Lists multiple concrete actions: 'verifies factual claims', 'using web search and official sources', 'proposes corrections with user confirmation'. Also specifies supported content types: 'AI model specs, technical documentation, statistics, and general factual statements'.

Clearly answers both 'what' (verifies factual claims using web search, proposes corrections) and 'when' (explicit 'Use when...' clause listing multiple trigger scenarios). Also specifies scope of supported document types.

Includes strong natural trigger terms users would actually say: 'fact-check', 'verify information', 'validate claims', 'check accuracy', 'update outdated information'. These cover common variations of how users would phrase such requests.

The combination of fact-checking, web search verification, and correction proposal creates a clear niche. The specific trigger terms like 'fact-check', 'validate claims', and 'check accuracy' are distinct and unlikely to conflict with general document editing or search skills.

The skill is extremely verbose at ~200+ lines. It explains concepts Claude already knows (what types of sources are authoritative, how to construct search queries, what 'good' vs 'poor' queries look like). The 'When to use' trigger phrases, source evaluation hierarchy, and handling ambiguity sections all explain things Claude inherently understands. The examples section adds three examples that are essentially abbreviated restatements of the workflow. The 'Limitations' section tells Claude it can't verify opinions or access paywalled content—things it already knows.

The workflow steps are reasonably concrete with a comparison table format, status codes, and a report template. However, the Python Edit() example is pseudocode-like (not a real executable API call), and the search strategy is descriptive rather than providing exact tool invocations. The skill describes what to do conceptually but lacks precise tool usage patterns for web search.

The 5-step workflow is clearly sequenced with a progress checklist, explicit validation checkpoints (user approval before applying changes, verification after edits), and a feedback loop (show report → get approval → apply → verify). The quality checklist at the end provides additional verification. The requirement to wait for user confirmation before destructive edits is well-handled.

The entire skill is a monolithic wall of text with no references to external files. Content like search best practices, source evaluation guidelines, citation formats, and detailed examples could easily be split into separate reference files. Everything is inline, making the skill unnecessarily long for the SKILL.md overview level. No bundle files are provided to support progressive disclosure.