Toward User Comprehension Supports for LLM Agent Skill Specifications
Pith reviewed 2026-05-21 07:27 UTC · model grok-4.3
The pith
LLM agent skill specifications should be evaluated as user-facing capability disclosures to support bounded user expectations.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central discovery is that textual cues for the four comprehension anchors are unevenly distributed across agent skill specifications, with comprehensive coverage rare, implying that users frequently lack sufficient information to form accurate expectations about skill capabilities.
What carries the argument
Rule-based coding of textual cues for four comprehension anchors in SKILL markdown files, which serves to quantify how well specifications support user comprehension of operational basis, output contract, boundary disclosure, and example capability demonstration.
If this is right
- Users selecting skills without example cues may have difficulty constructing local checks for expected behavior.
- Missing boundary disclosures could lead to unexpected skill behaviors in user contexts.
- Evaluation of agent skills needs to incorporate user comprehension metrics alongside safety audits.
- Skill creators should include all four anchors to better inform potential users.
Where Pith is reading between the lines
- Designing standardized templates that enforce the four anchors could standardize skill disclosures across platforms.
- Similar analysis could be applied to non-cybersecurity domains to see if the pattern holds.
- Integrating automated checks for these anchors into skill marketplaces might improve overall user trust.
Load-bearing premise
That the selected four comprehension anchors adequately capture what users need to form bounded expectations and that automated rule-based coding reliably detects them without significant errors or omissions.
What would settle it
Observing whether users who are shown only specifications without the four anchors can still accurately predict skill inputs, outputs, and limitations in a real usage scenario.
read the original abstract
Users often interpret and select agent skills through their SKILL markdown specifications. To protect users, existing audits mainly focus on malicious or unsafe skills. We study the complementary question of whether specifications help users form bounded expectations about what a skill consumes, produces, and covers. Across 878 cybersecurity skills, we used rule-based coding to measure textual cues for four comprehension anchors, namely operational basis, output contract, boundary disclosure, and example capability demonstration. Cues for operational basis were common, but only 19.0% of specifications exhibited cues for an example task, sample, or expected outcome, and only 2.3% exhibited cues for all four anchors. We further examined a small DNS/C2 telemetry subset (n$=$6) to illustrate why missing examples may matter. Examples appeared to make first local checks easier to construct, while no-example skills typically required helper code inspection to recover command arguments or output fields. We argue that agent-skill evaluation should treat specifications as user-facing capability disclosures, not merely as containers for executable instructions.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript reports an empirical analysis of 878 cybersecurity skill specifications for LLM agents. Using rule-based coding, the authors measure the presence of textual cues corresponding to four comprehension anchors: operational basis, output contract, boundary disclosure, and example capability demonstration. They find that operational basis cues are prevalent, but example cues appear in only 19.0% of specifications and all four anchors in just 2.3%. A qualitative examination of a small DNS/C2 subset (n=6) illustrates potential issues with missing examples. The authors conclude that skill specifications should be evaluated as user-facing capability disclosures rather than solely as executable instruction containers.
Significance. If the coding scheme proves reliable, this work supplies a useful large-sample observational baseline on the current state of skill specifications in the cybersecurity domain. The sample size of 878 strengthens the descriptive frequencies, and the reframing of specifications as capability disclosures could usefully inform design of agent skill marketplaces and auditing practices. The paper provides a clear empirical core with no free parameters or fitted models.
major comments (2)
- [Methods] Methods (rule-based coding description): The exact rule-based patterns used to detect cues for the four anchors (operational basis, output contract, boundary disclosure, example capability demonstration) are not specified, nor is inter-coder reliability or any human validation of the coding rules against direct comprehension measures reported. This is load-bearing for the central claim because the reported frequencies (19.0% for example cues, 2.3% for all four anchors) rest entirely on the untested assumption that these textual patterns reliably capture the anchors without substantial false negatives or context loss.
