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Debbie O’Brien says she used Skill Creator v2 in VS Code to review and revise her existing README Wizard agent skill, then compare the revised version with its baseline across four evaluations. She reports two wins and two ties, with the overall score rising from 81 to 97.5. That is a useful example of a testing workflow—not evidence that the tool will produce the same gains for other skills.
What O’Brien says she did
In her March 21, 2026, DEV Community account, O’Brien says she found Skill Creator through skills.sh, installed it, inspected its contents, and applied it to her README Wizard skill. Her account describes a sequence of review, focused instruction edits, added evaluation cases, and comparison runs. The account does not establish that every installation path or host works the same way; tool versions and compatibility can change.
Review the existing skill for specific gaps
O’Brien says the review surfaced underspecified workflow steps, limited README guidance, missing cases for projects with or without an existing README, overgeneralized sections, too few evaluations, and weak project detection. These were findings about her skill, not a universal checklist of flaws in agent skills.
Make focused changes and add cases
She describes editing the instructions and adding evaluations for a minimal project and a badge-focused README request. The assertions checked for elements such as a project description, usage guidance, relevant badges, and structure suited to the project or request. In practice, this kind of case turns a broad instruction such as “write a useful README” into observable criteria that can be checked against output.
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How the baseline comparison worked
O’Brien says she ran the baseline and revised skill in parallel against the test cases, graded the results against assertions, reviewed a benchmark summary, and used an HTML viewer to inspect outputs and review results. Those details describe her reported run; the cited account does not independently replicate it.
To judge a revision more carefully, compare both versions on the same prompts and assertions. Check whether the cases represent realistic requests, whether scoring captures meaningful quality rather than only checklist completion, and whether a person reviewing side-by-side outputs agrees with the grades. A good result on a few authored cases is evidence about those cases; it is not, by itself, proof of broad reliability.
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What the reported scores do—and do not—show
O’Brien reports that the revised skill beat the baseline on two of four evaluations and tied on the other two. She gives an overall score change from 81 to 97.5. The source calls that a 15.7% improvement, but the percentage does not match the ordinary calculation from those scores: the increase is 16.5 points, or about 20.4% relative to 81. The account does not explain how it derived 15.7%, so that figure should not be treated as verified by the scores.
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The example concerns one author’s README skill and four evaluation cases. It does not establish that Skill Creator v2 improves other skills by a similar amount, that four cases are enough for robust evaluation, or that benchmark scores predict outcomes for real users.
What the description-trigger check adds
O’Brien also says the workflow generated positive queries that should trigger the skill and negative queries that should not. She could review and edit those queries, export them, and use them in an optimization loop. This offers a way to examine whether a skill description routes plausible requests appropriately; it does not prove that every supported agent will trigger the skill reliably.
When reviewing trigger behavior, look at both sides: requests that should activate the skill and nearby requests that should be handled without it. Inspect the queries and results rather than treating an optimization score as a guarantee of correct routing in every context.
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How this relates to official Codex skill guidance
OpenAI’s Codex documentation describes a skill as a directory with a SKILL.md file containing YAML front matter and Markdown instructions, with optional supporting resources. Its evaluation article identifies $skill-creator as Codex’s built-in creator invocation. OpenAI’s general skills documentation likewise describes reusable instructions and optional references, scripts, and assets, and warns that skills can introduce instruction-security risks, so they should be reviewed before being made available to an agent.
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A practical way to tell whether your revision helped
- Use matched tests: Run the old and revised versions on identical prompts and evaluate them against the same criteria.
- Test meaningful coverage: Include ordinary requests, edge cases, and relevant project conditions; do not assume a small test set represents all users.
- Inspect quality as well as scores: Review generated outputs and whether each assertion measures something that matters to the task.
- Check routing separately: Include both positive trigger examples and negative examples that should not activate the skill.
- Look for repeatability: Treat a single demo run as preliminary evidence. More varied cases and repeated checks can show whether an apparent gain holds beyond the original examples.
O’Brien’s account provides a concrete illustration of reviewing a skill, adding targeted tests, comparing versions, and examining trigger queries. The strength of any conclusion still depends on the quality and breadth of the tests, and the reported result should remain attached to her one-skill demonstration.
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