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arxiv: 2606.20659 · v1 · pith:EDZTKGEMnew · submitted 2026-06-09 · 💻 cs.AI · cs.LG· cs.SE

Skill Coverage: A Test Adequacy Metric for Agent Skills

Boyin Tan , Xiaowei Huang , Youcheng Sun This is my paper

Pith reviewed 2026-06-27 13:31 UTC · model grok-4.3

classification 💻 cs.AI cs.LGcs.SE
keywords skill coveragetest adequacyagent skillsLLM agentsbenchmark evaluationSkillsBenchbehavior constraintstrajectory analysis
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The pith

Skill coverage shows existing agent benchmarks exercise only 39.90 to 43.98 percent of documented skill behavior constraints.

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

The paper introduces skill coverage as a test adequacy metric focused on the skill artifact itself rather than task outcomes. It extracts observable behavior constraints from skill documents and applies a binary judgment to determine whether an agent trajectory supplies enough evidence to exercise each constraint. This matters to a sympathetic reader because task success alone does not indicate which parts of a reusable skill have been tested or left unexamined. When the metric is applied to SkillsBench, the reported coverage falls in the 39.90 to 43.98 percent range. The finding indicates that current benchmark tasks leave large portions of documented skill guidance untested even when tasks are completed successfully.

Core claim

Skill coverage treats the skill artifact as the object under test. The method extracts observable skill behavior constraints from skill documents and measures whether an agent trajectory provides sufficient evidence to exercise and evaluate each constraint through a binary cover or not cover judgment that does not assign an additional outcome label. Application to SkillsBench shows that existing benchmark executions cover only 39.90 to 43.98 percent of skill behavior constraints, which demonstrates that successful task completion does not imply adequate testing of the skill artifact.

What carries the argument

Skill coverage metric, which extracts observable skill behavior constraints from skill documents and applies binary evidence-based coverage judgments to trajectories.

If this is right

  • Successful task completion does not guarantee that all documented skill behavior constraints have been exercised.
  • Current benchmark tasks leave over half of documented skill guidance untested.
  • Test adequacy for agent skills requires explicit measurement of constraint coverage beyond task-level outcomes.
  • Benchmark evaluations should report skill coverage in addition to task success rates.

Where Pith is reading between the lines

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

  • New benchmark tasks could be designed specifically to target the uncovered constraints identified by the metric.
  • The same extraction-and-judgment approach might be applied to evaluate coverage in other forms of procedural documentation used by agents.
  • Skill documents themselves could be improved to make more constraints explicitly observable for testing purposes.

Load-bearing premise

Observable skill behavior constraints can be reliably extracted from skill documents and a binary judgment on whether a trajectory supplies sufficient evidence for each constraint can be made consistently without outcome labels.

What would settle it

Independent extraction of constraints from the same skill documents followed by re-judgment of the same trajectories that produces coverage rates outside the 39.90 to 43.98 percent range would falsify the reported coverage levels.

Figures

Figures reproduced from arXiv: 2606.20659 by Boyin Tan, Xiaowei Huang, Youcheng Sun.

Figure 1
Figure 1. Figure 1: SBC coverage and task success ranges across three traces per agent–model config￾uration on 78 SkillsBench source tasks. Each row is sorted by average task success rate. The upper bar shows average task success and the lower bar shows average binary cover rate; whiskers show the corresponding ranges across three traces. Right-side labels report the averages. Existing SkillsBench executions cover less than h… view at source ↗
Figure 2
Figure 2. Figure 2: Distribution of per-skill coverage rates across four coverage buckets. Each row [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Task-outcome-conditioned binary SBC coverage across three traces per agent– model configuration. Each row partitions 234 task executions from 78 source tasks by benchmark verifier outcome. Open markers show coverage among unsuccessful task executions, filled markers show coverage among successful task executions, and right-side labels report the coverage difference. 9 [PITH_FULL_IMAGE:figures/full_fig_p00… view at source ↗
read the original abstract

Agent skills encode reusable procedural knowledge that guides large language model agents across tasks and execution contexts. Existing evaluations primarily assess skills through task level outcomes, yet task success alone does not reveal which parts of a skill have been exercised or which remain untested. We introduce skill coverage, a test adequacy metric that treats the skill artifact as the object under test. Our approach extracts observable skill behavior constraints from skill documents and measures whether an agent trajectory provides sufficient evidence to exercise and evaluate each constraint. Skill coverage uses a binary cover or not cover judgment, which reports whether a documented behavior has been exercised with sufficient observable evidence, without assigning an additional outcome label to the behavior. Applying skill coverage to SkillsBench reveals that existing benchmark executions cover only 39.90 to 43.98% of skill behavior constraints, suggesting that current benchmark tasks leave large portions of documented skill guidance untested. These findings show that successful task completion does not imply adequate testing of the skill artifact, highlighting skill coverage as a measure of how thoroughly agent skills are tested.

