analytics-product

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

Quality

41%

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 ./plugins/antigravity-awesome-skills-claude/skills/analytics-product/SKILL.md

Quality

Content

27%

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

The skill covers a broad range of product analytics topics with some useful executable code examples, but suffers from significant bloat with generic boilerplate sections that Claude doesn't need. The content would benefit greatly from splitting into focused sub-files, removing template filler, and adding proper workflow sequencing with validation steps. The mix of specific Auri product context with general analytics guidance creates confusion about whether this is a reusable skill or a project-specific reference.

Suggestions

Remove all generic boilerplate sections (When to Use, Do Not Use, Best Practices, Common Pitfalls, Limitations) — these waste tokens and Claude already knows this guidance.

Split the monolithic content into separate files (e.g., EVENTS.md, FUNNELS.md, AB-TESTING.md, COHORT.md) and use SKILL.md as a concise overview with clear references.

Add explicit validation/verification steps to workflows — e.g., after setting up event tracking, verify events appear in PostHog; after running A/B test calculations, validate sample size adequacy.

Fix the SQL bug in calculate_north_star (GROUP BY user_id should be in a subquery before the HAVING clause) and ensure all code examples are truly executable.

Dimension	Reasoning	Score
Conciseness	The skill is bloated with generic boilerplate sections (When to Use, Do Not Use, Best Practices, Common Pitfalls, Limitations) that add no value and are things Claude already knows. The quote, the verbose overview that repeats the description, and the generic 'Related Skills' section all waste tokens. The domain-specific content (events, funnels, cohort analysis) is reasonable but is buried in filler.	1 / 3
Actionability	The skill provides executable Python code for PostHog tracking, cohort retention calculation, A/B test significance, and feature flags, which is good. However, several sections are pseudocode-like or conceptual (funnel optimization steps, North Star framework), the commands table lists commands with no implementation details, and the SQL query in calculate_north_star has a bug (GROUP BY should be in a subquery). The code is partially actionable but incomplete in places.	2 / 3
Workflow Clarity	The funnel optimization section provides a clear 6-step process, but lacks explicit validation checkpoints or feedback loops. The overall skill doesn't clearly sequence how to go from zero to a working analytics setup — it presents disconnected code snippets and concepts rather than a coherent workflow. No verification steps for event tracking setup or dashboard creation.	2 / 3
Progressive Disclosure	The skill is a monolithic wall of text with no references to external files despite covering many complex topics (PostHog setup, cohort analysis, A/B testing, OKRs, feature flags) that would benefit from separation. No bundle files are provided. The commands table references actions like `/okr-template` with no supporting content. Everything is crammed into one long file with poor organization.	1 / 3
	Total	6 / 12 Passed

Description

54%

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 effectively establishes a clear niche in product analytics with strong trigger terms including specific tool names and domain concepts. However, it lacks concrete action verbs describing what the skill actually does and completely omits a 'Use when...' clause, making it unclear when Claude should select this skill. The description reads as a keyword/topic list rather than a functional capability description.

Suggestions

Add a 'Use when...' clause, e.g., 'Use when the user asks about product analytics, tracking user behavior, setting up events in PostHog or Mixpanel, building funnels, or defining product metrics.'

Replace the topic list with concrete actions, e.g., 'Configures product analytics events, builds conversion funnels, defines cohorts, analyzes retention curves, and creates product dashboards using PostHog or Mixpanel.'

Use third-person verb phrases to describe capabilities rather than listing nouns, e.g., 'Tracks and analyzes user behavior' instead of just 'eventos, funnels, cohorts'.

Dimension	Reasoning	Score
Specificity	Names the domain (product analytics) and lists relevant concepts like events, funnels, cohorts, retention, north star metric, OKRs, and dashboards, but doesn't describe concrete actions (e.g., 'create funnels', 'build dashboards', 'track retention'). It reads more like a topic list than a capability description.	2 / 3
Completeness	Describes the 'what' domain loosely but completely lacks a 'when should Claude use it' clause. There is no 'Use when...' or equivalent trigger guidance, which per the rubric should cap completeness at 2, and since the 'what' is also weak (topic listing rather than actions), it scores a 1.	1 / 3
Trigger Term Quality	Includes strong natural keywords users would actually say: PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs, dashboards de produto. These cover multiple common variations and specific tool names users would mention.	3 / 3
Distinctiveness Conflict Risk	The combination of specific tool names (PostHog, Mixpanel) and domain-specific terms (funnels, cohorts, retention, north star metric) creates a clear niche that is unlikely to conflict with other skills.	3 / 3
	Total	9 / 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: be20b37

Reviewed: 1 day ago

Table of Contents

Discovery Implementation Validation

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.