ads-meta

Meta Ads deep analysis covering Facebook, Instagram, and Threads advertising in the Andromeda + GEM + Lattice era. Evaluates 50 checks across Pixel/CAPI health, creative diversity and Entity-ID clustering risk, account structure, ASC/AAC defaults for Sales/Leads/App, and audience targeting. Includes Advantage+ assessment and creative-as-targeting scoring. Use when user says Meta Ads, Facebook Ads, Instagram Ads, Threads ads, Advantage+, ASC, AAC, Andromeda, GEM, Lattice, Entity-ID clustering, creative diversity, Sales optimization, Leads optimization, App optimization, or Meta campaign.

Quality

75%

Does it follow best practices?

Impact

—

No eval scenarios have been run

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/ads-meta/SKILL.md

Quality

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 excels across all dimensions. It provides highly specific capabilities (50 checks across named categories), includes a comprehensive 'Use when' clause with extensive trigger terms covering both common user language and technical terminology, and occupies a clearly distinct niche in Meta advertising analysis. The description uses proper third-person voice throughout.

Dimension	Reasoning	Score
Specificity	Lists multiple specific concrete actions: evaluates 50 checks across Pixel/CAPI health, creative diversity, Entity-ID clustering risk, account structure, ASC/AAC defaults, audience targeting, Advantage+ assessment, and creative-as-targeting scoring.	3 / 3
Completeness	Clearly answers both 'what' (deep analysis covering 50 checks across multiple dimensions) and 'when' (explicit 'Use when user says...' clause with comprehensive trigger terms).	3 / 3
Trigger Term Quality	Excellent coverage of natural terms users would say: 'Meta Ads', 'Facebook Ads', 'Instagram Ads', 'Threads ads', 'Advantage+', 'ASC', 'AAC', plus technical terms like 'Andromeda', 'GEM', 'Lattice', 'Entity-ID clustering', and optimization types.	3 / 3
Distinctiveness Conflict Risk	Highly distinctive with a clear niche in Meta/Facebook advertising analysis. The specific technical terms (Andromeda, GEM, Lattice, Entity-ID clustering, CAPI) and platform focus make it very unlikely to conflict with other 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 is a substantively rich skill that brings genuine domain expertise about Meta's 2025-2026 ad delivery stack changes, creative diversity scoring, and comprehensive audit criteria. Its main weaknesses are the lack of executable code/commands for actually performing the audit steps, missing validation checkpoints in the workflow, and inline content that could benefit from being split into referenced files. The threshold tables and scoring rubrics are strong actionable elements.

Suggestions

Add concrete executable examples for key steps — e.g., a Python snippet for querying MAPI v25 for EMQ scores, or a shell command for exporting Ads Manager data, to make the process steps truly actionable.

Add validation checkpoints to the process workflow — e.g., 'Step 2: Verify all 50 checks have data sources before scoring; if EMQ data is missing, flag as INCOMPLETE rather than FAIL' to create feedback loops.

Move the detailed Entity-ID Clustering Predictor heuristics and Creative-as-targeting scoring rubric into separate referenced files (e.g., ads/references/creative-clustering.md) to keep SKILL.md as a leaner overview.

Add a concrete example of the creative-cluster-risk.md deliverable output so Claude knows exactly what format to produce.

Dimension	Reasoning	Score
Conciseness	The skill is mostly efficient and contains domain-specific knowledge Claude wouldn't inherently know (Andromeda/GEM/Lattice stack, Entity-ID clustering, EMQ optimization). However, some sections like the 'What to Analyze' checklists are verbose with items that could be more compressed, and explanations like 'CAPI active (30-40% data loss without it post-iOS 14.5)' add parenthetical context that's borderline unnecessary for Claude.	2 / 3
Actionability	The skill provides concrete scoring rubrics, threshold tables, and specific metrics (EMQ ≥8.0, dedup ≥90%, etc.) which are highly actionable. However, it lacks executable code/commands — there are no actual scripts, API calls, or copy-paste-ready commands for data collection, MAPI queries, or report generation. The process section is a numbered list of high-level steps without concrete implementation details.	2 / 3
Workflow Clarity	The 7-step process is clearly sequenced and references external files for detailed checks, scoring, and benchmarks. However, there are no validation checkpoints or feedback loops — no step says 'verify data completeness before proceeding' or 'if EMQ data is unavailable, do X instead.' For a 50-check audit with weighted scoring, the absence of error handling or verification steps is a gap.	2 / 3
Progressive Disclosure	The skill references external files (meta-audit.md, benchmarks.md, scoring-system.md, compliance.md) which is good progressive disclosure design. However, no bundle files are provided, so these references are unverifiable. Additionally, the Andromeda/GEM/Lattice section and the detailed 'What to Analyze' checklists are quite long inline — the creative-as-targeting rubric and Entity-ID clustering predictor could arguably be in a referenced file to keep the SKILL.md leaner.	2 / 3
	Total	8 / 12 Passed

Validation

90%

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 — 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: 283d9d4

Reviewed: 7 days ago

Table of Contents

Discovery Implementation Validation

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