help?" to "how should Skills be written?" is a natural and needed progression. The timing positions the work well in the rapidly evolving agent evaluation landscape.
However, the field is moving extremely fast—the reference list is dominated by 2025-2026 preprints, and the practical relevance depends heavily on whether the Agent Skills specification gains lasting adoption versus being superseded by alternative approaches.
5. Strengths & Limitations
Key Strengths:
Notable Weaknesses:
Additional Observations:
Overall Assessment
SkillJuror makes a valuable conceptual and methodological contribution by cleanly separating Skill organization from content and demonstrating that organization changes agent behavior. The framework design is sound and reusable. However, the empirical findings, while carefully reported, show modest and statistically uncertain outcome effects, and the process metrics rely on unvalidated LLM judgment. The paper's primary value lies in establishing a controlled evaluation paradigm for an emerging problem space rather than in delivering definitive empirical conclusions.
Generated Jun 11, 2026
Comparison History (18)
MODF-SIR addresses the broader and more impactful problem of social intelligence reasoning with multimodal models, combining knowledge distillation, test-time adaptation, and multi-agent collaboration in a novel framework. It demonstrates state-of-the-art results across multiple benchmarks with only 30% training data, suggesting strong practical efficiency. Paper 1 (SkillJuror) makes a narrower contribution studying how skill organization affects agent behavior, with modest outcome improvements (+4.1%). Paper 2's combination of techniques and its applicability to social AI reasoning gives it wider relevance and stronger potential impact across multiple research communities.
HORMA addresses a fundamental and broadly relevant problem—efficient memory management for LLM agents in long-horizon tasks—with a comprehensive hierarchical approach combining structured memory construction and RL-trained navigation-based retrieval. It demonstrates strong results across multiple benchmarks (ALFWorld, LoCoMo, LongMemEval) with significant efficiency gains (22.17% token usage). Paper 2 (SkillJuror) provides useful but narrower insights about skill organization's effect on agent behavior, with modest outcome improvements (+4.1%). HORMA's broader applicability, stronger methodological contributions, and more impactful efficiency-performance trade-offs give it higher potential impact.
Paper 2 addresses Theory of Mind, a fundamental cognitive capability crucial for multi-agent systems and human-AI alignment. By introducing a recursive framework with formal logical grounding (KD45 analysis) and achieving state-of-the-art results across multiple benchmarks, it offers broader theoretical and practical implications. In contrast, Paper 1 focuses on a more specific architectural detail regarding agent skill organization, which, while useful, is less likely to drive foundational shifts in AI reasoning.
Paper 1 has higher likely impact due to a concrete, operational evaluation framework (SkillJuror) with controlled variants, matched multi-trial methodology, trajectory-level evidence, and quantitative results on a sizable benchmark (82 tasks). It addresses an immediate, widely relevant engineering question—how skill/agent instruction organization affects runtime behavior—yielding actionable guidance and open-source code. Paper 2 is conceptually timely and potentially broad for value-laden multi-agent systems, but appears more position/framework-focused with limited empirical validation described, which may reduce near-term adoption and measurable impact.
Paper 2 addresses a fundamental gap in multimodal large language models (MLLMs)—their inability to effectively use visual tools for complex mathematical reasoning. Exposing this critical weakness in tool-assisted visual grounding has broad implications for advancing MLLMs in scientific and engineering domains. Paper 1, while useful for optimizing agent skill retrieval, focuses on a narrower aspect of prompt organization and procedural knowledge management, making Paper 2's potential impact on model architecture and multimodal training broader and more significant.
Paper 2 addresses a highly interdisciplinary challenge spanning AI, education, and cognitive psychology. By applying dialogue policy optimization and process rewards to the novel task of eliciting human creativity, it offers a broader societal and methodological impact compared to Paper 1, which focuses on a narrower, albeit rigorous, optimization of LLM agent skill architecture.
MoCA-Agent introduces an innovative 'market-of-claims' architecture that advances multi-agent reasoning through atomic, claim-level verification rather than free-form debate. This effectively addresses silent errors and hallucinations in high-stakes numerical tasks. Its rigorous methodology and strong performance across diverse financial and tabular benchmarks suggest higher potential for real-world application and broader methodological impact compared to SkillJuror's narrower focus on evaluating skill organization.
Paper 2 has higher potential impact: it tackles a major scalability bottleneck (quadratic attention) with a broadly applicable, timely recipe (SA→SWA conversion + RL adaptation) that could enable efficient long-context reasoning without retraining from scratch. The architecture-aware RL insight generalizes beyond math to other tasks where data/architecture mismatch matters, potentially influencing model design and training pipelines across NLP and systems. Paper 1 is novel for agent-skill organization evaluation, but its scope is narrower (benchmarking/authoring paradigm effects) and outcome gains are modest and task-dependent.
Paper 1 is more novel and broadly impactful: it introduces a new setting (dynamic black-box LLM provenance) and a principled method (proxy-LLM activation mapping + temporal filtering + Bayesian evidence accumulation) that can generalize across prompts and scales with multiple proxy readers. The applications (model provenance, auditing, security, governance) are immediate and cross-cutting. While Paper 2 is timely and methodologically careful for agent evaluation, its contribution is narrower (skill organization effects) and the demonstrated outcome gains are modest, suggesting more limited near-term impact across fields.
Paper 2 presents a high-impact application in healthcare, addressing the critical challenge of pulmonary diagnosis using Electronic Medical Records. The introduction of a novel domain-specific knowledge graph (LungKG) combined with a specialized LLM (Lung-R1) provides valuable, reusable resources for the medical AI community. Its potential to improve real-world patient outcomes and its rigorous methodology bridging knowledge graphs with reinforcement learning give it broader cross-disciplinary impact compared to Paper 1's narrower focus on LLM agent prompt organization.