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arxiv: 2605.20023 · v1 · pith:FQABGMQ2new · submitted 2026-05-19 · 💻 cs.AI · cs.MA

When Skills Don't Help: A Negative Result on Procedural Knowledge for Tool-Grounded Agents in Offensive Cybersecurity

Samuel Jacob Chacko , James Hugglestone , Chashi Mahiul Islam , Xiuwen Liu This is my paper

Pith reviewed 2026-05-20 05:24 UTC · model grok-4.3

classification 💻 cs.AI cs.MA
keywords LLM agentsprocedural knowledgeskillsoffensive cybersecuritycapture the flagtool groundingenvironment feedbacknegative result
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The pith

In offensive cybersecurity, procedural Skills add only an insignificant 8.9 percentage point gain to tool-grounded LLM agents.

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

This paper re-analyzes a controlled study of an autonomous Capture-the-Flag agent under four levels of documentation richness that map to no-Skills, experiential-Skills, curated-Skills, and comprehensive-Skills conditions. The spread in success rates between the no-Skills and full-Skills settings is just 8.9 percentage points and fails to reach statistical significance under chi-square and trend tests. This stands in contrast to the 16.2 point average improvement reported across other domains. The authors identify environment-feedback bandwidth as the missing variable: when tools return strict, schema-validated, low-latency observations, the environment itself supplies the procedural correction that Skills are normally expected to provide.

Core claim

The paper establishes that in offensive cybersecurity the marginal benefit of Skills collapses because the agent's tool layer returns strict, schema-validated, low-latency observations that themselves supply the procedural correction signal normally provided by curated knowledge, resulting in a non-significant 8.9 pp difference between no-Skills and full-Skills conditions.

What carries the argument

Environment-feedback bandwidth: the richness, validity, and speed of observations returned by the agent's tools, which in this setting substitutes for the procedural guidance that Skills are designed to deliver.

Load-bearing premise

The four documentation conditions of increasing richness correspond almost exactly to the No-Skills, Experiential-Skills, Curated-Skills, and Comprehensive-Skills ablations without introducing other confounds.

What would settle it

A controlled replication in which the same agent and tasks are run with tools modified to return less detailed, delayed, or unvalidated observations; if the Skills benefit then becomes large and statistically significant, the feedback-bandwidth account is supported.

Figures

Figures reproduced from arXiv: 2605.20023 by Chashi Mahiul Islam, James Hugglestone, Samuel Jacob Chacko, Xiuwen Liu.

Figure 1
Figure 1. Figure 1: Skills gain (pp above no-Skills baseline) by con [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
read the original abstract

Agent Skills, structured packages of procedural knowledge loaded into an LLM agent at inference time, are widely reported to improve task pass rates by an average of 16.2~percentage points across diverse domains. Yet the same benchmarks show wide variance, with 16 of 84 tasks suffering negative deltas when Skills are introduced. The community has not yet articulated a clean mechanism for \emph{when} Skills help and when they are merely redundant overhead. We re-analyze a recently published 180-run controlled study of an MCP-grounded autonomous Capture-the-Flag (CTF) agent under four documentation conditions of increasing richness (55, 1{,}478, 1{,}976, and 4{,}147 lines), and show that these conditions correspond almost exactly to a No-Skills, Experiential-Skills, Curated-Skills, and Comprehensive-Skills ablation. In offensive cybersecurity, a domain not deeply covered by existing Skills benchmarks, the marginal benefit of Skills collapses. The spread between the no-Skills and full-Skills conditions is only 8.9~pp ($p = 0.71$, $\chi^2$; $p = 0.25$, Cochran--Armitage trend test; five of six pairwise Cohen's $h$ values fall below the $0.2$ small-effect threshold). We argue that the missing variable is \emph{environment-feedback bandwidth}. When an agent's tool layer returns strict, schema-validated, low-latency observations, the environment itself supplies the procedural correction signal that Skills are normally needed to provide. As a result, the marginal benefit of curated Skills diminishes substantially, and, in some cases (e.g., our timing side-channel setting), actively degrades performance. We articulate a falsifiable hypothesis, sketch its design implications for compound AI systems, and will release the reanalysis pipeline to support replication.

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

1 major / 1 minor

Summary. The paper re-analyzes a 180-run controlled study of an MCP-grounded autonomous CTF agent across four documentation conditions of increasing richness (55, 1,478, 1,976, and 4,147 lines). It maps these conditions to No-Skills, Experiential-Skills, Curated-Skills, and Comprehensive-Skills ablations and reports that the marginal benefit of Skills collapses in offensive cybersecurity, with an 8.9 pp spread between no-Skills and full-Skills conditions that is statistically non-significant (p=0.71, χ²; p=0.25, Cochran-Armitage; five of six pairwise Cohen's h < 0.2). The authors attribute the null result to high environment-feedback bandwidth, where strict, schema-validated tool observations supply the procedural correction signal normally provided by Skills, and they articulate a falsifiable hypothesis while committing to release the reanalysis pipeline.

