Your Claude Code skill triggers on the wrong prompts because the description is broad enough to match inputs you did not intend. Three specific causes: the description uses vocabulary that appears in unrelated task types, the description has no exclusion statements that rule out near-miss inputs, and the trigger scope was never tested against non-target prompts. Missing exclusions are the cause in the majority of over-triggering cases AEM diagnoses. Over-triggering is a specification failure, not a model failure.

TL;DR: The fix for most over-triggering is adding 1-2 explicit exclusion statements to your description ("not for X", "only when Y"). This resolves the problem without requiring a full description rewrite and takes about 5 minutes. The diagnosis — identifying which inputs are triggering incorrectly and why — takes longer. Here is the process.

What causes a skill to fire on unintended inputs?

Claude's skill discovery mechanism matches user intent against skill descriptions using semantic similarity, not keyword matching. A description reading "Use this skill to analyze customer feedback" will trigger not just on "analyze this customer feedback" but also on "look at these support tickets," which shares the same semantic intent of reviewing user-sourced text.

Three root causes of over-triggering:

  • Vocabulary overlap: Generic action words (analyze, review, generate, write, create) appear across many skill descriptions and match a wide range of prompts. A description relying on a generic verb without scoping it to a specific domain or output type is underspecified.
  • Missing exclusion language: Without negative trigger statements, Claude has no instruction to not use the skill for near-miss inputs. The description specifies when to trigger but says nothing about when not to. Claude defaults to triggering when there is ambiguity.
  • Untested scope: Most over-triggering goes unnoticed until the wrong output appears in the wrong context. Descriptions that have never been tested against non-target inputs have unknown false-positive rates.

Production testing of semantic similarity routing at default 0.7 thresholds found false positive rates reaching 99% for some embedding models before any exclusion tuning was applied (InfoQ, 2024). Claude holds all installed skill descriptions as metadata in the system prompt simultaneously, at approximately 100 tokens per skill (Anthropic, 2025), meaning over-broad descriptions compete for activation on every prompt across all installed skills.

"The failure mode isn't that the model is bad at the task — it's that the task wasn't specified tightly enough. Almost every production failure traces back to an ambiguous instruction." — Simon Willison, creator of Datasette and llm CLI (2024)

How do you identify which part of the description is causing the problem?

You identify the problem section by comparing each false-positive prompt to your description and finding which words appear in both. The overlapping terms are the over-broad elements. In most cases, the gap is missing exclusion language rather than wrong vocabulary. The three-step process below takes 10-15 minutes for a skill with clear false-positive patterns.

Three-step diagnosis:

  1. Collect the false-positive prompts: Write down the specific prompts that triggered your skill incorrectly. Three or more examples give you enough to work with.
  2. Identify the overlapping terms: Compare each false-positive prompt to your skill description. Which words in the description also appear in the false-positive prompts? Those are the over-broad terms.
  3. Check for exclusion language: Look for "not for", "only when", "does not apply to", or similar phrases in your current description. If none exist, that is the gap.

For example: if your description says "Use this skill to review pull requests for style issues" and it triggers on "review this design doc for style issues," the over-broad term is "review" combined with "style." The fix is to add scope ("only for code files") or add an exclusion ("not for design documents, Figma files, or written content"). The difficulty of this diagnosis reflects a broader pattern: benchmark research shows that even the strongest LLMs achieve below 50% accuracy on multi-step tool-selection tasks requiring dependent tool use, with the best-performing model reaching only 61.83% (ToolComp benchmark, OpenReview 2024).

How do negative triggers resolve over-triggering?

Negative triggers are explicit statements in the description that tell Claude when NOT to use the skill. They are the most direct fix for over-triggering because they give the classifier a discriminating signal it currently lacks. Adding one targeted exclusion statement eliminates the false-positive category that caused the problem.

Negative trigger formats that eliminate false positives:

  • "Not for [specific adjacent task that currently false-positives]"
  • "Only use when the user explicitly mentions [specific signal]"
  • "Does not apply to [category you want excluded]"
  • "Skip this skill if [condition indicating a different skill should handle it]"

The description field has a 1,024-character limit (Anthropic, 2025), so negative triggers must be concise. One sentence per exclusion is sufficient. Two to three exclusion statements cover most over-triggering cases without consuming excessive description space. Research on semantic tool routing shows that high-quality, scoped descriptions reduce routing errors by up to 86.4% compared to generic descriptions (vllm-semantic-router.com, 2024).

For a deeper look at how negative trigger language works in description design, see What Are Negative Triggers and Why Should I Include Them in the Description?.

How do you test trigger scope before the skill goes into production?

In our trigger testing protocol, every skill description is tested against 20 inputs before it ships to users: 10 target inputs that should trigger the skill and 10 non-target inputs that should not (AEM internal protocol, 2026). We track four outcomes:

  • True positives: target inputs that trigger correctly
  • False positives: non-target inputs that trigger incorrectly
  • True negatives: non-target inputs that correctly do not trigger
  • False negatives: target inputs that do not trigger when they should

A skill with any false positives among the 10 non-target inputs gets its description revised before shipping. The revision cycle is: identify the overlapping term, add an exclusion statement, retest. Most skills need 1-2 revision cycles. Anthropic's best-practices documentation recommends creating at least three test evaluations per skill before considering it production-ready (Anthropic, 2025). For context on what good looks like: even the best threshold-tuned semantic similarity systems in production still show false positive rates of 13.4% without architectural exclusion controls (InfoQ, 2024), which is why AEM's protocol adds explicit exclusion language rather than relying on threshold tuning alone.

This article covers over-triggering (firing too broadly). For the opposite problem — a skill that never fires when it should — see My Skill Produces Great Output but Never Activates Automatically.

FAQ

Over-triggering in Claude Code skills is almost always a description problem, not a model problem. The description field is the only specification Claude has for when to activate a skill. Every question below addresses a specific failure pattern in that specification: missing exclusion language, untested scope, or overcorrection when adding exclusions.

Is over-triggering always a description problem?

Almost always, yes. In rare cases, over-triggering is caused by the skill name containing words that appear in unrelated task types. But the description is the primary trigger signal. Diagnose the description first.

Can I fix over-triggering without changing the description?

Not reliably. The description is the specification for when Claude uses the skill. Without changing it, you are asking Claude to make distinctions it has no instruction to make.

How many exclusion statements is too many?

The 1,024-character description limit is the practical ceiling. In practice, 2-3 well-targeted exclusion statements resolve most over-triggering. If you need more than 5, the description may be covering a task scope too broad for one skill — consider splitting into two skills with more specific scopes.

What if fixing the over-triggering causes the skill to undertrigger instead?

This is the common overcorrection: adding too many exclusions that rule out legitimate trigger cases. Test against both target and non-target inputs after each revision. If a negative trigger accidentally excludes a target case, soften the exclusion or reframe it as a qualifier ("only skip if...") rather than a blanket exclusion.

Will adding more description text always improve trigger precision?

No. More text increases the chance that some portion of the description matches unrelated inputs. Precise, scoped language reduces false positives more reliably than comprehensive language. A 2-sentence description that names the exact task and one exclusion outperforms a 5-sentence description trying to cover all cases.

Is this related to skills conflicting with each other?

Yes. When two skills both over-trigger on the same input, they compete for activation. The result is unpredictable: Claude picks one, and the selection can vary across sessions. For diagnosing multi-skill conflicts, see Why Isn't My Claude Code Skill Working?.

Last updated: 2026-04-19