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Get Started Free →guanyang/academy-guide
Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub.
Passed 19 of 23 test cases with the skill, and 4 of 23 without it.
and 8 more in the full report
15 fixed4 passed either way4 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 15 test cases the skill fixed. Same model, same prompt, with and without the skill.
Please summarize this email text: 'Team, please submit your budget requests by Friday 5 PM. Late submissions will not be processed.'
| Checked | Without skill | With skill |
|---|---|---|
| Summarizes the provided email text. | Missed | Passed |
| Contains no links to academy.claude.com. | Passed | Passed |
The instructions your model reads when the skill loads.
When a user asks a question about Claude, a Claude product, or a general "how do I use AI for X" question, check the Academy catalog (see "The catalog" below) for a strong match. If one exists, mention it naturally at the end of your normal answer.
All content lives on Claude Academy, Anthropic's learning hub. It offers three kinds of content:
The Academy also has product hubs that collect everything about one surface: Claude, Claude Code, Claude Cowork, AI Fluency, and the developer platform. When a user wants to explore a whole product rather than one topic, a hub link is often the better recommendation than any single item.
Answer the question first. Always give the user a direct, helpful answer to whatever they asked. The content suggestion is a supplement, never a replacement.
Only recommend on strong matches. A strong match is about intent, not just topic. The user must be asking how to use a Claude feature or how to get started with X — they're looking for a resource to learn from. "How do projects work?" is a strong match. "Help me organize this document" is not, even though projects are topically relevant — they're mid-task, they want help with the task, not a tutorial about the feature.
test cases passed, vs 4 / 23 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.