Publisher: Anthropic · Editorial profile by AI Tools Ratings · Review method
Not runtime tested; source-document review only. No security certification.
What task is this skill designed for?
Create a narrow skill that converts approved release notes into a consistent internal update, then compare outputs with and without the skill.
What input and output should you expect?
Input: A repeated task, representative inputs, examples of acceptable output and cases where the skill should not activate.
Output: Expected, not independently tested: a skill draft, task examples and evaluation artifacts or feedback. Numeric results are meaningful only if the underlying runs actually happened.
What does it need to run?
A Claude-compatible environment, the bundled scripts and evaluation viewer, and enough execution capacity for repeated trials. Some source workflows use subagents or background runs; those features are host-dependent and must be authorized in the actual environment.
Published by Anthropic as a Claude skill example; its repository documents Claude Code installation. Not independently installed or executed here.
How should you approach installation?
- Inspect the pinned source and evaluation helpers before running them.
- Prepare a small synthetic task set and an unchanged baseline.
- Authorize a bounded evaluation budget, review actual outputs, and expand only after useful differences appear.
Installation guide: discovery, dependencies and runtime checks
What files, accounts or data can it access?
Writes skill files and evaluation outputs and can run helpers or launch test tasks when permitted. Test examples may expose internal material, so use synthetic or approved cases for the first comparison.
What does the skill cost?
The source has no stated purchase price. Repeated model trials and agent execution can consume substantial paid usage.
What are its limits and alternatives?
A small benchmark can overfit familiar prompts. Better trigger accuracy does not prove output correctness, and one model or tool environment may not represent another. Do not label a skill validated based on a drafted test plan.
Use a short project rule for a simple always-applicable convention. Choose skill-creator when a repeatable multi-step task has enough variation to justify reusable instructions and evaluation.
What could you ask in a small trial?
Try this with a small sample:
Draft a release-update skill from these synthetic examples. Define positive and negative trigger cases, propose an evaluation against a no-skill baseline, and clearly separate planned checks from executed results.
How would you verify the result?
- Test both in-scope tasks and nearby tasks that should not trigger.
- Keep a held-out example that was not used while editing the skill.
- Save outputs and actual run conditions before reporting any improvement.
Use a baseline and record acceptance evidence before treating a successful task as a reliable workflow.
Which source and license were checked?
Read the reviewed SKILL.md at its pinned commit · Complete reviewed folder
Repository and current publisher guidance
Checked:
Apache-2.0 — identified in skills/skill-creator/LICENSE.txt at the reviewed commit. Third-party assets and dependencies may have separate terms. License evidence
Version details and review scope
Source: anthropics/skills/skills/skill-creator
Reviewed commit: dbd4588f9e1033efb41dad4bef2f7947c8993d44
SKILL.md SHA-256: dcd4803e61e913e6fc27294184cd3a71f09f5e924ff20c8a9a20173e7b3c2bcf
Source-document review. Source text and stated dependencies were inspected for this profile. Referenced code was not comprehensively audited, no runtime test was performed, and source files are not mirrored here.