gtm-strategist-skills: Skill for Claude Code

.claude/skills/building-product/SKILL.md

building-product is a skill for Claude Code from GTM-Strategist/gtm-strategist-skills. It costs 79 tokens per session (5,564 once invoked), scanned A, original, MIT.

A guided product-planning workflow for turning customer research into an MVP, roadmap, value proposition, and measurement plan. An MVP is the smallest useful version of a product.

In plain words
What is it for?
Use it to refine a value proposition with the Jobs to Be Done method, define an MVP, plan a product roadmap, choose product metrics, and run usability tests.
Why use it?
It helps connect what customers need with what the team should build first. It also keeps product decisions organized and testable.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is GTM-Strategist/gtm-strategist-skills's own configuration. It tells Claude Code how to work on gtm-strategist-skills itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything gtm-strategist-skills configures →

Part of the .claude plugin — 12 skills shipped together

Reuse

Borrowing it

Nothing to install: this file belongs to GTM-Strategist/gtm-strategist-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/GTM-Strategist/gtm-strategist-skills/master/.claude/skills/building-product/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/GTM-Strategist/gtm-strategist-skills

Made for: Claude Code.

Or install .claude, the plugin that ships this one along with the rest of its 12 skills.

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 building-product

README.md
[![agentmods](https://agentmods.dev/badge/skills/gtm-strategist/gtm-strategist-skills/building-product/github.svg)](https://agentmods.dev/skills/gtm-strategist/gtm-strategist-skills/building-product)
Your own site
<a href="https://agentmods.dev/skills/gtm-strategist/gtm-strategist-skills/building-product"><img src="https://agentmods.dev/badge/skills/gtm-strategist/gtm-strategist-skills/building-product/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.

agentmods 80×15 button for building-product

Your own site · 80×15
<a href="https://agentmods.dev/skills/gtm-strategist/gtm-strategist-skills/building-product"><img src="https://agentmods.dev/badge/skills/gtm-strategist/gtm-strategist-skills/building-product.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,564 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00079 $0.05564
Opus 5 $0.00039 $0.02782
Sonnet 5 $0.00016 $0.01113
Haiku 4.5 $0.00008 $0.00556

Measured 12d ago against content hash ad4b76d25d35, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-product 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 12d 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.

.claude/skills/building-product/SKILL.md · 491 lines

How it starts

The opening of the file, as written. The whole thing — 491 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Building Product (Phase 4 of 12)

You are a go-to-market strategist guiding the user through Phase 4: Early Product Work. This phase translates validated customer insights into a buildable, testable, launchable product.

Before You Start

  1. Read my-gtm-context.md — you need the user's product, ICP, stage, team, and constraints.
  2. Check outputs/ for prior phase deliverables:
    • outputs/01-*.md (Phase 1: OPE canvas, SWOT, value proposition, 90-day plan)
    • outputs/02-*.md (Phase 2: beachhead segments, interviews, competitor analysis)
    • outputs/03-*.md (Phase 3: validated persona, assumption map, experiments, ICP/ECP)
  3. If Phase 3 outputs are missing, warn the user: "Phase 4 builds directly on your validated customer profile (ECP) from Phase 3. Without it, we're guessing at what to build. Want to complete Phase 3 first, or proceed with what we have?"
  4. Work through tasks one at a time. Present the deliverable, get feedback, then move to the next.

Output Files

Each task saves its deliverable to outputs/ with this naming:

Task Output File
1. Refine Value Proposition — JTBD outputs/04-jtbd-value-proposition.md
2. Product Roadmap outputs/04-product-roadmap.md
3. Ownership & Tools outputs/04-ownership-and-tools.md
4. Create MVP outputs/04-mvp-definition.md
5. Metrics & Analytics outputs/04-metrics-and-analytics.md
6. Tracking Plan outputs/04-tracking-plan.md
7. Bug Hunting outputs/04-bug-hunting-plan.md
8. Usability Testing outputs/04-usability-testing-plan.md
9. Pre-Mortem Workshop & FAQ outputs/04-pre-mortem-and-faq.md

Task 1: Refine Value Proposition — JTBD

Duration: 1-3 hours | Depends on: Phase 3 ECP, Phase 1 value proposition

Now that the user has a validated Early Customer Profile from Phase 3, go back to the value proposition and refine it using the Jobs-To-Be-Done framework.

Framework: Huryn & Abdul Rahul's JTBD Value Proposition Canvas

Read the full file on GitHub · 491 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. 12d ago First seen · 491 lines · 79 tokens per session scan A ad4b76d25d35

Subscribe to this mod's changes

building-product is a skill published in the GitHub repository GTM-Strategist/gtm-strategist-skills (254 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 5,564 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.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens