Debbie O’Brien’s account of using Skill Creator v2 in VS Code describes a practical loop for revising an existing agent skill: review its instructions, make targeted edits, add evaluation cases, compare the revised skill with a baseline, and inspect the outputs. In her four-case README Wizard demo, she reports scores rising from 81 to 97.5, with two wins and two ties. That is a useful example of how to test a revision, not evidence that the tool will improve other skills or that the score predicts real-world results.
What O’Brien says she did in VS Code
In her March 21, 2026, DEV Community article, Debbie O’Brien says she found Skill Creator through skills.sh, installed it, inspected its contents, and used it to improve her existing README Wizard skill. Her account describes one author’s workflow and results; it is not an independently replicated test or a universal product specification.
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She reports that the review identified several weak spots in the skill: workflow steps that were not sufficiently specified, limited guidance for generating a README, missing handling for projects with an existing README as well as those without one, sections that were too general, few evaluation cases, and weak project detection. She then describes making targeted instruction changes and adding test cases.
How the evaluation loop worked
Add cases that exercise different requests
O’Brien says she added a minimal-project case and a badge-focused README request. The cases included assertions for expected qualities such as a project description, usage guidance, relevant badges, and structure suited to the project context. Assertions make an evaluation more concrete than asking whether an output simply “looks better”: they give the grader specific criteria to check.
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Compare baseline and revised versions
Her account describes running the original and revised skill in parallel across the test cases, grading the outputs against the assertions, and reviewing a benchmark summary. She also describes an HTML viewer for inspecting generated outputs and review results side by side. This makes the comparison easier to examine, but the account does not establish a standardized or complete evaluation methodology.
Inspect whether the skill description triggers appropriately
O’Brien also describes an optimization workflow for the skill description. It generated positive queries intended to trigger the skill and negative queries intended not to trigger it; she could review, edit, export, and use those queries in an optimization loop. This is a way to probe routing behavior. It is not proof that every supported agent will recognize the skill reliably, or that the same results transfer across hosts.
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What the reported scores do—and do not—show
O’Brien reports that the revised README Wizard scored 97.5, compared with 81 for the baseline. Across four evaluation cases, she says the revision won on two and tied on two. These figures are her reported results for that demonstration, not independent measurements.
The Tool Desk
Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Her article calls the change a 15.7% improvement, but that percentage does not follow from the two scores under the ordinary relative-change calculation. The difference is 16.5 points; relative to the baseline score of 81, that is about 20.4%. The article does not explain the basis for its 15.7% figure, so it should not be treated as a percentage verified by the score arithmetic.
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Four cases on one README skill cannot establish that Skill Creator v2 will improve other skills by a similar amount, that four cases are enough to cover a skill’s real use, or that evaluation scores predict what users will experience. A benchmark is most useful here as evidence about the tested prompts and criteria, not as a general performance guarantee.
How to judge whether your own skill revision helped
O’Brien’s account suggests a useful comparison pattern, but does not validate it as a complete standard. To make your own result more informative, keep the comparison controlled and inspect the evidence behind the score.
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- Use the same prompts for both versions. Compare the baseline and revision against identical cases so that changes in the test set do not explain the difference.
- Write criteria that can be checked. Assertions should describe observable requirements, such as whether an output includes project-specific usage guidance, rather than relying only on a vague overall impression.
- Cover realistic variation. Include more than one kind of request and relevant project conditions—for example, the minimal-project and badge-focused cases O’Brien reports, along with cases that reflect the skill’s actual intended use.
- Review outputs, not just aggregate scores. A score can hide a regression in one case or a weak answer that happens to satisfy a narrow assertion. Inspecting outputs side by side helps identify what changed.
- Test both triggering and non-triggering. Check that relevant requests activate the skill and that unrelated requests do not. Generated queries are test material to review, not proof of reliable routing on their own.
- Check repeatability and coverage. A result limited to a few demo cases is a starting point. Confidence depends on whether the improvement holds across a broader set of representative requests.
How this relates to official Codex skill guidance
Codex documentation describes skills as reusable agent instructions with a SKILL.md manifest and optional supporting resources. The Codex evaluation article describes the file as Markdown instructions with YAML front matter and identifies $skill-creator as Codex’s built-in creator invocation. OpenAI’s broader skills documentation also advises reviewing skills before making them available to an agent because they can introduce instruction-security risks.
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This is related context, not a description of Skill Creator v2’s installation or exact operation. O’Brien’s article discusses a third-party tool and a folder containing instructions, helper agents, references, schemas, scripts, and review tooling. Tool versions, installation steps, and host compatibility can change; confirm current instructions with the tool’s own listing before following a setup procedure.
Quick Recap
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