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If Claude seems to ignore a Skill, test it with prompts that clearly match its purpose, near-misses, and unrelated requests. Compare repeated responses for distinctive instructions from the Skill. This can show whether Claude behaves as though the Skill is helping, but it cannot prove that an internal selection event occurred: Anthropic describes relevance-based loading, not a universal activation log or guaranteed trigger threshold.
Why Claude may not use a Skill
Anthropic describes Skills as dynamically selected: “When you ask Claude to complete a task, it reviews available skills, loads relevant ones, and applies their instructions.” That wording describes a relevance-based process, not a promise that every loosely related prompt will use a particular Skill. The exact selection threshold is not publicly specified.
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A Skill is packaged as a folder containing a SKILL.md file. Its YAML frontmatter must include name and description. The description should explain both what the Skill does and when Claude should use it; it is the discovery information available to help Claude judge relevance. The Agent Skills overview lists a maximum description length of 1,024 characters. That is a format limit, not a recommended target or a trigger threshold. See Anthropic’s Agent Skills overview.
Check the Skill before testing
- Confirm the intended product surface. Make sure the Skill is available and enabled where you are testing it, whether that is Claude, Claude Code, or an API workflow. Do not assume a Skill configured in one surface is available in another.
- Check account and settings. Anthropic’s Help Center says Skills are available on Free, Pro, Max, Team, and Enterprise plans and that code execution must be enabled. Product support and settings can change, so verify the current interface and your account configuration in Anthropic’s “What are skills?” help article.
- Validate
SKILL.md. Check that the file is in the Skill folder, the YAML frontmatter is valid, and both required fields are present. Make sure the description names the task and the conditions for using it, rather than merely giving the Skill a broad label.
Run a repeatable prompt test
The following is a practical test method based on Anthropic’s documented relevance-based behavior, not an official Anthropic protocol. Keep the context consistent and record the exact prompt and response so you can compare runs.
- Write at least three clear positive prompts. Each should plainly ask for the task the Skill handles and include the conditions or task language named in its description.
- Add boundary prompts. Use similar vocabulary but ask for something outside the Skill’s stated use case. These near-misses help reveal whether the description distinguishes the intended task from a merely related one.
- Add unrelated negative controls. Choose ordinary requests that have no meaningful connection to the Skill. Note whether the response unexpectedly follows its distinctive instructions.
- Repeat each prompt. Use a fresh conversation or another consistent context, and run every prompt more than once. A single response that follows—or does not follow—the Skill is not conclusive.
- Record what you can observe. For each run, note the product surface, whether the Skill was enabled, the exact prompt, and whether the answer contains a distinctive step, format, or other instruction from the Skill.
| Prompt group | What to check |
|---|---|
| Clear positive | Does Claude’s response show distinctive instructions from the Skill on repeated runs? |
| Boundary | Does Claude distinguish a similar-sounding request that falls outside the stated use case? |
| Unrelated negative control | Does the Skill’s distinctive behavior appear on a request that should have no connection to it? |
Improve the description before expanding the Skill
If clear positive prompts do not produce the expected behavior, refine the description before adding more instructional text to the body. State the task, the conditions that make the Skill relevant, and the phrases a user might naturally use to request that task. A clear description gives Claude more useful discovery information; changing it is a reasonable troubleshooting step, not a guaranteed fix.
Change one factor at a time, beginning with the description, then run the same prompts again. Keeping the test set fixed makes it easier to tell whether the observed behavior changed alongside your edit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Interpret the result without overclaiming
If an answer repeatedly follows a distinctive Skill instruction on clear positive prompts—and does not do so on unrelated controls—that is useful evidence that the Skill is influencing observable behavior. It does not prove that you have seen an internal selection event. The public documentation reviewed does not establish a universal selection log or activation metric, and it does not publish a trigger-reliability benchmark.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11If results vary, report the product surface, account setting, Skill metadata, and exact test prompts when asking for help. Those details make the behavior easier to investigate without implying that a hidden-selection audit mechanism is available.
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