Names the domain (Reddit Ads API) and lists some areas (campaigns, targeting, conversions, agentic optimization), but these are high-level categories rather than concrete actions. No verbs describing what the skill actually does (e.g., 'create campaigns', 'configure targeting rules', 'track conversions').

Only partially addresses 'what' (listing topic areas without concrete actions) and completely lacks a 'when' clause or any explicit trigger guidance. The missing 'Use when...' clause would cap this at 2 per the rubric, but the 'what' is also weak, so it scores 1.

Includes relevant keywords like 'Reddit Ads', 'campaigns', 'targeting', 'conversions' that users might naturally say, but misses common variations like 'ad groups', 'Reddit advertising', 'ad spend', 'ROAS', 'Reddit marketing', or file/API-specific terms.

'Reddit Ads API' is fairly distinctive and unlikely to conflict with generic advertising or other platform-specific ad skills. However, terms like 'campaigns', 'targeting', and 'conversions' are generic enough to potentially overlap with other ad platform skills (Google Ads, Facebook Ads, etc.).

Extremely verbose at ~1500+ lines. Provides full client implementations in both Python and TypeScript (duplicated), full OAuth flows in both languages, complete API endpoint tables Claude could infer from docs, and massive agentic optimizer code in both languages. The dual-language duplication alone doubles the token cost with minimal added value. Concepts like OAuth2 flows, REST API patterns, and basic CRUD operations don't need this level of explanation for Claude.

The code examples are fully executable and copy-paste ready. Complete client implementations, OAuth flows, campaign creation examples, conversion tracking, and the full agentic optimizer service are all concrete and runnable with proper credentials.

The complete workflow example shows a clear campaign creation sequence (create campaign → create ad group → create ad), and the agentic optimizer has a clear cycle (fetch → analyze → execute → log). However, there are no explicit validation checkpoints — the campaign is created without verifying success before proceeding to ad group creation, and the optimizer executes changes without confirmation or rollback mechanisms for destructive operations like pausing ads or scaling budgets.

This is a monolithic wall of content with no bundle files to offload detail into. The full client implementations, both language variants, complete optimizer services, multi-agent architecture, Docker deployment, and mock testing code are all inline. The API endpoint reference tables, auth flows, and optimizer code should each be in separate referenced files. There are no references to external files within the bundle.