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-successgit 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-success)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/customer-success"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/customer-success/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-success"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/customer-success.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 | $0.01326 |
| Opus 5.5 | $0.00032 | $0.00530 |
| Sonnet 5.5 | $0.00016 | $0.00265 |
| Haiku 4.5 | $0.00008 | $0.00133 |
Grade A, and why
customer-success 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Customer Success (THRIVE Framework)
Design a Customer Success operating system that turns post-sale into a predictable revenue engine. The skill enforces explicit coverage tiering, a health score that actually predicts churn, named risk playbooks, value reviews that earn renewals, and expansion motions that move NRR - instead of a generic "we should do QBRs" plan.
Core Principle
Customer Success is a coverage and signal problem, not a relationship problem. Most CS teams over-invest in friendly check-ins with healthy accounts and under-invest in early risk signals and value proof. THRIVE forces tiered coverage, leading indicators, and renewal-defensible value evidence.
The THRIVE Framework
| Letter | Stage | The Question |
|---|---|---|
| T | Tier the Book | Which accounts get high-touch, tech-touch, or pooled coverage - and why? |
| H | Health Scoring | What 4-6 leading signals predict churn 90+ days out? |
| R | Risk Playbooks | When a signal trips, what named play runs in what timeframe? |
| I | Insight Reviews | What evidence of value gets shown at each review cadence? |
| V | Value Realization | How is realized ROI captured and quantified before renewal? |
| E | Expansion Motion | Which signals trigger which expansion play, and who owns the handoff? |
Coverage Tiering
| Tier | ARR Band | Coverage | Cadence | Primary Goal |
|---|---|---|---|---|
| High-touch | Top 10-20% of ARR | Named CSM, exec sponsor | Monthly check-in + quarterly EBR | NRR > 120% |
| Mid-touch | Mid 30-60% | Pooled CSM, named for risks | Quarterly value review | GRR > 92% |
| Tech-touch | Long tail | Digital programs, in-product nudges | Automated lifecycle | Self-serve renewal > 80% |
Health Score Design
A useful health score uses leading, not lagging, signals. Lagging scores (NPS, ticket volume, login count) confirm churn after it's already locked in.
| Signal Type | Example | Why It Leads |
|---|---|---|
| Adoption depth | % of paid seats active weekly on critical workflows | Predicts contract value justification |
| Outcome attainment | Customer-defined success metric progress | Predicts renewal defensibility |
| Stakeholder coverage | # of named champions + exec sponsor engaged in last 90 days | Predicts survivability of champion change |
| Support signal velocity | Critical-severity tickets trend (not count) | Predicts frustration cliff |
| Commercial signal | Procurement contact, contract questions, late payment | Predicts negotiation posture |
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 · 93 lines · 79 tokens per session scan A d0a102fafee3
customer-success is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 79 tokens to every session and 1,326 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 2026-08-31.
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