analytics-tracking
Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
36
33%
Does it follow best practices?
Impact
—
No eval scenarios have been run
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./plugins/antigravity-awesome-skills-claude/skills/analytics-tracking/SKILL.mdQuality
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) and lists high-level actions, but it lacks the concrete specificity, natural trigger terms, and explicit 'Use when...' guidance needed for reliable skill selection. The phrase 'decision-ready data' is marketing-style fluff rather than a useful trigger term. Without clearer triggers and more specific capabilities, this skill risks being overlooked or confused with general data/analytics skills.
Suggestions
Add an explicit 'Use when...' clause with trigger scenarios, e.g., 'Use when the user asks about event tracking, analytics instrumentation, tracking plans, data collection validation, or debugging analytics pipelines.'
Replace vague phrases like 'decision-ready data' with concrete actions such as 'create tracking plans, validate event schemas, audit tag implementations, debug data collection issues.'
Include natural user terms and tool names users might mention, such as 'Google Analytics, Mixpanel, Segment, GTM, event tracking, UTM parameters, conversion tracking.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | Names the domain (analytics tracking systems) and lists some actions (design, audit, improve), but these actions are fairly high-level and not deeply concrete. It doesn't specify particular techniques, tools, or outputs like 'create tracking plans, validate event schemas, debug data pipelines.' | 2 / 3 |
Completeness | It describes what the skill does (design, audit, improve analytics tracking systems) 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 the 'what' is also somewhat vague, placing this at 1. | 1 / 3 |
Trigger Term Quality | Includes some relevant keywords like 'analytics,' 'tracking,' and 'data,' but misses common user terms like 'events,' 'metrics,' 'Google Analytics,' 'Mixpanel,' 'UTM,' 'tagging,' 'instrumentation,' or 'data quality.' Users might not naturally say 'decision-ready data.' | 2 / 3 |
Distinctiveness Conflict Risk | The focus on 'analytics tracking systems' provides some specificity, but terms like 'data' and 'analytics' are broad enough to overlap with data analysis, business intelligence, or general data engineering skills. | 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 reads more like a comprehensive analytics playbook or training manual than a concise, actionable skill for Claude. Its strengths are in providing a structured scoring framework and clear event taxonomy, but it suffers from significant verbosity, explaining many concepts Claude already understands, and lacking concrete executable examples (no actual code for dataLayer pushes, GTM configurations, or validation scripts). The phased workflow has a good gate condition but lacks feedback loops for remediation.
Suggestions
Cut at least 50% of the content by removing explanations of concepts Claude already knows (what conversions are, what UTMs are, basic privacy principles) and keeping only the specific rules and patterns unique to this skill.
Add concrete, executable code examples: a sample dataLayer.push() call, a GTM custom event tag configuration, a validation script or browser console command for checking event firing.
Add explicit feedback loops to the workflow: after scoring the Measurement Readiness Index, specify exactly what remediation steps to take for each readiness band, with re-scoring checkpoints.
Split detailed reference content (event taxonomy, scoring rubric details, GA4/GTM specifics) into separate bundle files and reference them from a leaner SKILL.md overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~300+ lines, explaining many concepts Claude already knows (what a conversion is, what UTMs are, basic privacy principles, what PII means). Extensive tables and category definitions could be dramatically condensed. Much of the content reads like a training document for a junior analyst rather than actionable instructions for Claude. | 1 / 3 |
Actionability | The skill provides structured frameworks (scoring index, event taxonomy, output tables) that give some concrete guidance, but lacks executable code, specific commands, or copy-paste-ready implementations. The GA4/GTM section is particularly vague ('Prefer GA4 recommended events', 'Push clean dataLayer events') without concrete examples of dataLayer pushes or GTM configurations. | 2 / 3 |
Workflow Clarity | There is a clear phased sequence (Phase 0 → Phase 1 → Design → Implementation) with a gate condition ('If verdict is Broken, stop'), which is good. However, validation steps are listed as bullet points without clear sequencing, there are no feedback loops for remediation, and the relationship between phases is implicit rather than explicitly connected with checkpoints. | 2 / 3 |
Progressive Disclosure | The content references related skills at the bottom (page-cro, ab-test-setup, etc.) but has no bundle files to offload detailed content to. The entire skill is a monolithic document where sections like Event Model Design, Conversion Strategy, and GA4/GTM guidance could be split into separate reference files. The structure uses headers well but everything is inline. | 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 | |
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- sickn33/antigravity-awesome-skills
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