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arxiv: 2604.24026 · v4 · submitted 2026-04-27 · 💻 cs.CL · cs.AI

Recognition: unknown

From Skill Text to Skill Structure: The Scheduling-Structural-Logical Representation for Agent Skills

Qiliang Liang , Hansi Wang , Zhong Liang , Yang Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-08 04:01 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords agent skillsstructured representationLLM agentsskill discoveryrisk assessmentscheduling-structural-logicalknowledge disentanglementskill reuse
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The pith

An explicit Scheduling-Structural-Logical representation disentangles skill text into scheduling, structure, and logic for better machine use.

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

The paper proposes the Scheduling-Structural-Logical (SSL) representation to turn dense text skill documents into explicit, machine-readable structures for LLM agents. It draws on classical cognitive models to separate skill-level scheduling signals, scene-level execution structure, and logic-level action and resource evidence. An LLM normalizer creates these structures from existing text, and tests on skill discovery and risk assessment show higher performance than plain text baselines. The gains indicate that explicit separation helps agents search collections and inspect side effects without losing details buried in natural language. A sympathetic reader would care because reusable skills in agent systems currently require heavy reasoning over entangled text, limiting reliability and scalability.

Core claim

The Scheduling-Structural-Logical (SSL) representation disentangles skill-level scheduling signals, scene-level execution structure, and logic-level action/resource-use evidence from text-heavy skill artifacts. Instantiated with an LLM-based normalizer, SSL yields MRR@50 of 0.729 on Skill Discovery (up from 0.649) and macro F1 of 0.509 on Risk Assessment (up from 0.409), showing that source-grounded structure improves search and review over text-only methods.

What carries the argument

The Scheduling-Structural-Logical (SSL) representation, which separates scheduling signals, execution structure, and action logic from skill text using principles from Memory Organization Packets, Script Theory, and Conceptual Dependency to make capabilities easier for machines to acquire and leverage.

If this is right

  • Agent skill collections become searchable by explicit interfaces and constraints rather than full-text similarity.
  • Risk assessment gains precision because reviewers can examine scheduling, structure, and logic separately.
  • Skill reuse in execution becomes more reliable since control flow and side effects are no longer hidden in prose.
  • Skill management systems can support inspection and auditing without requiring full natural-language reasoning at every step.
  • The approach positions skills as operationally actionable packages rather than static documents.

Where Pith is reading between the lines

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

  • SSL structures could support automated composition of skills into larger workflows by matching scheduling and logic components.
  • The separation may help detect conflicts or redundancies across skill libraries that text similarity alone misses.
  • Classical cognitive representations might be combined with modern LLM agents to create hybrid systems that retain inspectability.
  • Extending the normalizer to live execution traces could test whether SSL remains stable when skills are applied in real environments.

Load-bearing premise

The LLM-based normalizer accurately and consistently extracts scheduling, structural, and logical components from text skill descriptions without introducing hallucinations, biases, or loss of critical information.

What would settle it

Apply the normalizer to a benchmark set of skills that have manually annotated ground-truth SSL structures, then measure extraction fidelity and check whether the reported performance lifts disappear when using noisy or incomplete extractions.

Figures

Figures reproduced from arXiv: 2604.24026 by Hansi Wang, Qiliang Liang, Yang Liu, Zhong Liang.

Figure 1
Figure 1. Figure 1: Overview of the SSL representation. A text-heavy skill artifact is converted by a normalizer view at source ↗
read the original abstract

Large language model (LLM) agents increasingly rely on reusable skills: capability packages that combine instructions, control flow, constraints, and tool calls. In current agent systems, however, skills are still represented by text-heavy artifacts, mainly SKILL{.}md-style documents whose machine-usable evidence remains embedded largely in natural-language descriptions. As a result, skill-centered agent systems face a representation problem: both managing skill collections and using skills during agent execution require reasoning over invocation interfaces, execution structure, and concrete side effects, but these signals are often entangled in a single textual surface. An explicit representation of skill knowledge may therefore help make these artifacts easier for machines to acquire and leverage. Drawing on Memory Organization Packets, Script Theory, and Conceptual Dependency from Schank and Abelson's classical work on cognitive linguistic representation, we introduce what is, to our knowledge, the first structured representation for agent skill artifacts that disentangles skill-level scheduling signals, scene-level execution structure, and logic-level action/resource-use evidence: the Scheduling-Structural-Logical (SSL) representation. We instantiate SSL with an LLM-based normalizer and evaluate SSL-derived representations in two tasks, Skill Discovery and Risk Assessment. The experiment shows that SSL significantly outperforms the text-only baselines: in Skill Discovery, MRR@50 improves from 0.649 to 0.729; in Risk Assessment, macro F1 improves from 0.409 to 0.509. These findings suggest that an explicit, source-grounded structure can make agent skills easier to search and review, positioning SSL as a practical step toward more inspectable, reusable, and operationally actionable skill representations, rather than a finished standard or end-to-end skill-management mechanism.

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 the Scheduling-Structural-Logical (SSL) representation for agent skills, drawing on Schank and Abelson's Memory Organization Packets, Script Theory, and Conceptual Dependency. Skills are converted from text (e.g., SKILL.md) to this disentangled form via an LLM-based normalizer. The representation is evaluated on Skill Discovery (MRR@50 improves from 0.649 to 0.729) and Risk Assessment (macro F1 improves from 0.409 to 0.509), outperforming text-only baselines. The authors position SSL as a practical step toward more inspectable and reusable skill artifacts for LLM agents.

