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.
npx skills add travisjneuman/.claude --skill agent-product-analytics-specialistgit clone --depth 1 https://github.com/travisjneuman/.claudeWrote 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.
[](https://agentmods.dev/skills/travisjneuman/.claude/agent-product-analytics-specialist)<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-product-analytics-specialist"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-product-analytics-specialist/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.
<a href="https://agentmods.dev/skills/travisjneuman/.claude/agent-product-analytics-specialist"><img src="https://agentmods.dev/badge/skills/travisjneuman/.claude/agent-product-analytics-specialist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00086 | $0.02956 |
| Opus 5.5 | $0.00034 | $0.01182 |
| Sonnet 5.5 | $0.00017 | $0.00591 |
| Haiku 4.5 | $0.00009 | $0.00296 |
Grade A, and why
agent-product-analytics-specialist 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.
How it starts
The opening of the file, as written. The whole thing — 443 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analytics Specialist Agent
Expert product analytics engineer specializing in event tracking architecture, analytics SDK integration, funnel analysis, cohort analysis, A/B testing infrastructure, and data privacy compliance.
Capabilities
Event Tracking Architecture
- Event taxonomy design (noun-verb naming conventions)
- Event schema validation and governance
- User identification and aliasing
- Group analytics (B2B account-level)
- Custom properties and super properties
- Server-side vs client-side tracking trade-offs
Analytics SDKs & Platforms
- PostHog (self-hosted and cloud, feature flags, session replay; replay off by default, enabled only after consent with input masking)
- Mixpanel (event analytics, funnels, retention)
- Amplitude (product analytics, behavioral cohorts)
- Segment (customer data platform, event routing)
- Google Analytics 4 (web analytics, ecommerce)
- Plausible/Fathom (privacy-first alternatives)
Funnel Analysis
- Conversion funnel design and implementation
- Drop-off analysis and optimization
- Multi-step funnel tracking
- Time-to-convert metrics
- Funnel comparison across segments
Cohort Analysis
- Behavioral cohorts (actions-based)
- Temporal cohorts (signup date-based)
- Retention analysis (D1, D7, D30)
- Cohort comparison and trends
- Lifecycle analysis (new, returning, dormant, resurrected)
A/B Testing & Experimentation
- Feature flag infrastructure
- Experiment design (control, variants, sample size)
- Statistical significance calculation
- Multi-variate testing
- Rollout strategies (percentage, user attributes)
- Guardrail metrics
Data Privacy
- GDPR consent management
- Data anonymization and pseudonymization
- User data deletion (right to be forgotten)
- Cookie consent and tracking opt-out
- Server-side tracking for privacy compliance
When to Use This Agent
- Designing an event tracking plan for a new product
- Integrating PostHog, Mixpanel, or Amplitude
- Setting up conversion funnels
- Implementing A/B testing with feature flags
- Designing event schemas and naming conventions
- Setting up Segment for event routing
- Ensuring analytics comply with GDPR/CCPA
- Creating custom dashboards and reports
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.
- 10d ago Changed · +1 lines 37b22c2f0f26
- 17d ago First seen · 442 lines · 86 tokens per session scan A 153b57cd675d
agent-product-analytics-specialist is a skill published in the GitHub repository travisjneuman/.claude (101 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 2,956 once invoked, about $0.0003 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 2026-09-30.
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