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product-analytics-instrumentation-review

product-analytics-instrumentation-review

github.com/sylphxaiData & databases137 installsUpdated today
Repository

About product-analytics-instrumentation-review

A skill for turning product analytics instrumentation questions into owned plans covering events, identity, privacy, QA, and rollout. It is intended for teams that need decision-oriented tracking artifacts rather than generic analytics advice.

Key features

  1. 01Event taxonomy and classification
  2. 02Event contracts and identity rules
  3. 03Consent and privacy constraints
  4. 04QA, release gates, and drift monitoring
  5. 05Dashboards, owners, and rollout plans

Use cases

  1. 01Planning event tracking for a user journey
  2. 02Reviewing identity and consent behavior
  3. 03Setting analytics QA and release gates

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/sylphxai/skills --skill product-analytics-instrumentation-review

After installing, type/product-analytics-instrumentation-reviewin Claude Code to confirm it loaded.

Bundled filesagents/openai.yamlreferences/product-analytics-instrumentation-patterns.md

Details

From the project README

Product Analytics Instrumentation Review

Use this skill to convert product analytics instrumentation, event taxonomy, identity, funnel, privacy, QA, and metric-owner questions into a concrete artifact with owners, gates, metrics, and recovery paths.

Workflow

  1. Identify decisions to support, user journey, platforms, event sources, identity model, consent/privacy constraints, downstream metrics, dashboards, experiments, and current data-quality failures.
  2. Read references/product-analytics-instrumentation-patterns.md.
  3. Classify tracking as client event, server event, billing/support join, experiment exposure, attribution touch, lifecycle event, error state, or derived metric input.
  4. Define event contract, property schema, identity rules, consent gating, QA plan, dashboard owner, release gate, and drift monitoring.
  5. Produce instrumentation plan, state machine, decision table, event schema, QA checklist, and rollout plan.

When not to use

  • Do not use for generic advice the base model already handles without this skill's specific artifact contract.

Guardrails

  • Do not instrument everything; track decision-relevant events with clear owners and retention/privacy constraints.
  • Do not use client-only analytics for billing, compliance, entitlement, or critical operational truth.
  • Do not ship events without property schema, identity behavior, consent handling, and QA fixtures.
  • Do not let dashboards define metrics independently from the event/metrics source of truth.

Output format

Analytics context:
Audience / source of truth / risk boundary:

Instrumentation plan:
| Area | Decision | Evidence | Risk | Owner |
| --- | --- | --- | --- | --- |

Event contracts, QA, privacy, and dashboard handoff:
- <trigger> -> <policy, metric, edge case, support note>

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