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 growth-loopgit 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/growth-loop)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/growth-loop"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/growth-loop/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/growth-loop"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/growth-loop.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 | $0.00048 | $0.03299 |
| Opus 5.5 | $0.00019 | $0.01320 |
| Sonnet 5.5 | $0.00010 | $0.00660 |
| Haiku 4.5 | $0.00005 | $0.00330 |
Grade A, and why
growth-loop 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Growth Loop (ANCHOR System)
A structured lifecycle engine that designs every phase of post-acquisition customer engagement -- from initial activation through renewal and advocacy. ANCHOR maximizes net revenue retention by combining health scoring, trigger-based expansion plays, and systematic advocacy development into a unified retention and growth system.
When to Use
- Designing a post-launch customer success and retention strategy
- Building a customer health scoring model to predict and prevent churn
- Creating expansion playbooks for upsell and cross-sell motions
- Developing customer advocacy programs (case studies, referrals, advisory boards)
- Improving net revenue retention (NRR) and reducing logo churn
- Structuring the handoff from launch/acquisition to ongoing customer success
- Building automated lifecycle programs with trigger-based interventions
What You'll Need
Critical inputs (ask if not provided):
- Product name, pricing model, and customer segments
- Current retention and churn rates (or estimates)
- Key value moments and activation milestones in the product
- Customer support and success team structure
- Expansion products or tiers available for upsell/cross-sell
- Revenue targets (NRR, expansion revenue, logo retention)
Nice-to-have:
- Customer journey maps (from journey-architect)
- Product usage analytics and feature adoption data
- NPS or CSAT baseline scores
- Existing health scoring model (if any)
- Competitive switching triggers (from battle-scanner)
- Launch metrics framework (from launch-pulse)
Process
Step 1: Map the ANCHOR Lifecycle Stages
Define six lifecycle stages with specific timeframes, goals, and success criteria.
| Stage | Timeframe | Goal | Key Metric | Exit Criteria |
|---|---|---|---|---|
| A - Activate | Days 0-7 | First value moment achieved | Time to Value (TTV) | User completes core activation action |
| N - Nurture | Days 7-30 | Expand usage breadth and depth | Feature adoption rate | 3+ features adopted, regular usage pattern |
| C - Champion | Days 30-90 | Identify and cultivate internal advocates | Champion score | At least 1 identified champion per account |
| H - Habituate | Days 90-180 | Embed product into daily workflows | DAU/WAU ratio | Stickiness ratio >50%, multi-team usage |
| O - Optimize | Days 180-365 | Demonstrate and quantify business ROI | ROI documentation | ROI report delivered, value validated |
| R - Renew | 60 days pre-renewal | Secure renewal and identify expansion | Renewal rate | Renewal committed, expansion opportunity identified |
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 · 234 lines · 48 tokens per session scan A b225cd91c80c
growth-loop is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 48 tokens to every session and 3,299 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.
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