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 / 0 minor

Summary. The paper proposes skill coverage, a test adequacy metric for agent skills that extracts observable behavior constraints from skill documents and uses binary judgments to determine whether an agent trajectory supplies sufficient evidence for each constraint (without outcome labels). Applying the metric to SkillsBench shows existing benchmark executions cover only 39.90–43.98% of constraints, leading to the claim that task success does not imply adequate testing of the skill artifact.

Significance. If the extraction and judgment procedures prove reliable, the metric offers a useful complement to outcome-only evaluations by quantifying how thoroughly documented skill guidance is exercised. The paper correctly distinguishes task-level success from coverage of the skill document itself and applies the metric to an external benchmark, which is a constructive step toward more granular agent evaluation.

major comments (2)
  1. [Section 3] Section 3 and the evaluation protocol: the central empirical result (39.90–43.98% coverage) depends on two unvalidated steps—extraction of observable skill behavior constraints from documents and binary “sufficient evidence” judgments on trajectories—yet no inter-annotator agreement statistics or blinded validation against ground-truth covered trajectories are reported.
  2. [Evaluation protocol] Evaluation protocol: the binary judgments are presented as deterministic once constraints are fixed, but the protocol withholds outcome labels; without a comparison to human experts who have access to task success/failure or to known covered/uncovered trajectories, the reported coverage interval cannot be treated as a stable property of the benchmark executions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the referee's thoughtful comments. We address each major comment below and commit to revisions where the manuscript lacks supporting validation.

read point-by-point responses

  1. Referee: [Section 3] Section 3 and the evaluation protocol: the central empirical result (39.90–43.98% coverage) depends on two unvalidated steps—extraction of observable skill behavior constraints from documents and binary “sufficient evidence” judgments on trajectories—yet no inter-annotator agreement statistics or blinded validation against ground-truth covered trajectories are reported.

    Authors: We agree that the manuscript does not report inter-annotator agreement or blinded validation for constraint extraction and binary judgments. While the extraction targets observable behaviors described in skill documents and the judgments assess presence of sufficient evidence without outcome labels, these steps would benefit from explicit reliability metrics. In the revised manuscript we will add a validation subsection that reports IAA on a sampled subset of constraints and judgments together with a blinded comparison against a set of ground-truth covered/uncovered trajectories. revision: yes

  2. Referee: [Evaluation protocol] Evaluation protocol: the binary judgments are presented as deterministic once constraints are fixed, but the protocol withholds outcome labels; without a comparison to human experts who have access to task success/failure or to known covered/uncovered trajectories, the reported coverage interval cannot be treated as a stable property of the benchmark executions.

    Authors: The metric is intentionally outcome-agnostic because task success does not guarantee that every documented constraint has been exercised; the binary judgment therefore relies solely on observable evidence matching each constraint. Nevertheless, we accept that external validation against expert judgments (with and without outcome information) is needed to establish stability of the 39.90–43.98 % figures. The revision will include such a comparison on a held-out sample of trajectories. revision: yes

Circularity Check

0 steps flagged

No circularity: metric defined independently and applied to external benchmark

full rationale

The paper introduces skill coverage by defining extraction of observable constraints from skill documents and binary coverage judgments on trajectories. This definition stands alone and does not reference or depend on the SkillsBench coverage percentages. The reported 39.90–43.98% figures are the result of applying the metric, not inputs that construct it. No equations, fitted parameters, or self-citation chains reduce any claim to its own outputs. The derivation is self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that skill documents yield extractable observable constraints and that trajectories can be judged for coverage independently of task outcome.

axioms (1)
  • domain assumption Skill documents contain extractable observable behavior constraints that admit consistent binary coverage judgments from trajectories.
    Invoked in the definition of the metric and its application to SkillsBench.

pith-pipeline@v0.9.1-grok · 5710 in / 1097 out tokens · 25533 ms · 2026-06-27T13:31:22.355992+00:00 · methodology

discussion (0)

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