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arxiv: 2605.10990 · v1 · submitted 2026-05-09 · 💻 cs.SE · cs.AI

Recognition: no theorem link

Skill Drift Is Contract Violation: Proactive Maintenance for LLM Agent Skill Libraries

Linfeng Fan , Yuan Tian , Ziwei Li , Zhiwu Lu
Authors on Pith no claims yet

Pith reviewed 2026-05-13 07:25 UTC · model grok-4.3

classification 💻 cs.SE cs.AI
keywords skill driftLLM agentscontract violationenvironment contractsproactive maintenanceskill librariesfalse positive reduction
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The pith

Skill drift in LLM agent libraries is contract violation, detected precisely by extracting and validating role-bearing environment assumptions from skill documents.

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

The paper claims that reusable skills for LLM agents decay silently as external services and APIs change, but existing monitors flag changes at the wrong level by watching raw values instead of the roles those values play inside a skill. It treats skill drift as a contract violation and presents SkillGuard as a system that pulls executable environment contracts out of skill documents, then checks only the assumptions that actually matter for the skill's operation against known or live conditions. This turns broad, noisy monitoring into a focused maintenance signal that eliminates false alarms in large test sets, detects real drift with high precision, and makes targeted repairs far more successful. A reader would care because agent systems increasingly depend on stable skill libraries, and unchecked drift turns reliable automation into fragile, hard-to-debug code.

Core claim

Skill drift is contract violation. SkillGuard extracts executable environment contracts from skill documents and validates only the role-bearing assumptions within them against known or live conditions, converting noisy change detection into a precision-first maintenance signal that achieves zero false alarms over 599 no-drift cases, 100 percent precision in known-drift verification, and 86 percent conservative precision on live drift across 49 real skills while raising one-round repair success from 10 percent to 78 percent.

What carries the argument

SkillGuard, which extracts executable environment contracts from skill documents and validates only role-bearing assumptions against known or live conditions.

If this is right

  • Contract-free CI probes produce 40 percent false positives while the contract-based method raises zero false alarms over 599 no-drift and hard-negative cases.
  • In known-drift verification the method reaches 100 percent precision and 76 percent recall with the strongest backbone.
  • Over 49 real skills the method discovers live drift with 86 percent conservative precision.
  • Violated contracts localize the exact assumption that failed, raising one-round repair success from 10 percent to 78 percent.
  • An 880-pair benchmark for skill degradation is released to support further evaluation.

Where Pith is reading between the lines

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

  • The contract view could be applied to other reusable components such as prompt templates or tool descriptions that also reference external state.
  • Live validation might be combined with automated rollback or version pinning to reduce manual intervention further.
  • The released benchmark could become a standard testbed for comparing drift detectors across different agent frameworks.

Load-bearing premise

Executable environment contracts can be accurately and completely extracted from existing skill documents without missing critical dependencies or misinterpreting role-bearing statements.

What would settle it

A documented skill that contains an unextracted dependency or mislabeled assumption, followed by an undetected live change in that dependency that breaks the skill's function.

Figures

Figures reproduced from arXiv: 2605.10990 by Linfeng Fan, Yuan Tian, Zhiwu Lu, Ziwei Li.

Figure 1
Figure 1. Figure 1: Skill drift is role-dependent. Raw environmental monitoring treats every changed URL, version, or configuration value as potentially relevant. SKILLGUARD instead distinguishes incidental mentions from operational obligations. This granularity explains the main empirical gap: contract-free CI probes produce 40% FPR, while SKILLGUARD raises zero false alarms over 599 no-drift and hard-negative cases. skill d… view at source ↗
Figure 2
Figure 2. Figure 2: SKILLGUARD turns skill maintenance into contract validation. The system first extracts environmental mentions from a skill, keeps only role-bearing operational obligations, validates them against known or live conditions, and uses failed contracts to localize repair. The key step is not extraction alone, but separating operational assumptions from incidental mentions before probing the environment. an envi… view at source ↗
Figure 3
Figure 3. Figure 3: Contracts change the monitoring error pro￾file. Contract-free probes detect some drift but produce high false-positive rates because they probe incidental mentions. SKILLGUARD occupies the precision-first re￾gion: 76% recall and 0% FPR on known drifts, with zero false alarms over 599 no-drift and hard-negative cases. Ca￾nary execution is an oracle-style upper bound that requires full runtime access [PITH_… view at source ↗
Figure 4
Figure 4. Figure 4: Contract violations support live maintenance, not just offline detection. (A) In a pre-registered scan of 49 real skills, SKILLGUARD flags 14 skills and achieves 86% conservative precision and 55% recall; two apparent false positives were later adjudicated as genuine drift. (B) Failed contracts localize repair: one-round repair improves from 10% without localization to 78%, matching stronger multi-round ba… view at source ↗
read the original abstract

LLM agents increasingly rely on reusable skill libraries, but these skills silently decay as the external services, packages, APIs, and configurations they reference evolve. Existing monitors detect such changes at the wrong granularity: they observe values, not the role those values play in a skill. A version string in a comment is noise; the same string in a pinned dependency is an operational obligation. We formulate skill drift as contract violation and introduce \sgname{}, which extracts executable environment contracts from skill documents and validates only those role-bearing assumptions against known or live conditions. This distinction turns noisy monitoring into a precision-first maintenance signal. Contract-free CI probes produce 40\% false positives, while \sgname{} raises zero false alarms over 599 no-drift and hard-negative cases (Wilson 95\% CI $[0,0.6]\%$). In known-drift verification, \sgname{} achieves 100\% precision and 76\% recall with the strongest backbone; in a pre-registered study over 49 real skills, it discovers live drift with 86\% conservative precision. Violated contracts also make repair actionable, improving one-round success from 10\% without localization to 78\%. We release \dbname{}, an 880-pair benchmark for skill degradation.

