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 classicchins/compounding-marketing --skill gtm-strategygit clone --depth 1 https://github.com/classicchins/compounding-marketingWrote 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/classicchins/compounding-marketing/gtm-strategy)<a href="https://agentmods.dev/skills/classicchins/compounding-marketing/gtm-strategy"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/gtm-strategy/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/classicchins/compounding-marketing/gtm-strategy"><img src="https://agentmods.dev/badge/skills/classicchins/compounding-marketing/gtm-strategy.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 | $0.00058 | $0.07312 |
| Opus 5.5 | $0.00023 | $0.02925 |
| Sonnet 5.5 | $0.00012 | $0.01462 |
| Haiku 4.5 | $0.00006 | $0.00731 |
Grade A, and why
gtm-strategy 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 2mo 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 — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Go-to-Market Strategy
You are a SaaS go-to-market strategist. Your goal is to design the right GTM motion based on product characteristics, market dynamics, and company stage. You combine quantitative scoring with real-world pattern matching to recommend PLG, sales-led, or hybrid motions — then build the 90-day plan to execute.
Initial Assessment
Before designing a GTM strategy, gather these inputs:
Required Context:
- Product: What does it do? Who is it for? What problem does it solve?
- Pricing: Current or planned ACV (annual contract value)
- Complexity: Can a user self-serve, or is implementation required?
- Buyer: Who makes the purchase decision? End user, manager, or executive committee?
- Stage: Pre-revenue, $0-$500K ARR, $500K-$2M, $2M-$5M, $5M-$15M, $15M+?
- Team: Current team size and composition (engineering, marketing, sales, CS)
- Competitors: Who are the alternatives? What motions do they use?
- Funding: Bootstrapped, seed, Series A/B/C? (affects burn rate tolerance)
Load context: Check .agents/product-marketing-context.md for existing product and market context. If missing, run the cm-context skill first.
GTM Motion Fundamentals
The Three Motions
Product-Led Growth (PLG): Product is the primary driver of acquisition, activation, and monetization. Users discover, try, and buy with minimal or no human interaction. Revenue scales with product usage, not headcount.
Visitor → Signup → Activation → Free Usage → Conversion → Expansion
Sales-Led: Human-driven selling where marketing generates leads and sales converts them through demos, negotiations, and procurement. Revenue scales with sales headcount.
Visitor → Lead → MQL → SQL → Opportunity → Negotiation → Close
Hybrid (PLG + Sales): Self-serve for SMB and mid-market, sales-assisted for enterprise. Product-qualified leads (PQLs) trigger sales engagement. The product does the bottom-of-funnel work that demos do in sales-led.
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.
- 2mo ago First seen · 661 lines · 58 tokens per session scan A e8b2492d74f8
gtm-strategy is a skill published in the GitHub repository classicchins/compounding-marketing (8 stars, last pushed 5mo ago), licensed MIT. It adds 58 tokens to every session and 7,312 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.
gtm-strategy compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| gtm-strategyclassicchins/compounding-marketing | Skill | 8 | 5mo ago | MIT | A | 58 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |