ad-lead-quality-analyzer

For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

Quality

70%

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Securityby

Passed

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Quality

Content

81%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is a well-sequenced, gated workflow with strong validation checkpoints and concrete formulas, scoring highly on workflow clarity and actionability. Conciseness and progressive disclosure are solid but could improve by trimming familiar framing and moving report templates/bucket tables into reference files.

Suggestions

Move the full report-template blocks (Phase 6 and Tracking-Gap Mode) into separate reference files and link to them, keeping only the structure outline inline.

Trim contextual framing Claude already knows (e.g. 'Meta optimizes for whatever conversion event you fire') to tighten conciseness.

Provide a copy-paste-ready example query or API call for the data-extraction step in Phase 2 to push actionability from 4 to 5.

Dimension	Reasoning	Score
Conciseness	The body is dense and largely earns its tokens — phases, tables, thresholds, and bucket rules are all substance with little padding — though a few framing sentences (e.g. 'Meta optimizes for whatever conversion event you fire') explain context Claude already knows and could be trimmed.	4 / 5
Actionability	Concrete and executable in most places: exact formulas (True CAC = spend ÷ qualified leads), coverage thresholds (≥80%/50–80%/<50%), volume cutoffs (≥30 signups), and a fixed report template. It falls short of 5 because data-extraction steps are described generically ('pull via the existing Meta Marketing API connection') rather than with copy-paste-ready commands.	4 / 5
Workflow Clarity	Phases 0–6 are explicitly sequenced with gating checkpoints (Phase 1 coverage thresholds gate Phases 2–6), validation/feedback loops (re-validate, confidence flags, rollback plans), cohort-maturation guards against premature Cut decisions, and a tracking-gap fallback mode — a clear sequence with explicit validation and error-recovery paths.	5 / 5
Progressive Disclosure	Well-organized into clearly headed sections with a single-level structure and no nested references; cross-skill pointers (meta-ads-analyzer, ad-to-landing-page-auditor) are clearly signaled one level deep. It stays at 4 rather than 5 because the body inlines substantial reference-style material (full report templates and bucket tables) that could live in separate files, and no bundle files are present to split that detail.	4 / 5
	Total	17 / 20 Passed

Description

58%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description is specific and clearly scoped to a distinct niche, but it lacks an explicit 'Use when...' trigger clause and leans on technical/jargon terms over natural user phrasings. Adding trigger guidance and more user-natural synonyms would raise completeness and trigger_term_quality.

Suggestions

Add an explicit 'Use when...' clause (e.g. 'Use when auditing lead quality of paid lead-gen or participant-recruitment campaigns, computing true CAC per qualified lead, or deciding which creatives to scale vs. cut').

Incorporate natural user phrasings such as 'lead quality', 'cost per qualified lead', and 'ad performance' alongside the technical CPA/CAC terms.

Briefly mention the cohort-maturation handling in the description to make the capability set more comprehensive.

Dimension	Reasoning	Score
Specificity	Lists several concrete actions — 'replaces vanity CPA with true CAC per qualified lead', 'joining ad-platform data with downstream funnel events', 'surfaces tracking gaps', and 'classifies every creative into Scale / Keep / Investigate / Cut' — covering the domain well with minor gaps (e.g., the cohort-maturation handling is not surfaced).	4 / 5
Completeness	The 'what' is explicit and detailed (join data, replace CPA, surface gaps, classify creatives), but there is no 'Use when...' or equivalent explicit trigger guidance, which caps completeness at 3 per the rubric.	3 / 5
Trigger Term Quality	Contains relevant domain terms like 'lead-gen', 'participant-recruitment ads', 'CPA', 'CAC', and 'qualified lead', but omits the natural user-facing phrasings (e.g. 'lead quality', 'ad performance', 'cost per qualified lead') and any platform-specific synonyms a user would actually say.	3 / 5
Distinctiveness Conflict Risk	The narrow niche — paid lead-gen/participant-recruitment ads with downstream funnel joining and creative bucketing — is mostly distinct from generic ad-analysis skills, with only minor overlap risk against related meta-ads or campaign-analyzer skills.	4 / 5
	Total	14 / 20 Passed

Validation

93%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

Criteria	Description	Result
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	15 / 16 Passed

Repository: gooseworks-ai/goose-skills
Path: skills/ads/composites/ad-lead-quality-analyzer/SKILL.md
Commit: 2523432

Reviewed: 7 days ago

Table of Contents

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