analytics-tracking

Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.

1.18x

Quality

33%

Does it follow best practices?

Impact

95%

1.18x

Average score across 3 eval scenarios

Securityby

Passed

No known issues

Optimize this skill with Tessl

npx tessl skill review --optimize ./skills/analytics-tracking/SKILL.md

Quality

Discovery

32%

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 identifies a reasonable domain (analytics tracking) with three high-level actions, but lacks the concrete specificity and explicit trigger guidance needed for reliable skill selection. The absence of a 'Use when...' clause is a significant gap, and the trigger terms don't cover the natural language variations users would employ when requesting help with analytics tracking.

Suggestions

Add an explicit 'Use when...' clause with trigger scenarios, e.g., 'Use when the user asks about event tracking, analytics instrumentation, tracking plans, tag management, or data collection quality.'

Include more specific concrete actions such as 'create tracking plans, validate event schemas, debug data pipelines, configure tag managers, audit tracking implementations'.

Add natural keyword variations users would say, such as 'Google Analytics', 'GTM', 'event tracking', 'conversion tracking', 'data layer', 'UTM parameters', '.csv exports'.

Dimension	Reasoning	Score
Specificity	Names the domain (analytics tracking systems) and lists some actions (design, audit, improve), but these are fairly high-level and don't describe concrete sub-actions like 'create event schemas', 'validate data pipelines', or 'configure tracking tags'.	2 / 3
Completeness	Describes what the skill does but completely lacks a 'Use when...' clause or any explicit trigger guidance for when Claude should select this skill. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'what' is also somewhat vague, this scores a 1.	1 / 3
Trigger Term Quality	Includes relevant terms like 'analytics', 'tracking', and 'data', but misses common user-facing variations such as 'Google Analytics', 'event tracking', 'UTM parameters', 'tag management', 'metrics', 'instrumentation', or 'data quality'.	2 / 3
Distinctiveness Conflict Risk	The phrase 'analytics tracking systems' provides some specificity, but 'reliable, decision-ready data' is generic enough to overlap with data engineering, data quality, or business intelligence skills. Without more precise triggers, there's moderate conflict risk.	2 / 3
	Total	7 / 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 provides a comprehensive conceptual framework for analytics tracking strategy but suffers from significant verbosity, explaining many concepts Claude already understands. It lacks executable code examples (no dataLayer snippets, no validation scripts, no GA4 API calls) which limits actionability for a technical implementation skill. The phased structure with a readiness gate is a strength, but the scoring methodology is underspecified and the validation workflow lacks concrete tooling.

Suggestions

Cut 50%+ of the content by removing explanations of basic concepts (what conversions are, why data quality matters, what UTMs do) and focus only on the specific methodology and decision rules Claude wouldn't already know.

Add executable code examples: a GTM dataLayer.push snippet for event firing, a GA4 Measurement Protocol validation call, and a concrete script or checklist for the Measurement Readiness scoring (e.g., specific questions with point values for each sub-category).

Split detailed reference content (event taxonomy, GA4/GTM specifics, UTM rules) into separate linked files, keeping SKILL.md as a concise workflow overview with navigation pointers.

Add concrete validation steps with specific tools (e.g., 'Use GA4 DebugView to verify event firing', 'Check GTM Preview mode for trigger conditions') and a feedback loop for remediation when issues are found.

Dimension	Reasoning	Score
Conciseness	The skill is extremely verbose at ~300+ lines, with significant padding. It explains concepts Claude already knows (what a conversion is, what UTMs are, basic analytics principles). Sections like 'Core Principles' and 'Privacy & Compliance' state obvious best practices. The 'Purpose' section explaining what the index prevents is unnecessary framing. Much of this could be condensed to 1/3 the length.	1 / 3
Actionability	The skill provides structured frameworks (scoring index, event taxonomy, output tables) which give some concrete guidance, but lacks executable code examples (no GTM dataLayer push snippets, no GA4 configuration code, no validation scripts). The scoring system has categories and weights but no concrete method for calculating sub-scores—it's more of a conceptual framework than executable guidance.	2 / 3
Workflow Clarity	There is a phased structure (Phase 0 → Phase 1) with a gate ('if Broken, stop'), which is good. However, the validation section lists what to check but provides no concrete validation steps or tools. The workflow from scoring to implementation to verification lacks explicit checkpoints and feedback loops for error recovery.	2 / 3
Progressive Disclosure	The content references related skills at the bottom which is good, but the main body is a monolithic wall of text that could benefit from splitting detailed sections (event taxonomy, GA4/GTM guidance, UTM rules) into separate reference files. The document tries to be both an overview and a comprehensive reference, resulting in excessive inline content.	2 / 3
	Total	7 / 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: sickn33/antigravity-awesome-skills
Commit: d739c8b

Reviewed: 4 days ago

Table of Contents

Discovery Implementation Validation

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