Significance. If the central mapping holds, this negative result in an underrepresented domain supplies a useful counter-example to the average 16.2 pp gain reported in existing Skills benchmarks. It isolates environment-feedback bandwidth as a plausible moderator and offers design implications for compound AI systems in which rich tool feedback may render curated procedural knowledge redundant or even harmful. The planned release of the reanalysis pipeline and the explicit falsifiable hypothesis are concrete strengths that support replication and further testing.

major comments (1)
  1. [Abstract and re-analysis description] The assertion that the four documentation conditions 'correspond almost exactly' to the No-Skills / Experiential-Skills / Curated-Skills / Comprehensive-Skills ablation is load-bearing for the bandwidth interpretation, yet the manuscript provides no explicit side-by-side comparison of prompt templates, tool schemas, observation formatting, latency, error-handling, or task distributions across the four conditions. Without this, differences in non-procedural elements could confound the reported 8.9 pp spread and the attribution to feedback bandwidth.
minor comments (1)
  1. [Results] The phrase 'five of six pairwise Cohen's h values' would be clearer if the specific pairs and the sixth value were enumerated in a table or footnote.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful and constructive review of our re-analysis. The point about strengthening the mapping between documentation conditions and skill ablations is well taken, and we address it directly below.

read point-by-point responses

  1. Referee: [Abstract and re-analysis description] The assertion that the four documentation conditions 'correspond almost exactly' to the No-Skills / Experiential-Skills / Curated-Skills / Comprehensive-Skills ablation is load-bearing for the bandwidth interpretation, yet the manuscript provides no explicit side-by-side comparison of prompt templates, tool schemas, observation formatting, latency, error-handling, or task distributions across the four conditions. Without this, differences in non-procedural elements could confound the reported 8.9 pp spread and the attribution to feedback bandwidth.

    Authors: We agree that an explicit side-by-side comparison would improve transparency and reduce the possibility that unmeasured differences in non-procedural elements are driving the observed spread. The four conditions were taken directly from the original controlled study, in which the primary manipulated variable was documentation richness while the underlying agent architecture, tool interface, task distribution, and environment feedback mechanisms were held fixed. Nevertheless, we acknowledge that the current manuscript does not present this comparison in one place. In the revised version we will add a new table (and accompanying text) that directly juxtaposes prompt templates, tool schemas, observation formatting, latency characteristics, error-handling behavior, and task distributions across the four conditions. This addition will make the correspondence to the No-Skills / Experiential-Skills / Curated-Skills / Comprehensive-Skills ablation explicit and will further support the environment-feedback-bandwidth interpretation. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical re-analysis

full rationale

The paper's derivation consists of re-analyzing pass-rate data from a prior 180-run CTF study across four documentation conditions (55 to 4,147 lines) and applying standard statistical tests (χ², Cochran-Armitage, Cohen's h) to compare no-Skills vs. full-Skills performance. The reported 8.9 pp spread and non-significant p-values follow directly from the observed outcomes under the stated conditions; no equations, fitted parameters, or self-referential definitions reduce the result to its inputs by construction. The asserted correspondence between documentation richness and Skills ablations is an interpretive mapping rather than a definitional or self-citation load-bearing step. This is a self-contained empirical negative result with independent statistical content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The claim depends on the proxy mapping being accurate and the statistical tests being appropriately applied to the reanalyzed data.

axioms (1)
  • domain assumption Documentation line counts serve as valid proxies for skill richness levels
    The paper equates specific line counts (55, 1478, etc.) to No-Skills through Comprehensive-Skills.
invented entities (1)
  • environment-feedback bandwidth no independent evidence
    purpose: Explains the diminished role of Skills in tool-grounded settings
    Proposed as the missing variable without independent measurement or prior definition in the abstract.

pith-pipeline@v0.9.0 · 5890 in / 1348 out tokens · 82443 ms · 2026-05-20T05:24:22.571363+00:00 · methodology

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Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

  • IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    We re-analyze a recently published 180-run controlled study of an MCP-grounded autonomous Capture-the-Flag (CTF) agent under four documentation conditions of increasing richness (55, 1,478, 1,976, and 4,147 lines), and show that these conditions correspond almost exactly to a No-Skills, Experiential-Skills, Curated-Skills, and Comprehensive-Skills ablation.

  • IndisputableMonolith/Foundation/RealityFromDistinction.lean reality_from_one_distinction unclear
    ?
    unclear

    Relation between the paper passage and the cited Recognition theorem.

    H1 (Feedback-Bandwidth). The marginal benefit of curated Agent Skills is inversely related to the bandwidth of deterministic environment feedback available to the agent during task execution.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

9 extracted references · 9 canonical work pages · 2 internal anchors

  1. [1]

    Agent Skills Overview , year =

    work page

  2. [2]

    Agent Skills Specification , year =

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  3. [3]

    SkillsBench: 84 tasks across 11 domains , year =

    work page

  4. [4]

    SkillsBench: Benchmarking How Well Agent Skills Work Across Diverse Tasks

    SkillsBench: Benchmarking how well agent skills work across diverse tasks , author=. arXiv preprint arXiv:2602.12670 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  5. [5]

    Model Context Protocol , year =

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  6. [6]

    [Citation withheld for double-blind review; full reference will be restored in camera-ready.] , year =

    work page

  7. [7]

    SoK: Agentic Skills -- Beyond Tool Use in LLM Agents

    SoK: Agentic Skills--Beyond Tool Use in LLM Agents , author=. arXiv preprint arXiv:2602.20867 , year=

    work page internal anchor Pith review Pith/arXiv arXiv

  8. [8]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    Grammar-constrained decoding for structured NLP tasks without finetuning , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    work page 2023

  9. [9]

    Transactions of the association for computational linguistics , volume=

    Lost in the middle: How language models use long contexts , author=. Transactions of the association for computational linguistics , volume=

    work page