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 varunk130/ai-gtm-skill-library --skill customer-analyticsgit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/customer-analytics)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/customer-analytics"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/customer-analytics/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/varunk130/ai-gtm-skill-library/customer-analytics"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/customer-analytics.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 | $0.00061 | $0.01241 |
| Opus 5.5 | $0.00024 | $0.00496 |
| Sonnet 5.5 | $0.00012 | $0.00248 |
| Haiku 4.5 | $0.00006 | $0.00124 |
Grade A, and why
customer-analytics 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Analytics (LENS Framework)
Design a customer analytics architecture that answers which customers, doing what, are driving (or breaking) the business - instead of dashboards full of vanity counts. LENS produces a defensible segmentation, a retention model, an engagement score, and a behavioral diagnostic loop that PMs and CS can act on weekly.
Core Principle
Customer analytics fails when it stops at "users went up." LENS forces decomposition into who, what, when, and why - the four axes a dashboard usually collapses into one number.
The LENS Framework
| Letter | Stage | The Question |
|---|---|---|
| L | Lifecycle Mapping | What are the named lifecycle stages and what does each one's "good" look like? |
| E | Engagement Scoring | What weighted score combines depth, breadth, and recency of value events? |
| N | Net Retention Decomposition | Where exactly is NRR coming from - new logo, expansion, contraction, churn? |
| S | Segment Behavior | Which segments behave differently, and which behavioral cohorts predict outcomes? |
Lifecycle Stages
| Stage | "Good" Signal | Diagnostic |
|---|---|---|
| New | First value event within target window | Activation rate by cohort |
| Activated | ≥ N value events / week within 30 days | Stickiness (DAU/WAU or analog) |
| Habituated | Multi-workflow + multi-user adoption | Workflow coverage % |
| Expanding | New seats / modules / use cases attached | Expansion lead indicators |
| At-risk | Engagement decay + stakeholder loss | Churn-risk score |
| Churned / Contracted | Logo or ARR loss | Reason-coded post-mortems |
Engagement Scoring
Engagement is depth × breadth × recency, not raw event counts.
| Dimension | Definition | Example |
|---|---|---|
| Depth | Frequency of core value events per active user | Core actions / week |
| Breadth | % of paid seats active + # of distinct workflows used | Seat activation, workflow coverage |
| Recency | Time since last value event, weighted exponentially | Decay half-life of 14-30 days |
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 · 105 lines · 61 tokens per session scan A 783f6a28d152
customer-analytics is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 61 tokens to every session and 1,241 once invoked, about $0.0002 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 2026-08-31.
Other skills, from other repositories
chief-data-officer-advisor
Chief Data Officer advisory for startups: AI training data rights and consent provenance, data product strategy (warehouse vs lakehouse vs mesh, build-vs-buy), B2B customer-data-as-asset valuation and M&A readiness, data team org evolution. Use when deciding whether to train models on customer data, choosing data…
vpe-advisor
VP of Engineering advisory for startups: delivery throughput (DORA 4 metrics + bottleneck identification), engineering hiring funnel (sourcing → screen → onsite → offer conversion + time-to-fill + pipeline gap), engineering team structure (squad/tribe/chapter design + tech-lead manager-trigger thresholds), and…
general-counsel-advisor
General Counsel advisory for startups: contract review (MSA, SaaS, NDA, DPA, employment), IP strategy, term sheet decoding, and regulatory landscape mapping. Use when reviewing any contract or term sheet, deciding when to engage outside counsel, defining IP strategy, evaluating regulatory exposure (HIPAA, GDPR, FDA…
strategic-alignment
Cascades strategy from boardroom to individual contributor. Detects and fixes misalignment between company goals and team execution. Covers strategy articulation, cascade mapping, orphan goal detection, silo identification, communication gap analysis, and realignment protocols. Use when teams are pulling in different…
board-meeting
Multi-agent board meeting protocol for strategic decisions. Runs a structured 6-phase deliberation: context loading, independent C-suite contributions (isolated, no cross-pollination), critic analysis, synthesis, founder review, and decision extraction. Use when the user invokes /c-level-agents:boardroom, calls a…
wrap-up
Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says "wrap up", "close this out", "what do I own now", "give me the summary", "recap the agent", or when the orchestrator routes…