- [Results] Results (DNS/C2 subset): The n=6 DNS/C2 illustration is presented as post-hoc qualitative support for why missing examples matter, but its small size and lack of systematic sampling prevent it from validating the anchors or demonstrating causal effects on user expectation formation. This weakens the bridge from the observational frequencies to the recommendation that specifications be treated as capability disclosures.
minor comments (2)
- [Abstract] Abstract: The abstract could explicitly name the domain (cybersecurity skills) and total sample size earlier for immediate clarity.
- [Methods] The manuscript would benefit from an appendix or supplementary table listing the precise textual cue patterns used in the rule-based coding to allow replication.
Simulated Author's Rebuttal
We thank the referee for their constructive feedback on our manuscript. We address each major comment below, indicating planned revisions where appropriate.
read point-by-point responses
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Referee: [Methods] Methods (rule-based coding description): The exact rule-based patterns used to detect cues for the four anchors (operational basis, output contract, boundary disclosure, example capability demonstration) are not specified, nor is inter-coder reliability or any human validation of the coding rules against direct comprehension measures reported. This is load-bearing for the central claim because the reported frequencies (19.0% for example cues, 2.3% for all four anchors) rest entirely on the untested assumption that these textual patterns reliably capture the anchors without substantial false negatives or context loss.
Authors: We agree that the specific rule-based patterns should be provided to support reproducibility. In the revised manuscript we will add an appendix containing the exact keyword lists, regular expressions, and decision logic used to detect each of the four anchors. Because the procedure is fully deterministic and rule-based, conventional inter-coder reliability statistics are not applicable; we will nevertheless document the iterative development and spot-checking of the rules on a held-out sample. We acknowledge that the study does not include direct human validation against comprehension measures; this was outside the scope of the observational baseline we set out to establish. We will state this limitation explicitly and identify user studies that map textual cues to actual expectation formation as valuable future work. revision: partial
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Referee: [Results] Results (DNS/C2 subset): The n=6 DNS/C2 illustration is presented as post-hoc qualitative support for why missing examples matter, but its small size and lack of systematic sampling prevent it from validating the anchors or demonstrating causal effects on user expectation formation. This weakens the bridge from the observational frequencies to the recommendation that specifications be treated as capability disclosures.
Authors: We agree that the DNS/C2 examination (n=6) is small, post-hoc, and illustrative only. Its role in the paper is to supply concrete, domain-specific examples of how the absence of example cues can affect practical inspection, not to validate the anchors or demonstrate causality. We will revise the relevant section to emphasize its limited, qualitative purpose and to avoid any implication that it independently supports the broader recommendation. The primary empirical contribution and the argument for treating specifications as capability disclosures rest on the frequencies observed across the full set of 878 specifications. revision: yes
Circularity Check
No significant circularity in empirical measurement study
full rationale
The paper conducts a direct empirical study by applying rule-based coding to detect the presence of textual cues for four author-defined comprehension anchors across a dataset of 878 cybersecurity skill specifications. Reported statistics such as 19.0% exhibiting example cues and 2.3% exhibiting all four anchors are straightforward frequency counts from this coding scheme applied to the source texts. The n=6 DNS/C2 subset is presented only as an illustration of potential implications. No equations, fitted parameters, predictive derivations, self-citations, or uniqueness theorems appear in the provided text that would reduce these measurements to prior inputs by construction. The analysis is self-contained as an observational coding exercise.
Axiom & Free-Parameter Ledger
axioms (1)
- domain assumption The four comprehension anchors (operational basis, output contract, boundary disclosure, example capability demonstration) are the appropriate set for assessing whether specifications help users form bounded expectations.
