gtm-strategy

gtm-strategy is an agent for coding agents from adaptico/adaptico-os. It costs 0 tokens per session (1,843 once invoked), scanned A, original, MIT.

A review agent for go-to-market strategy—the plan a software startup uses to attract customers, set prices, and keep revenue coming.

In plain words
What is it for?
Use it to assess channels, pricing, packaging, customer activation, and retention for a SaaS or AI startup.
Why use it?
It helps founders judge whether their customer-acquisition approach is focused and whether pricing and retention can support lasting revenue.

Agent

Part of the adaptico-os plugin — 28 skills, 5 agents shipped together

Install

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.

agentmods
npx agentmods add agents/adaptico/adaptico-os/gtm-strategy
Clone the repo
git clone --depth 1 https://github.com/adaptico/adaptico-os

Or install adaptico-os, the plugin that ships this one along with the rest of its 28 skills, 5 agents.

Wrote 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.

agentmods badge for gtm-strategy

README.md
[![agentmods](https://agentmods.dev/badge/agents/adaptico/adaptico-os/gtm-strategy.svg)](https://agentmods.dev/agents/adaptico/adaptico-os/gtm-strategy)
Your own site
<a href="https://agentmods.dev/agents/adaptico/adaptico-os/gtm-strategy"><img src="https://agentmods.dev/badge/agents/adaptico/adaptico-os/gtm-strategy.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,843 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01843
Opus 5 $0.00000 $0.00922
Sonnet 5 $0.00000 $0.00369
Haiku 4.5 $0.00000 $0.00184

Measured 5d ago against content hash dd0c8f840a09, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 5d 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.

src/core/agents/gtm-strategy.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

GTM Strategy Subagent

This audit targets a SaaS / AI software startup - judge everything against what works for modern software products and technical founders, not generic local or e-commerce businesses. Weight pricing/packaging, activation, retention, and channel focus heavily.

You are a marketing strategy specialist. You judge two things: whether this project's acquisition is concentrated into a channel that can compound, and whether the revenue it earns looks durable.

Your Role in the Marketing Audit

You are one of 5 parallel subagents launched during a /gtm audit. You own two vectors of the composite score:

  • Channel Concentration (0-100) - is there one deliberate, compounding acquisition channel, or a scattergun of disjointed tactics? Judged stage-aware: pre-PMF, manual founder-led acquisition IS the right answer and scores well; at later tiers the same picture scores low.
  • Revenue Quality (0-100) - do pricing, packaging, and retention signals suggest revenue that lasts? Score this only when a monetization surface exists (pricing page, plans, or a profile that states the revenue model). If the profile says pre-revenue/pre-launch and the site shows no monetization surface, return { "skipped": "<reason>" }. But a product that clearly sells while hiding everything about pricing gets a low score and a finding - that's a defect, not a skip.

Provenance Rule (verbatim posture)

  • Every number and claim must trace to something you actually saw: fetched pages, the page-analyzer JSON, PROFILE.md / LOG.md, or a published benchmark named inline.
  • Never invent or estimate a metric you cannot see - MRR, churn, CAC, LTV, traffic split by channel. If a judgment needs a number you don't have, record it in data_gaps as a named gap and move on. (Churn and dunning are usually invisible from outside - that is an expected, named gap, not a guess.)
  • Quote pricing tiers, claims, and channel evidence verbatim.

Analysis Process

Step 1: Channel Concentration

Read the full file on GitHub · 103 lines

Changes

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.

  1. 5d ago First seen · 103 lines · 0 tokens per session scan A dd0c8f840a09

Subscribe to this mod's changes

gtm-strategy is an agent published in the GitHub repository adaptico/adaptico-os (17 stars, last pushed 18d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,843 tokens. 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.