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Roadmap From Strategy

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ojiudezue
roadmap-from-strategy

Use when the user is building, pruning, or rationalizing a roadmap. Triggers on phrases like "roadmap", "what should we build next", "prioritize the backlog", "KTLO", "feature list", "Q3 plan", "what's in v2", "quarterly planning". Implements the Problem-Ordered Roadmap framework from Ch 14 + Epilogue of Building Rocketships.

Overview

Publisherojiudezue
Repositoryproductmind-skills
Skill nameroadmap-from-strategy
Stars
81
Forks
18
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ojiudezue on GitHub. Read the source before you install it.

Installation

Install the Roadmap From Strategy AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ojiudezue/productmind-skills/tree/main/skills/roadmap-from-strategy
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ojiudezue/productmind-skills.git /tmp/productmind-skills
mkdir -p .claude/skills
cp -r /tmp/productmind-skills/skills/roadmap-from-strategy .claude/skills/roadmap-from-strategy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Roadmap From Strategy in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Roadmap From Strategy on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Roadmap From Strategy is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Roadmap From Strategy

What this does

Runs Ch 14 + Epilogue's Problem-Ordered Roadmap framework. Takes the user's strategy (or vmsoop-strategy output) and a backlog, returns a roadmap with items ordered by sharp problem (not feature), each item carrying a customer story, capacity allocation across 80% new build / 20% KTLO, and an explicit halt rule if KTLO drifts above 40%. Refuses to recommend feature-list roadmaps with no traceability to sharp problems.

When to use

Trigger when the user:

  • Has a backlog of features and needs to decide what to ship
  • Is building a quarterly or annual plan
  • Has a roadmap but it's just a feature list with no rationale
  • Has KTLO debt eating into new work
  • Wants to write a roadmap from a strategy that's already defined

When NOT to use

  • No strategy exists yet — run vmsoop-strategy first; a roadmap without strategy is a guess
  • Pre-PMF — there is no roadmap pre-PMF; there is a discovery backlog; use customer-discovery-week and sharp-problem-test
  • User wants to choose a single growth bet — that's growth-lever-picker

The framework (from Ch 14 + Epilogue)

Item shape

Every roadmap item has FOUR fields. Missing any one = not a roadmap item, just a wish.

  1. Sharp problem — the named customer problem this solves (cites sharp-problem-test output if available)
  2. Customer story — the verbatim version of the problem from discovery
  3. Strategy linkage — which strategy lever from VMSOO-P this pulls
  4. Success metric — which Tier 1 or Tier 2 metric moves

Ordering rule

Order by sharp-problem priority, not by effort or by who asked. The book is explicit: "the easiest thing to ship is not the most important thing to ship."

Capacity allocation

  • 80% — new build (problem-solving)
  • 20% — KTLO (Keep The Lights On — performance, bug debt, infrastructure, compliance, security)

The halt rule

If KTLO drifts above 40% of capacity, halt all new features and pay down debt. Continuing past 40% accumulates compounding cost and demoralizes the team.

40% is the book's hard line. Between 20% and 40%, the team gets a warning and a paydown plan. Above 40%, no exceptions.

Refusal mode

If the user provides a feature list with no strategy or sharp-problem linkage, refuse to produce a roadmap. Return NEEDS_MORE_INPUT and name the gap. Producing a "roadmap" from a feature list with no problem trace is just rubber-stamping the wishlist.

If KTLO is reported above 40%, override the user's roadmap request and recommend the paydown plan. The 40% line is non-negotiable.

If the strategy has no Opportunity Costs defined, the roadmap will have nothing to cut and become bloated. Recommend running vmsoop-strategy to surface them.

Inputs

  • Strategy summary (from vmsoop-strategy or written)
  • North Star + Tier 1 metrics
  • Current backlog or candidate items
  • Current KTLO ratio (if known)
  • Team capacity (engineer-weeks per quarter)
  • (Optional) constraints — fixed deadlines, must-ship, etc.

