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 jeremylongworth-source/AgentSkills --skill product-analytics-instrumentationgit clone --depth 1 https://github.com/jeremylongworth-source/AgentSkillsWrote 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/jeremylongworth-source/agentskills/product-analytics-instrumentation)<a href="https://agentmods.dev/skills/jeremylongworth-source/agentskills/product-analytics-instrumentation"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/product-analytics-instrumentation/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/jeremylongworth-source/agentskills/product-analytics-instrumentation"><img src="https://agentmods.dev/badge/skills/jeremylongworth-source/agentskills/product-analytics-instrumentation.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 | $0.00079 | $0.00502 |
| Opus 5.5 | $0.00032 | $0.00201 |
| Sonnet 5.5 | $0.00016 | $0.00100 |
| Haiku 4.5 | $0.00008 | $0.00050 |
Grade A, and why
product-analytics-instrumentation 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 1mo 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 — 47 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Product Analytics Instrumentation
Core Workflow
- Define the product decision, user journey, and metric questions before naming events.
- Map the journey: acquisition, onboarding, activation, core loop, conversion, retention, expansion, referral, failure, and support.
- Design event taxonomy with consistent names, required properties, optional properties, identity rules, timestamps, source, and versioning.
- Separate product analytics events from technical observability signals, while preserving useful joins between them.
- Plan implementation and QA: trigger conditions, deduplication, consent, privacy, test users, environments, and validation queries.
- Define dashboard and analysis use cases before instrumenting extra data.
- Maintain the taxonomy: ownership, change review, deprecation, documentation, and data quality checks.
Freshness Rule
Verify current analytics, privacy, SDK, consent, and observability documentation before giving platform-specific advice for GA4, Firebase, Amplitude, Segment, PostHog, Mixpanel, OpenTelemetry, data warehouses, or mobile/game SDKs.
Instrumentation Principles
- Track meaningful user behavior, not every click.
- Name events consistently and in business language where possible.
- Use properties for context, not to hide separate actions.
- Avoid high-cardinality dimensions in metrics systems unless the backend supports them and the use case justifies it.
- Keep PII and sensitive data out of analytics events unless there is a clear legal basis and privacy review.
- QA analytics like product behavior: event fires once, at the right time, with the right properties, in the right environment.
Deliverable Shape
For analytics instrumentation work, provide:
- Decision questions and product journey
- North-star, activation, funnel, retention, and guardrail metrics
- Event taxonomy or tracking plan
- Identity, privacy, and consent assumptions
- Implementation notes
- QA and data-quality checks
- Dashboard or analysis plan
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 47 lines · 79 tokens per session scan A 38bc04534c9b
product-analytics-instrumentation is a skill published in the GitHub repository jeremylongworth-source/AgentSkills (1 stars, last pushed 17d ago), licensed MIT. It adds 79 tokens to every session and 502 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 2026-09-03.
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