analytics-product
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
41
41%
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-product/SKILL.mdQuality
Discovery
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 provides excellent trigger term coverage with specific tool names and product analytics concepts, making it highly distinctive. However, it reads as a keyword/topic list rather than describing concrete actions, and critically lacks any 'Use when...' clause to guide skill selection. Adding explicit actions and trigger conditions would significantly improve its effectiveness.
Suggestions
Add a 'Use when...' clause, e.g., 'Use when the user asks about product analytics, tracking events, building funnels, analyzing retention, or setting up dashboards in PostHog or Mixpanel.'
Convert the topic list into concrete actions, e.g., 'Configures event tracking, builds conversion funnels, defines user cohorts, analyzes retention curves, and creates product dashboards using PostHog or Mixpanel.'
| Dimension | Reasoning | Score |
|---|---|---|
Specificity | The description names the domain (product analytics) and lists relevant concepts (events, funnels, cohorts, retention, north star metric, OKRs, dashboards), but doesn't describe concrete actions — it reads more like a topic list than a list of specific capabilities (e.g., 'create funnels', 'build dashboards', 'define cohorts'). | 2 / 3 |
Completeness | The description answers 'what domain' but lacks any explicit 'Use when...' clause or equivalent trigger guidance. Per the rubric, a missing 'Use when...' clause caps completeness at 2, and since the 'what' is also more of a keyword list than a clear capability statement, this falls to 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 a wide range of terms a user working in product analytics would naturally use. | 3 / 3 |
Distinctiveness Conflict Risk | The combination of specific product analytics tools (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 |
Implementation
27%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This skill contains some useful executable code (PostHog integration, cohort retention, A/B testing) but is undermined by excessive boilerplate, product-specific content ('Auri') that limits generalizability, and poor organization. The content would benefit significantly from trimming generic sections, splitting detailed code into referenced files, and adding explicit validation workflows for analytics setup.
Suggestions
Remove all generic boilerplate sections (When to Use, Do Not Use, Common Pitfalls, Best Practices, Limitations) that provide no domain-specific value and waste tokens.
Split the detailed code examples (cohort retention, A/B test calculator, event taxonomy) into separate referenced files and keep only a concise overview with navigation links in SKILL.md.
Add an explicit end-to-end workflow with validation checkpoints for setting up product analytics (e.g., define events → implement tracking → verify events firing → build dashboard → validate data).
Generalize the content beyond the 'Auri' product so it serves as reusable analytics guidance, or clearly frame the Auri-specific parts as examples rather than the primary content.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is bloated with generic boilerplate sections (When to Use, Do Not Use, Common Pitfalls, Best Practices, Limitations) that add no value and are things Claude already knows. The content also includes a lengthy quote, verbose event taxonomy for a specific product ('Auri'), and benchmark tables that pad the token count significantly. Many sections explain obvious concepts. | 1 / 3 |
Actionability | The skill provides executable Python code for PostHog tracking, cohort retention, A/B test significance, and feature flags, which is good. However, much of it is specific to a product called 'Auri' rather than being generalizable guidance, the commands table lists slash commands with no implementation details, and the funnel optimization section is abstract rather than concrete. | 2 / 3 |
Workflow Clarity | The funnel optimization section provides a numbered sequence (identify → hypothesize → test → measure → learn), but lacks explicit validation checkpoints or feedback loops. There's no clear workflow for setting up analytics end-to-end, and the various code snippets are presented as isolated fragments rather than a coherent multi-step process with verification steps. | 2 / 3 |
Progressive Disclosure | The content is a monolithic wall of text with no references to external files despite being over 200 lines. The event taxonomy, cohort analysis code, A/B testing calculator, and benchmark tables could all be split into separate reference files. The Related Skills section lists other skills but provides no meaningful navigation within this skill's content. | 1 / 3 |
Total | 6 / 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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