Borrowing it
Nothing to install: this file belongs to LeanOS-Technologies/strategy-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/LeanOS-Technologies/strategy-os/main/.claude/skills/stg-designing-gtm/SKILL.mdgit clone --depth 1 https://github.com/LeanOS-Technologies/strategy-osWrote 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/leanos-technologies/strategy-os/stg-designing-gtm)<a href="https://agentmods.dev/skills/leanos-technologies/strategy-os/stg-designing-gtm"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-designing-gtm/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/leanos-technologies/strategy-os/stg-designing-gtm"><img src="https://agentmods.dev/badge/skills/leanos-technologies/strategy-os/stg-designing-gtm.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 | $0.00072 | $0.04937 |
| Opus 5 | $0.00036 | $0.02469 |
| Sonnet 5 | $0.00014 | $0.00987 |
| Haiku 4.5 | $0.00007 | $0.00494 |
Grade A, and why
stg-designing-gtm 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 10d 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 — 468 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GTM Plan Design
Design the complete go-to-market plan for the hypothesis register. Translate channel strategy, value proposition, growth architecture, and segment data into a phased execution plan with gates, KPIs, messaging, constraints, and kill criteria.
Procedure
Step 1: Load Inputs [S]
Read upstream outputs:
- Value Proposition (register section 4): claim, JTBD dimensions, differentiator, alternative, unique capability, clause validation status.
- Channel Strategy from Unit Economics (register section 3): ranked channel list with per-channel CAC, investment splits, phase sequencing, channel-economics coherence result.
- Growth Architecture (register section 5): PLG / Network / Sales-Led / Hybrid, required conditions, rationale.
- Segment Hypothesis (register section 2): primary segment, trigger event, budget owner, where they gather, observable characteristics, access paths.
- Pricing from Unit Economics (register section 3): ACV range, tier structure, upgrade triggers.
- Mode (VENTURE / BOOTSTRAP / HYBRID) from governor.
Assemble input summary with sources.
Gate: inputs_loaded: bool -- channel strategy, growth architecture,
value proposition, and segment hypothesis all available.
- Pass: Step 2.
- Fail: If channel strategy missing, cannot design GTM -- stg-designing-channels must run first. If growth architecture missing, infer from ACV and buyer type with reduced confidence and flag.
Step 2: Select GTM Architecture [R]
Match growth architecture to GTM execution pattern. The architecture constrains which GTM motions are viable. Getting this wrong misallocates every downstream dollar and hire.
| Growth Architecture | GTM Pattern | Primary Motion | Key Metric |
|---|---|---|---|
| PLG | Self-serve acquisition + expansion | Signup -> Activation -> Conversion -> Expansion | Activation rate (% reaching aha moment) |
| Network/Viral | Seed + grow network | Recruit constrained side -> density -> expansion | K-factor and cycle time |
| Sales-Led | Outbound + inbound pipeline | Pipeline generation -> qualification -> close | Pipeline-to-close rate |
| Hybrid | PLG bottom-up + sales expansion | Self-serve < $5K, sales-assist $5K-$50K, enterprise > $50K | PQL-to-close rate |
| Marketplace | Supply-side recruitment + demand activation | Curate supply -> create demand -> liquidity | Liquidity rate (% listings transacting) |
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.
- 10d ago First seen · 468 lines · 72 tokens per session scan A 18a73a7898e4
stg-designing-gtm is a skill published in the GitHub repository LeanOS-Technologies/strategy-os (37 stars, last pushed 4mo ago), licensed MIT. It adds 72 tokens to every session and 4,937 once invoked, about $0.0004 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 2026-08-30.
Other skills, from other repositories
email-sequence
When the user wants to create or optimize an email sequence, drip campaign, automated email flow, or lifecycle email program. Also use when the user mentions "email sequence," "drip campaign," "nurture sequence," "onboarding emails," "welcome sequence," "re-engagement emails," "email automation," or "lifecycle…
llm-app-patterns
Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
qa-test-planner
Generate comprehensive test plans, manual test cases, regression test suites, and bug reports for QA engineers. Includes Figma MCP integration for design validation.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.
content-research-writer
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
training-llms-megatron
Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA…