analytics-tracking

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

Quality

58%

Does it follow best practices?

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tessl review fix ./skills/analytics-tracking/SKILL.md

Quality

Content

57%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 body delivers concrete, actionable analytics-tracking guidance with a strong worked example and validation checklist, but it is a long single-file document whose large inlined review rubric and distributed validation steps keep conciseness, workflow clarity, and progressive disclosure at the midpoint.

Suggestions

Move the "Measurement Readiness & Signal Quality Index" rubric into a separate references/ file and link to it, reducing inlined bulk and improving progressive disclosure.

Tighten or relocate the restated Core Principles and GA4/GTM sections to trim tokens without losing the actionable guidance.

Reframe the phases as a numbered sequence with explicit validation checkpoints (e.g. validate event firing → fix → re-validate before defining conversions) to lift workflow clarity above 3.

Dimension	Reasoning	Score
Conciseness	The ~410-line body is mostly efficient bullet/table guidance and avoids explaining basic concepts, but the large inlined "Measurement Readiness & Signal Quality Index" rubric and restated Core Principles could be tightened or moved out.	3 / 5
Actionability	Concrete, specific guidance is provided throughout: the object_action[_context] naming pattern with examples, an event taxonomy, include/avoid property lists, UTM rules, conversion-counting rules, output-format table templates, and a worked dedup example with expected outcome.	4 / 5
Workflow Clarity	Phases (Phase 0, Phase 1) and a Validation & Debugging checklist give a recognizable sequence with validation present, but checkpoints are not crisply tied to each step as an explicit validate→fix→retry loop, leaving the workflow more reference-manual than step-sequenced.	3 / 5
Progressive Disclosure	Sections are well-organized with clear headers, but no bundle/reference files exist and the sizable inlined review rubric is content that arguably belongs in a separate file, so structure is good yet not appropriately split.	3 / 5
	Total	13 / 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 clearly states what the skill does with three concrete verbs and a well-scoped domain, but omits any explicit "Use when..." trigger guidance and lacks the tool-specific synonyms (GA4, GTM, UTMs) users would naturally say. This leaves trigger_term_quality and completeness at the midpoint.

Suggestions

Add an explicit trigger clause, e.g. ".Use when adding decision-relevant events, investigating discrepant conversion counts, or auditing GA4/GTM attribution and duplicate firing."

Include natural tool-specific trigger terms (GA4, GTM, Mixpanel, Amplitude, UTMs, conversion tracking) so the description matches how users actually phrase the need.

Consider naming a more granular action or two (e.g. "define event taxonomies", "reconcile duplicate purchases") to lift specificity from 4 toward 5.

Dimension	Reasoning	Score
Specificity	"Design, audit, and improve" lists several concrete actions on a named domain (analytics tracking systems), though the verbs are high-level categories rather than granular operations, leaving minor coverage gaps.	4 / 5
Completeness	The description gives a clear "what" (design/audit/improve analytics tracking systems) but contains no explicit "Use when..." trigger clause, capping completeness at 3 per the rubric guideline.	3 / 5
Trigger Term Quality	"analytics tracking" and "decision-ready data" are relevant keywords, but common natural synonyms users say (GA4, GTM, event tracking, conversion tracking, UTMs, Mixpanel) are missing.	3 / 5
Distinctiveness Conflict Risk	The tracking-instrumentation framing ("analytics tracking systems that produce reliable, decision-ready data") carves a mostly distinct niche with only minor overlap risk against general data/analytics 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: sickn33/agentic-awesome-skills
Path: skills/analytics-tracking/SKILL.md
Commit: b1aebac

Reviewed: ~18 hours ago

Table of Contents

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