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 Uxcel-Lab/product-skills --skill gtm-plangit clone --depth 1 https://github.com/Uxcel-Lab/product-skillsWrote 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/uxcel-lab/product-skills/gtm-plan)<a href="https://agentmods.dev/skills/uxcel-lab/product-skills/gtm-plan"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/gtm-plan/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/uxcel-lab/product-skills/gtm-plan"><img src="https://agentmods.dev/badge/skills/uxcel-lab/product-skills/gtm-plan.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 | $0.00134 | $0.03269 |
| Opus 5 | $0.00067 | $0.01635 |
| Sonnet 5 | $0.00027 | $0.00654 |
| Haiku 4.5 | $0.00013 | $0.00327 |
Grade A, and why
pm-gtm-plan 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 11d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Go-to-Market Plan Skill
How this skill behaves (read first)
This is a generative deliverable skill, and "write a GTM plan" is where an AI assistant defaults to a generic launch checklist — announce, blog post, social, email, press — with no strategy underneath. The deeper mistakes: treating GTM as a one-time launch rather than a living system, stage-skipping (bolting a reseller network or paid-acquisition engine onto a company with 8 customers), ignoring timing (seasonality, B2B budget cycles, market readiness), and funnel-forcing buyers through a pipeline on the company's schedule. A GTM plan that doesn't fit the company's stage burns the resources that should be going to the actual foundational work.
So this skill gates hard:
- Establish context — GTM readiness (is demand validated?), company maturity stage, customer/business-model type, and timing conditions.
- Apply the always-true core — GTM as a system of five interlocking components, stage-appropriate execution, channel-as-cost-structure, timing awareness, non-linear buyer journey.
- Surface the context-dependent decisions (stage tactics, channel model, business-model motion, entry timing, partnerships) with trade-offs; let the user choose.
Then it hands off to pm-okr-metric-validity-audit (are the launch metrics valid, not vanity?) and pm-assumption-rigor-audit (is demand really validated, is the timing window real, will the channel actually work?).
Scope: this skill owns the go-to-market plan and its launch execution. It defers product strategy to pm-vision-strategy, positioning and competitive set to pm-competitive-analysis, pricing strategy to ux-pricing, buyer-journey/funnel analytics to pm-analytics, and post-launch experiments/learning to pm-experimentation-ab.
Step 0 — Establish context before planning
Ask if not known; state the assumption if proceeding without an answer:
- Is the product GTM-ready? No GTM plan repairs a product without validated demand. Readiness test: have real customers paid (or seriously committed), used it repeatedly, and can you explain why they bought and what would make them leave? If not, route back to validation (
pm-discovery,pm-experimentation-ab) before building channels and pricing. - What's the company maturity stage? Level 1 early traction (3–5 customers, manual) → Level 2 demand generation (5–25, one channel experiment) → Level 3 efficiency (25–100+, 2–3 proven channels) → Level 4 scaling TAM (platform/partnership decisions). Stage is set by context, not just customer count — business model, ticket size, customer type, and relationship complexity (3 enterprise contracts ≠ 3 consumer signups).
- Who's the customer / what's the business model? Consumer/prosumer (self-serve, product sells itself), SMB (weeks, light sales support), enterprise (6–18 mo, multi-stakeholder, consultative); and B2B / B2C / B2G / B2B2C — each changes the motion.
- What timing conditions apply? Seasonality, B2B budget cycle, first-mover vs. fast-follower position, and technology/regulatory readiness. Name these before setting the calendar.
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.
- 11d ago First seen · 128 lines · 134 tokens per session scan A f5b0bca0377c
pm-gtm-plan is a skill published in the GitHub repository Uxcel-Lab/product-skills (10 stars, last pushed 2mo ago), licensed MIT. It adds 134 tokens to every session and 3,269 once invoked, about $0.0007 per session on Opus 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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