There is a markdown file in your repo that tells the agent how the job is done. A benchmark just checked whether eight agents actually follow one.
SkillForgeBench (arXiv 2610.14918, v1, 10 October 2026) builds 842 sandboxed tasks across five professional domains, uses agent skills as the execution blueprint, and scores each task against fine-grained completion and process rubrics. In the authors' own numbers:
1. Strict completion — every requirement satisfied — runs from 12.6% to 40.9% across eight agents, led by Opus 4.8 at 40.9%. 2. Score the rubrics individually and the same agents pass 70.0% to 85.7%. Requiring all of them at once is what collapses the figure. 3. Process rubrics are satisfied far less often than completion rubrics — under 3% for Haiku 4.5. The record of how the work was done is the weakest artefact in the run. 4. Skills help unevenly: +19.2 points for Opus 4.8 against +5.8 for Haiku 4.5. External guidance rewarded the agent that needed it least.
Point 3 is the one with a compliance shape, and the reflex is the wrong article. AI Act Article 14 human oversight is what you reach for, and the Digital Omnibus — Regulation (EU) 2026/1744, in force 27 July 2026 — moved Annex III to 2 December 2027. Article 50 transparency has applied since 2 August 2026, with machine-readable marking from 2 December 2026, but that is a duty to declare the machine, not to inspect its procedure.
The limb already phrased as a process duty is not in the AI Act at all. GDPR Article 32(1)(d) requires a process for regularly testing, assessing and evaluating the effectiveness of your measures, and Article 5(2) requires you to be able to demonstrate it. Where your agent touches personal data, a completion-only gate leaves you nothing to demonstrate with. Where it does not, no duty in force reaches the procedure — so the trace is a control you build or simply do not have.
A skill file is a claim about how the work will go. It is not evidence that it did.
What does your acceptance gate record — the result, or the path?
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