ads-competitor
Competitor ad intelligence analysis across Google, Meta, LinkedIn, TikTok, and Microsoft. Analyzes competitor ad copy, creative strategy, keyword targeting, estimated spend, and identifies competitive gaps and opportunities. Use when user says competitor ads, ad spy, competitive analysis, competitor PPC, or ad intelligence.
79
75%
Does it follow best practices?
Impact
Pending
No eval scenarios have been run
Advisory
Suggest reviewing before use
Optimize this skill with Tessl
npx tessl skill review --optimize ./skills/ads-competitor/SKILL.mdQuality
Discovery
100%Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.
This is a strong skill description that clearly defines its scope across specific advertising platforms, lists concrete analytical actions, and provides explicit trigger terms. It follows the recommended pattern of 'what it does' followed by 'Use when...' with natural user language. The description is concise, uses third person voice, and would be easily distinguishable from other skills.
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Lists multiple specific concrete actions: analyzes ad copy, creative strategy, keyword targeting, estimated spend, and identifies competitive gaps and opportunities. Also specifies the platforms (Google, Meta, LinkedIn, TikTok, Microsoft). | 3 / 3 |
Completeness | Clearly answers both 'what' (analyzes competitor ad copy, creative strategy, keyword targeting, estimated spend, identifies gaps) and 'when' with an explicit 'Use when...' clause listing specific trigger phrases. | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would say: 'competitor ads', 'ad spy', 'competitive analysis', 'competitor PPC', 'ad intelligence'. These are natural phrases a user would use when seeking this capability. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused specifically on competitor ad intelligence across named ad platforms. The trigger terms like 'ad spy', 'competitor PPC', and 'ad intelligence' are very specific and unlikely to conflict with general marketing or analytics skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
50%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill provides a comprehensive competitive ad intelligence framework with good structural organization and useful analytical frameworks (messaging themes table, gap identification categories). However, it reads more like a marketing strategy guide than an actionable skill for Claude—it lacks concrete executable steps, specific URLs/tools, validation checkpoints, and could benefit significantly from splitting detailed reference content into separate files while keeping the main skill lean.
Suggestions
Add specific URLs for each ad library (e.g., https://adstransparency.google.com/) and concrete step-by-step instructions for how to research each platform, rather than describing what each platform shows.
Include a validation checkpoint after step 3 (research) to confirm findings with the user before proceeding to full analysis, and add error handling for when platforms are inaccessible or data is limited.
Move the detailed platform-specific research sections, competitive response strategies, and gap analysis frameworks into separate reference files (e.g., `ads/references/platform-research.md`, `ads/references/gap-analysis.md`) and keep the SKILL.md as a concise overview with clear pointers.
Add a concrete output template or example snippet showing what the COMPETITOR-INTELLIGENCE-REPORT.md should look like, so Claude can generate consistent, structured reports.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is reasonably well-organized but includes some content Claude would already know (e.g., explaining what Meta Ad Library shows, basic definitions of messaging themes). The tables and frameworks add value but could be tightened—several sections read more like a marketing textbook than actionable instructions for Claude. | 2 / 3 |
Actionability | The skill provides structured frameworks and checklists but lacks concrete executable steps. There are no actual URLs for the ad libraries, no code for data extraction or report generation, and the spend estimation formula is the only concrete calculation. Most guidance is directional ('research competitor ad presence') rather than specific commands or templates. | 2 / 3 |
Workflow Clarity | The 7-step process at the top provides a clear sequence, but there are no validation checkpoints or feedback loops. For a research-heavy skill that generates reports, there's no verification step to confirm data accuracy, no checkpoint to validate findings with the user before generating the full report, and no error handling for when data sources are unavailable. | 2 / 3 |
Progressive Disclosure | The skill references `ads/references/benchmarks.md` and output files, showing some awareness of file structure. However, the content is quite long and monolithic—the detailed platform-specific research sections, competitive response strategies, and gap analysis frameworks could be split into separate reference files with the SKILL.md serving as a concise overview. | 2 / 3 |
Total | 8 / 12 Passed |
Validation
90%Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.
Validation — 10 / 11 Passed
Validation for skill structure
| Criteria | Description | Result |
|---|---|---|
frontmatter_unknown_keys | Unknown frontmatter key(s) found; consider removing or moving to metadata | Warning |
Total | 10 / 11 Passed | |
- Repository
- AgriciDaniel/claude-ads
- Commit
c2ca51f
- Reviewed
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