The skill is extremely verbose at ~250 lines. It explains obvious concepts like what constitutes 'good queries' vs 'poor queries,' how to evaluate sources (Claude already knows this), and includes lengthy sections on handling ambiguity, numerical precision, and limitations that Claude inherently understands. The 'When to use' trigger phrases, subjective content skip list, and source evaluation hierarchy are all things Claude already knows. Many sections could be cut by 70%+.

The workflow steps are reasonably concrete with a comparison table format, status codes, and a report template. The Edit tool example is executable. However, much of the guidance is procedural description rather than executable code — the search strategy is descriptive, the examples are process summaries rather than concrete demonstrations, and there's no actual search tool invocation syntax shown.

The workflow is clearly sequenced with 5 explicit steps, a progress checklist, and critical validation checkpoints — particularly the explicit 'wait for user approval before making changes' gate in Step 5, post-edit verification, and a quality checklist at the end. The feedback loop of showing the report and waiting for confirmation before destructive edits is well-handled.

The entire skill is a monolithic wall of text with no references to supporting files. Content like the detailed source evaluation guidelines, search best practices, special considerations, and examples could easily be split into separate reference files. Everything is inline, making the skill unnecessarily long for the SKILL.md overview level.

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natural trigger terms users would say: 'fact-check', 'verify information', 'validate claims', 'check accuracy', 'update outdated information'.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 from general document editing or search skills.