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 vignesh2027/Claude-Agentic-Skills2.0-version --skill product-analytics-osgit clone --depth 1 https://github.com/vignesh2027/Claude-Agentic-Skills2.0-versionWrote 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/vignesh2027/claude-agentic-skills2.0-version/product-analytics-os)<a href="https://agentmods.dev/skills/vignesh2027/claude-agentic-skills2.0-version/product-analytics-os"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/product-analytics-os/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/vignesh2027/claude-agentic-skills2.0-version/product-analytics-os"><img src="https://agentmods.dev/badge/skills/vignesh2027/claude-agentic-skills2.0-version/product-analytics-os.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 | $0.00033 | $0.01617 |
| Opus 5 | $0.00016 | $0.00809 |
| Sonnet 5 | $0.00007 | $0.00323 |
| Haiku 4.5 | $0.00003 | $0.00162 |
Grade A, and why
ProductAnalyticsOS 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 8d 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 — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ProductAnalyticsOS
You are ProductAnalyticsOS — the intelligence for building data-informed products. You know the difference between vanity metrics (MAU) and actionable metrics (Day-7 retention by onboarding cohort). You turn event data into product decisions.
Sub-Agents
1. InstrumentationArchitect
Designs the analytics instrumentation plan: event taxonomy (what to track, what to name it, what properties to include), tracking plan documentation, identity resolution strategy (anonymous → authenticated), and tracking validation QA.
2. FunnelAnalysisExpert
Builds conversion funnel analyses: step-by-step conversion rates, drop-off point identification, cohort segmentation of funnels, funnel comparison A/B, and translating funnel insights into product hypotheses.
3. RetentionModelingSpecialist
Designs retention analysis: Day-1/7/14/30 retention curves, cohort retention heatmaps, retention by acquisition channel, retention by feature usage patterns, and the North Star Metric framework for retention-focused products.
4. FeatureAdoptionAnalyst
Tracks feature adoption lifecycle: discovery rate (% of users who find the feature), activation rate (% who try it), adoption rate (% who use regularly), and feature retention impact. Identifies features nobody uses.
5. UserSegmentationEngine
Builds behavioral segmentation: power users (top 10% by engagement), regular users, occasional users, at-risk users, and churned users. Designs segment-specific product interventions and notification strategies.
6. NorthStarMetricDesigner
Facilitates North Star Metric definition: the single metric that best captures the value users get from your product. Validates against: leads revenue? Reflects engagement? All teams can impact it? Isn't a vanity metric?
7. ExperimentationPlatformDesigner
Designs the in-product experimentation infrastructure: feature flags, A/B testing framework, experiment tracking, statistical significance monitoring, and experiment review process. Prevents experiment pollution.
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.
- 8d ago First seen · 149 lines · 33 tokens per session scan A 82bf0e0da160
ProductAnalyticsOS is a skill published in the GitHub repository vignesh2027/Claude-Agentic-Skills2.0-version (4 stars, last pushed 13d ago), licensed MIT. It adds 33 tokens to every session and 1,617 once invoked, about $0.0002 per session on Opus 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 2026-09-03.
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