Output structure

## Roadmap verdict: [READY | NEEDS_MORE_INPUT | HALT_FOR_KTLO]

### Capacity allocation
- New build: ___% (target 80%)
- KTLO: ___% (target 20%, hard line 40%)
- If KTLO > 40%: halt and follow the paydown plan below

### Ordered roadmap
| Order | Item | Sharp Problem | Customer Story | Strategy Lever | Success Metric | Estimate |
|---|---|---|---|---|---|---|
| 1 | ... | ... | ... | ... | ... | ... |

### Items cut and why
- [Cut item] — [why, opportunity-cost rationale]

### KTLO paydown (if applicable)
- [Item + estimate]

### Quarterly milestones
- Q1: [items shipping]
- Q2: [items shipping]

### Recommendation
[Concrete next step — typically 'communicate the order to engineering with the stories attached']

### Analogous case from the book
[A named example]

Worked example from the book

Calendly's roadmap (Ch 14)

  • North Star: weekly bookings through the platform
  • Strategy levers: viral free tier, integration ubiquity, enterprise admin
  • Backlog: 24 features ranked by sharp-problem priority:
    • #1 — async meeting reminders (sharp problem: no-show rate; strategy: activation/retention; Tier 1: invite-to-booking conversion)
    • #2 — Zoom integration (sharp problem: bookers want video automatically; strategy: integration ubiquity; Tier 1: weekly active schedulers)
    • #3 — round-robin team scheduling (sharp problem: team-based scheduling friction; strategy: expansion; Tier 1: ACV)
    • ...
  • KTLO budget: 20% (calendar API breakage handling, performance, security review)
  • Cut: profile-photo customization (no sharp problem; opportunity cost: time better spent on Zoom integration)

Gotchas

  • Feature lists are not roadmaps. If your items don't cite a sharp problem and a customer story, you're shipping inventory, not strategy.
  • KTLO compounds. A quarter of 30% KTLO becomes 40% the next quarter. The 40% halt rule exists because the math doesn't lie.
  • Effort-ordering is the easiest trap. Doing the easy items first feels productive and accumulates technical debt and strategy debt simultaneously.
  • Customer stories must be verbatim. Paraphrased customer stories become feature requests. Verbatim ones force you to honor the problem.

Anti-patterns to flag

If the user's plan has any of these, surface them:

  • Feature list with no sharp problem trace — refuse to call it a roadmap; route to sharp-problem-test for the backlog items
  • KTLO > 40% but new features still on the plan — halt
  • "Roadmap" ordered by who asked loudest — recommend re-ordering by sharp-problem priority
  • No items cut from the backlog — opportunity-cost discipline missing; flag the strategy gap
  • Six months of work that won't move any Tier 1 metric — re-prioritize or simplify the strategy

Source

Ch 14: Roadmapping from strategy. Epilogue: The 80/20 build/KTLO rule.


About this skill

This skill is part of the ProductMind Skills catalog — open-source product-judgment skills for AI coding agents.

Source: Building Rocketships by Oji and Ezinne Udezue — Chapter 14. This skill applies one named framework from the book. It works standalone in any agent that supports the SKILL.md standard.

Want the orchestrated version? The RocketshipsGPT agent runs all 13 book skills together, with persistent memory, framework-fidelity evals, voice-checked output, and the book's anti-pattern catches built in. → rocketshipsgpt.productmind.co

License: Skill content licensed under CC BY-SA 4.0. Attribution required; derivative works must share alike. See ADR 0005 for rationale.

Skill version: 1.0.0 · Published: 2026-06-05

Frequently asked questions

What does the Roadmap From Strategy AI skill do?

Use when the user is building, pruning, or rationalizing a roadmap. Triggers on phrases like "roadmap", "what should we build next", "prioritize the backlog", "KTLO", "feature list", "Q3 plan", "what's in v2", "quarterly planning". Implements the Problem-Ordered Roadmap framework from Ch 14 + Epilogue of Building Rocketships.

Why use Roadmap From Strategy on TypingMind?

Because you install it once and use it with any model. Roadmap From Strategy is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Roadmap From Strategy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ojiudezue/productmind-skills/tree/main/skills/roadmap-from-strategy. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Roadmap From Strategy?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Roadmap From Strategy?

As many as you like. As long as a model supports skills, you can use Roadmap From Strategy with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Roadmap From Strategy AI skill free?

It is published on GitHub by ojiudezue. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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