ads-competitor
Competitor ad intelligence analysis across Google, Meta, LinkedIn, TikTok, Microsoft, and Apple Ads. 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.
73
67%
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 via a 'Use when' clause. It follows third-person voice throughout and is concise without unnecessary padding. The description would effectively help Claude distinguish this skill from other marketing or analytics 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 across named platforms. | 3 / 3 |
Completeness | Clearly answers both 'what' (competitor ad intelligence analysis across platforms, analyzing copy/strategy/targeting/spend, identifying gaps) and 'when' with an explicit 'Use when' clause listing trigger terms. | 3 / 3 |
Trigger Term Quality | Includes strong natural trigger terms users would say: 'competitor ads', 'ad spy', 'competitive analysis', 'competitor PPC', 'ad intelligence'. Also names specific platforms (Google, Meta, LinkedIn, TikTok, Microsoft, Apple Ads) which users would mention. | 3 / 3 |
Distinctiveness Conflict Risk | Highly distinctive niche focused specifically on competitor ad intelligence across paid advertising platforms. The combination of 'competitor', 'ad spy', 'PPC', and 'ad intelligence' creates a clear, non-overlapping domain unlikely to conflict with general marketing or analytics skills. | 3 / 3 |
Total | 12 / 12 Passed |
Implementation
35%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill reads more like a comprehensive reference guide or wiki article than a concise, actionable skill for Claude. Its strengths are thorough coverage of platforms and a structured analytical framework, but it's significantly over-length with content that should be split into reference files. The lack of validation checkpoints and executable examples weakens its utility as an operational guide.
Suggestions
Move platform-specific research details, 2025-2026 platform updates, and competitive response strategies into separate reference files (e.g., ads/references/platform-details.md, ads/references/platform-updates.md) and link to them from the main skill.
Remove explanations of what each ad library shows—Claude can look these up—and instead focus on specific analytical steps and decision criteria.
Add validation checkpoints to the workflow, such as 'Verify data from at least 2 sources before estimating spend' or 'Confirm competitor identification with user before deep analysis.'
Include a concrete example of a completed competitor analysis snippet (even abbreviated) showing expected output format, rather than just listing deliverable file names.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~200+ lines. It explains platform basics Claude already knows (what Meta Ad Library shows, what LinkedIn Ad Library is), includes extensive tables and lists that read more like a reference document than actionable instructions, and contains time-sensitive platform updates that bloat the content significantly. Much of this could be offloaded to reference files. | 1 / 3 |
Actionability | The skill provides structured frameworks (ad copy analysis checklist, messaging themes table, spend estimation formulas) which are somewhat actionable, but most guidance is descriptive rather than executable. There are no concrete code examples, API calls, or copy-paste-ready commands. The spend estimation formula is the closest thing to executable guidance, but it's minimal. | 2 / 3 |
Workflow Clarity | The 7-step process at the top provides a clear sequence, but there are no validation checkpoints, no feedback loops for verifying data quality or completeness, and no explicit criteria for when to proceed between steps. For an analysis workflow that could produce misleading conclusions from incomplete data, validation steps are important. | 2 / 3 |
Progressive Disclosure | The skill references two external files (benchmarks.md and mcp-integration.md) which is good, but the vast majority of content that should be in reference files (platform-specific research details, 2025-2026 platform updates, competitive response strategies) is inlined. The platform updates section alone could be a separate reference file, and the detailed framework sections could be split out. | 2 / 3 |
Total | 7 / 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
402ba63
- Reviewed
Table of Contents
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