Significance. If the results hold after proper validation, SSL offers a principled, source-grounded structure that could improve skill search, review, and operational use in agent systems. The explicit grounding in classical cognitive representations is a notable strength, providing external theoretical grounding rather than ad-hoc invention. The quantitative gains on two distinct tasks suggest potential for broader applicability in skill management. However, the significance is limited by the absence of controls that would confirm the gains arise from the SSL disentanglement itself.

major comments (2)
  1. [Experimental Evaluation] Experimental Evaluation: The headline improvements (MRR@50 0.649→0.729; macro F1 0.409→0.509) are reported without details on experimental setup, baseline implementations, skill data sources, statistical significance testing, or controls for LLM variability. This is load-bearing for the central claim that SSL outperforms text-only baselines, as unstated factors could explain the modest deltas.
  2. [SSL Instantiation] SSL Instantiation via LLM Normalizer: No quantitative validation (accuracy, hallucination rate, information loss) of the LLM normalizer is provided. Because SSL representations are generated by this normalizer, any systematic augmentation, rephrasing of constraints, or addition of scheduling cues would make the downstream comparisons non-equivalent; the performance delta cannot be attributed to the Scheduling-Structural-Logical disentanglement.
minor comments (2)
  1. [Introduction] The abstract and introduction could more explicitly define the three SSL components (scheduling signals, scene-level structure, logic-level evidence) with a small illustrative example to aid reader comprehension.
  2. [Evaluation] Notation for the SSL tuple or components is introduced but not consistently referenced in the evaluation sections; a table mapping original text elements to SSL fields would improve traceability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and describe the revisions we will incorporate to improve the manuscript.

read point-by-point responses

  1. Referee: [Experimental Evaluation] The headline improvements (MRR@50 0.649→0.729; macro F1 0.409→0.509) are reported without details on experimental setup, baseline implementations, skill data sources, statistical significance testing, or controls for LLM variability. This is load-bearing for the central claim that SSL outperforms text-only baselines, as unstated factors could explain the modest deltas.

    Authors: We agree that additional experimental details are necessary to substantiate the reported gains. In the revised manuscript we will expand the Experimental Evaluation section with: a complete description of the setup including LLM prompting strategies and hyperparameters; explicit implementation details and code references for all baselines; characteristics and provenance of the skill datasets; results of statistical significance testing (e.g., bootstrap confidence intervals or paired tests); and controls for LLM variability such as repeated runs across seeds and temperatures with reported variance. These additions will allow readers to evaluate the robustness of the MRR@50 and macro-F1 improvements. revision: yes

  2. Referee: [SSL Instantiation] No quantitative validation (accuracy, hallucination rate, information loss) of the LLM normalizer is provided. Because SSL representations are generated by this normalizer, any systematic augmentation, rephrasing of constraints, or addition of scheduling cues would make the downstream comparisons non-equivalent; the performance delta cannot be attributed to the Scheduling-Structural-Logical disentanglement.

    Authors: We acknowledge the importance of validating the normalizer to isolate the contribution of the SSL structure. The current manuscript does not contain such quantitative checks. In revision we will add an evaluation subsection that measures normalizer fidelity on a human-annotated subset of skills, reporting accuracy, hallucination rate, and information-preservation metrics (semantic similarity and manual review of scheduling, structural, and logical elements). We will also clarify that text-only baselines operate directly on the original skill documents without LLM processing, ensuring the comparison isolates the effect of the disentangled representation rather than any incidental augmentation by the normalizer. revision: yes

Circularity Check

0 steps flagged

SSL representation grounded in external classical work; no derivation reduces to self-inputs

full rationale

The paper defines the Scheduling-Structural-Logical (SSL) representation by explicit reference to Schank and Abelson's Memory Organization Packets, Script Theory, and Conceptual Dependency, which are independent external sources. No equations, fitted parameters, or predictions are present; the LLM normalizer is described as an instantiation method rather than a learned component whose outputs are then re-used as evidence. Downstream evaluations (Skill Discovery MRR@50 and Risk Assessment F1) compare against text baselines without any self-citation chain or renaming of known results as novel derivations. The central claim therefore remains non-circular and self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the applicability of Schank and Abelson's classical cognitive theories to modern LLM agent skills and the assumption that an LLM normalizer can faithfully produce the SSL structure from text.

axioms (1)
  • domain assumption Memory Organization Packets, Script Theory, and Conceptual Dependency from Schank and Abelson provide a suitable foundation for representing agent skills
    Invoked to motivate the three-layer SSL structure in the abstract.
invented entities (1)
  • Scheduling-Structural-Logical (SSL) representation no independent evidence
    purpose: Disentangle scheduling signals, scene-level execution structure, and logic-level action/resource evidence in agent skills
    Newly introduced structured format for skill artifacts.

pith-pipeline@v0.9.0 · 5618 in / 1396 out tokens · 65464 ms · 2026-05-08T04:01:42.468242+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

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    They deterministically extract headings, short bullet items, interface- or resource-looking lines, and short prose spans from the original SKILL.md

    The source-outline controls in Table 1 are constructed without SSL fields. They deterministically extract headings, short bullet items, interface- or resource-looking lines, and short prose spans from the original SKILL.md. This provides a deterministic non-SSL document-enrichment control for testing whether the SSL improvement can be explained by generic...

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