Lists multiple specific concrete actions: 'Identifies error-prone APIs, dangerous configurations, and footgun designs that enable security mistakes.' It also specifies concrete review targets like API designs, configuration schemas, and cryptographic library ergonomics.

Clearly answers both 'what' (identifies error-prone APIs, dangerous configurations, footgun designs) and 'when' (reviewing API designs, configuration schemas, cryptographic library ergonomics, evaluating secure-by-default principles) with an explicit 'Use when' clause and a 'Triggers' list.

Includes a rich set of natural trigger terms that users would actually say: 'footgun', 'misuse-resistant', 'secure defaults', 'API usability', 'dangerous configuration', plus contextual terms like 'pit of success' and 'cryptographic library ergonomics'. Good coverage of the domain vocabulary.

Occupies a clear niche focused on security usability and misuse-resistant design, which is distinct from general code review, general security scanning, or API design skills. The specific focus on 'footgun designs', 'pit of success', and 'secure by default' principles makes it unlikely to conflict with other skills.

The skill is fairly comprehensive and most content earns its place, but there's some verbosity in explanations (e.g., the 'Rationalizations to Reject' table is somewhat preachy, and some detection pattern lists repeat similar ideas). The core principle section and some category descriptions could be tighter. However, the code examples and tables are efficient.

The skill provides concrete, executable code examples across multiple languages (PHP, Python, Go, YAML) demonstrating specific footguns. Each sharp edge category includes detection patterns, specific questions to ask, and real code showing both dangerous and safe usage. The analysis workflow gives specific, actionable steps.

The four-phase workflow (Surface Identification, Edge Case Probing, Threat Modeling, Validate Findings) is clearly sequenced with explicit validation in Phase 4 including a feedback loop ('If a finding seems questionable, return to Phase 2'). The quality checklist at the end serves as a final validation checkpoint. Severity classification provides clear criteria for categorizing findings.

Excellent progressive disclosure with a clear overview in the main file and well-signaled one-level-deep references organized by category (crypto-apis.md, config-patterns.md, auth-patterns.md, case-studies.md) and by language (11 language-specific guides). References are clearly labeled and organized in tables for easy navigation.