Reference graph
Works this paper leans on
-
[1]
Bharathi Donku, Shahriar Rahman Khan, Tariqul Islam, and Raiful Hasan. 2025. Discrepancies in Mobile App Permissions: Exploring Transparency and User Awareness in the Android Ecosystem. InCHI EA. 1–8. doi:10.1145/3706599.3719902
- [2]
- [3]
-
[4]
Mahipal Jangra. 2026. Anthropic Cybersecurity Skills.https://github. com/mukul975/Anthropic-Cybersecurity-Skills
work page 2026
-
[5]
Eyad Kelleh. 2026. Awesome Claude Skills Security.https://github. com/Eyadkelleh/awesome-claude-skills-security
work page 2026
-
[6]
Patrick Gage Kelley, Joanna Bresee, Lorrie Faith Cranor, and Robert W. Reeder. 2009. A "Nutrition Label" for Privacy. InSOUPS. 1–12. doi:10.1145/1572532.1572538
-
[7]
Ishika Keswani, Kerick Walker, Adrian Clement, Eusila Kitur, Nanna- pas Wonghirundacha, Ryan Aubrey, Vivien Song, and Eleanor Birrell
-
[8]
User Understandings of Technical Terms in App Privacy Labels. InSOUPS. 279–298.https://www.usenix.org/conference/soups2025/ presentation/keswani
-
[9]
Frederic Lardinois. 2025. Agent Skills: Anthropic’s Next Bid to Define AI Standards.https://thenewstack.io/agent-skills-anthropics-next- bid-to-define-ai-standards/
work page 2025
-
[10]
Eric Olsson, Benjamin Eriksson, Pablo Picazo-Sanchez, Lukas An- dersson, and Andrei Sabelfeld. 2024. FakeX: A Framework for De- tecting Fake Reviews of Browser Extensions. InASIA CCS. 769–784. doi:10.1145/3634737.3656999
-
[11]
Rahul Pandita, Xusheng Xiao, Wei Yang, William Enck, and Tao Xie
-
[12]
WHYPER: Towards Automating Risk Assessment of Mobile Applications. InUSENIX Security. 527–542.https://www.usenix.org/ system/files/conference/usenixsecurity13/sec13-paper_pandita.pdf
-
[13]
Mark Pors. 2026. skill-audit.https://github.com/pors/skill-audit
work page 2026
-
[14]
Zhengyang Qu, Vaibhav Rastogi, Xinyi Zhang, Yan Chen, Tiantian Zhu, and Zhong Chen. 2014. AutoCog: Measuring the Description- to-permission Fidelity in Android Applications. InCCS. 1354–1365. doi:10.1145/2660267.2660287
-
[15]
Alireza Rezvani. 2026. Claude Skills.https://github.com/ alirezarezvani/claude-skills
work page 2026
-
[16]
SaFo-Lab. 2026. DynAuditClaw.https://github.com/SaFo-Lab/ DynAuditClaw
work page 2026
-
[17]
Durity, and Lorrie Faith Cranor
Florian Schaub, Rebecca Balebako, Adam L. Durity, and Lorrie Faith Cranor. 2015. A Design Space for Effective Privacy Notices. InSOUPS. 1–17.https://www.usenix.org/system/files/conference/soups2015/ soups15-paper-schaub.pdf
work page 2015
-
[18]
Faysal Hossain Shezan, Kaiming Cheng, Zhen Zhang, Yinzhi Cao, and Yuan Tian. 2020. TKPERM: Cross-platform Permission Knowledge Transfer to Detect Overprivileged Third-party Applications. InNDSS. doi:10.14722/ndss.2020.24287
-
[19]
Trail of Bits. 2026. Skills.https://github.com/trailofbits/skills
work page 2026
-
[20]
Transilience. 2026. Community Tools.https://github.com/ transilienceai/communitytools
work page 2026
-
[21]
Takuya Watanabe, Mitsuaki Akiyama, Tetsuya Sakai, Hironori Washizaki, and Tatsuya Mori. 2015. Understanding the Inconsisten- cies between Text Descriptions and the Use of Privacy-sensitive Re- sources of Mobile Apps. InSOUPS.https://www.usenix.org/system/ files/conference/soups2015/soups15-paper-watanabe.pdf
work page 2015
-
[22]
Haiyue Zhang. 2026. Agent Audit: Static Security Analysis for AI Agent Applications.https://github.com/HeadyZhang/agent-audit
work page 2026
-
[23]
Shikun Zhang, Lily Klucinec, Kyerra Norton, Norman Sadeh, and Lor- rie Faith Cranor. 2024. Exploring Expandable-Grid Designs to Make iOS App Privacy Labels More Usable. InSOUPS. 139–157.https: //www.usenix.org/conference/soups2024/presentation/zhang
work page 2024
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