analytics-product

analytics-product is a skill for Claude Code from lingxling/awesome-skills-cn. It costs 35 tokens per session (2,959 once invoked), scanned A, original, MIT.

A guide to product analytics, which measures how people use a product through events, funnels, cohorts, retention, and dashboards. It covers tools such as PostHog and Mixpanel.

In plain words
What is it for?
It is for defining activation events, investigating funnel drops, calculating retention, setting product goals, and planning dashboards or experiments.
Why use it?
It helps turn product usage data into clearly defined measurements instead of vague or inconsistent tracking. It also calls out factors such as consent, time zones, and the unit being measured.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions Codex; mentions Gemini CLI.

Part of the agentic-awesome-skills plugin — 174 skills shipped together

Good fit It is for defining activation events, investigating funnel drops, calculating retention, setting product goals, and planning dashboards or experiments.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lingxling/awesome-skills-cn/analytics-product
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add lingxling/awesome-skills-cn --skill analytics-product
Clone the repo
git clone --depth 1 https://github.com/lingxling/awesome-skills-cn

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 174 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analytics-product

README.md
[![agentmods](https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/analytics-product/github.svg)](https://agentmods.dev/skills/lingxling/awesome-skills-cn/analytics-product)
Your own site
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/analytics-product"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/analytics-product/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analytics-product

Your own site · 80×15
<a href="https://agentmods.dev/skills/lingxling/awesome-skills-cn/analytics-product"><img src="https://agentmods.dev/badge/skills/lingxling/awesome-skills-cn/analytics-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,959 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00035 $0.02959
Opus 5.5 $0.00014 $0.01184
Sonnet 5.5 $0.00007 $0.00592
Haiku 4.5 $0.00003 $0.00296

Measured 10d ago against content hash 057672d73579, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analytics-product scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

antigravity-awesome-skills/plugins/agentic-awesome-skills-claude/skills/analytics-product/SKILL.md · 320 lines

How it starts

The opening of the file, as written. The whole thing — 320 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ANALYTICS-PRODUCT — Decida com Dados

Overview

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto. Ativar para: configurar tracking de eventos, criar funil de conversao, analise de cohort, retencao, DAU/MAU, feature flags, A/B testing, north star metric, OKRs, dashboard de produto.

When to Use This Skill

  • Use para definir um evento de ativacao, investigar queda de funil ou calcular retencao com denominador e janela explicitos.
  • Antes de instrumentar, registre a decisao de produto, a fonte de dados, o consentimento aplicavel, o fuso horario e a unidade de analise.

Do Not Use This Skill When

  • The task is unrelated to analytics product
  • A simpler, more specific tool can handle the request
  • The user needs general-purpose assistance without domain expertise

How It Works

[objeto]_[verbo_passado]

Correto:   user_signed_up, conversation_started, upgrade_completed
Errado:    signup, click, conversion

Analytics-Product — Decida Com Dados

"In God we trust. All others must bring data." — W. Edwards Deming


Exemplo ilustrativo: eventos de um assistente

AURI_EVENTS = {
    # Aquisicao
    "user_signed_up":        {"props": ["source", "medium", "campaign"]},
    "onboarding_started":    {"props": ["step_count"]},
    "onboarding_completed":  {"props": ["time_to_complete", "steps_skipped"]},

    # Ativacao
    "first_conversation":    {"props": ["intent", "response_time"]},
    "aha_moment_reached":    {"props": ["trigger", "session_number"]},
    "feature_discovered":    {"props": ["feature_name", "discovery_method"]},

    # Retencao
    "conversation_started":  {"props": ["intent", "user_tier", "device"]},
    "conversation_completed":{"props": ["messages_count", "duration", "rating"]},
    "session_started":       {"props": ["days_since_last", "platform"]},

    # Receita
    "upgrade_viewed":        {"props": ["trigger", "current_tier"]},
    "upgrade_started":       {"props": ["target_tier", "trigger"]},
    "upgrade_completed":     {"props": ["tier", "plan", "revenue"]},
    "subscription_canceled": {"props": ["reason", "tier", "tenure_days"]},
    "payment_failed":        {"props": ["attempt_count", "error_code"]},
}

Read the full file on GitHub · 320 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 320 lines · 35 tokens per session scan A 057672d73579

Subscribe to this mod's changes

analytics-product is a skill published in the GitHub repository lingxling/awesome-skills-cn (312 stars, last pushed 10d ago), licensed MIT. It adds 35 tokens to every session and 2,959 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-07.

Related

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