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 manuscript claims that skill drift in LLM agent skill libraries can be formulated as contract violation. It introduces SkillGuard, which extracts executable environment contracts from skill documents and validates only role-bearing assumptions against known or live conditions. This yields zero false positives over 599 no-drift cases (Wilson 95% CI [0, 0.6]%), 100% precision and 76% recall in known-drift verification, 86% conservative precision in a pre-registered 49-skill study, and improved one-round repair success from 10% to 78%. A benchmark of 880 skill-degradation pairs is released.

Significance. If the extraction of executable contracts proves accurate and complete, the work provides a meaningful advance in proactive maintenance for LLM agents by converting noisy value monitoring into precise, actionable signals. Credit is due for the concrete metrics (zero false positives, pre-registered study), the released benchmark supporting reproducibility, and the demonstration that violated contracts improve repair localization.

major comments (2)
  1. [§3] §3 (contract extraction): The zero false-positive rate over 599 cases and 100% precision in known-drift verification both presuppose that the LLM-mediated extraction neither invents spurious contracts nor omits critical dependencies. No ablation study, error analysis, or independent verification of extraction correctness is reported, leaving the central precision advantage ungrounded.
  2. [§4.3] §4.3 (pre-registered study): The 86% conservative precision on 49 real skills is promising, but without reporting how many contracts were extracted per skill or the distribution of missed vs. spurious contracts, it is unclear whether the result generalizes beyond the tested backbones.
minor comments (2)
  1. The abstract and introduction should explicitly define the commands or macros for SkillGuard and the benchmark dataset on first use.
  2. [Table 2] Table 2 (or equivalent results table): Clarify whether the contract-free CI baseline uses the same skill documents or a different monitoring granularity.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our work. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [§3] §3 (contract extraction): The zero false-positive rate over 599 cases and 100% precision in known-drift verification both presuppose that the LLM-mediated extraction neither invents spurious contracts nor omits critical dependencies. No ablation study, error analysis, or independent verification of extraction correctness is reported, leaving the central precision advantage ungrounded.

    Authors: The referee correctly notes the absence of a dedicated ablation study or error analysis focused on the contract extraction process. Our reported results are end-to-end evaluations of the complete SkillGuard system. The zero false-positive rate across 599 no-drift cases offers supporting evidence that the extraction did not introduce a significant number of spurious contracts, as such inventions would have manifested as false alarms during validation. Similarly, the 100% precision in known-drift tests suggests that the extracted contracts captured the relevant dependencies. Nevertheless, we concur that direct verification of extraction quality would provide stronger grounding for the precision claims. In the revised version, we will incorporate an error analysis of the extraction step, including a manual review of a subset of extracted contracts for accuracy and completeness, as well as an ablation on the impact of extraction errors. revision: yes

  2. Referee: [§4.3] §4.3 (pre-registered study): The 86% conservative precision on 49 real skills is promising, but without reporting how many contracts were extracted per skill or the distribution of missed vs. spurious contracts, it is unclear whether the result generalizes beyond the tested backbones.

    Authors: We agree that the pre-registered study would benefit from more granular reporting on contract extraction. The manuscript currently presents aggregate metrics without detailing the per-skill contract counts or breaking down the sources of imprecision into missed drifts versus spurious detections. To address this, the revised manuscript will include additional statistics on the number of contracts extracted per skill in the 49-skill study, along with an analysis of the distribution of missed and spurious contracts. This will allow readers to better assess generalizability across different LLM backbones and skill types. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results independent of inputs

full rationale

The paper defines skill drift as contract violation and presents SkillGuard as an extraction-plus-validation system evaluated on external benchmarks (599 no-drift cases, 49 real skills, known-drift verification). Reported metrics (0% false positives, 100% precision, 86% conservative precision) are direct empirical outcomes from those datasets and live checks, not obtained by fitting parameters to the target quantities or by self-referential definitions. No equations, uniqueness theorems, or ansatzes are invoked that reduce the central claim to its own inputs by construction. The extraction step is a methodological component whose accuracy is tested rather than presupposed tautologically.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim depends on the domain assumption that skill documents contain extractable, executable contracts that capture all role-bearing dependencies; no free parameters or invented entities are stated in the abstract.

axioms (1)
  • domain assumption Skill documents contain sufficient information to extract executable environment contracts that capture all role-bearing assumptions
    The extraction step in SkillGuard presupposes this property of the input documents.

pith-pipeline@v0.9.0 · 5526 in / 1252 out tokens · 51384 ms · 2026-05-13T07:25:22.224931+00:00 · methodology

discussion (0)

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