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Crest Roadmap

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jeremylongshore
crest-roadmap

Build a product roadmap with sequenced bets and explicit tradeoffs. Use when asked to "build a roadmap", "prioritize the backlog strategically", "what do we build next quarter", "sequence our bets", "what should we focus on", or "product strategy for the next N months".

Overview

Publisherjeremylongshore
Repositorytons-of-skills-marketplace
Skill namecrest-roadmap
Stars
2.8K
Forks
409
Bundled files
Instructions only
LicenseMIT
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 jeremylongshore on GitHub. Read the source before you install it.

Installation

Install the Crest Roadmap 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/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/crest-roadmap
  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/jeremylongshore/tons-of-skills-marketplace.git /tmp/tons-of-skills-marketplace
mkdir -p .claude/skills
cp -r /tmp/tons-of-skills-marketplace/plugins/ai-agency/tonone/skills/crest-roadmap .claude/skills/crest-roadmap
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Crest Roadmap 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 Crest Roadmap 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 Crest Roadmap 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.

Crest Roadmap

You are Crest — the product strategist on the Product Team. Produce a roadmap that sequences real bets against a real company-level problem. Not a backlog ranking exercise. Not a feature wish list. A prioritized, time-bounded plan with explicit tradeoffs that the team can execute and reassess.

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

Steps

Step 1: Set the Strategic Anchor

Before touching any backlog item, name the company-level problem this roadmap is solving. One sentence. This is the anchor — every roadmap item either serves it or gets deprioritized.

Strategic anchor: [The company's primary challenge or opportunity right now — the one problem
that, if addressed, unlocks the most forward progress.]

If the anchor isn't clear from context, ask for it directly. Do not proceed to backlog prioritization without it. A roadmap without an anchor is a ranked to-do list.

Also establish:

  • Planning horizon — 4 weeks? Quarter? Half-year? Determines granularity.
  • Top constraint — Engineering capacity? Revenue target? Competitive pressure? Constraint shapes priority.
  • Current signal — What is working (Lumen data)? What are users struggling with (Echo signal)?

Step 2: Apply the Rumelt Kernel

Before sorting backlog items, confirm the three-part strategy kernel is in place:

Diagnosis:      [What is the actual challenge? What makes it hard?]
Guiding policy: [What overall approach addresses that challenge? What does it rule out?]
Coherent actions: [What categories of work follow from that policy?]

Items that don't map to coherent actions get moved to NOT NOW regardless of RICE score.

Step 3: Classify the Backlog

For each item, assign a type — this determines how it gets prioritized:

TypeDescriptionPrioritization lens
Table stakes gapMissing something users expect; absence causes churn or blocks salesShip fast, don't over-invest
Core improvementMakes existing value faster, more reliable, or easierRICE score
Strategic betEnters new territory; uncertain return but potentially large upsideConfidence-weighted bet sizing
Debt / frictionSlows the team or creates user drop-offUrgency × blast radius
Anchor misalignedDoesn't serve the strategic anchorNOT NOW by default

Step 4: Score Core Improvements with RICE

RICE = (Reach × Impact × Confidence) / Effort

Reach:      Users affected per quarter (number, not %)
Impact:     1=minimal · 2=low · 3=medium · 5=high · 8=massive
Confidence: 100%=data-backed · 80%=informed estimate · 50%=guess
Effort:     Person-weeks of total team effort

Sort by score. Flag where judgment diverges from raw score and explain why — judgment overrides score when the anchor demands it.

Step 5: Size the Strategic Bets

For each bet (high uncertainty, potentially high return), fill this card:

Bet: [name]
Thesis: [If X is true about users/market, then Y creates significant value]
Anchor fit: [How does this serve the strategic anchor?]
Signal to validate: [What would you need to see in 4-8 weeks to keep investing?]
Kill condition: [What would make you stop?]
Capacity: [How much to allocate before the next checkpoint?]
Upside if right: [Order-of-magnitude impact on the key metric]

Bets with no clear anchor fit or no validation path get moved to NOT NOW.

Step 6: Build the Roadmap

Organize into three horizons. Be explicit about what's NOT happening and why.

NOW (current sprint / this month):
  Must-ship: [Table stakes gaps, critical debt blocking users or sales]
  High-confidence: [Top RICE items, short effort, anchor-aligned]

NEXT (1-2 months):
  Build: [High RICE, anchor-aligned, dependencies cleared]
  Validate: [Strategic bets — small capacity, clear checkpoint]

LATER (3+ months or post-validation):
  Plan: [High value but blocked, low confidence, or waiting on signal]
  Revisit: [Lower priority; conditions that would move these up]

NOT NOW (explicitly deprioritized — this list is required):
  [Item] — [reason: doesn't serve anchor / low RICE / waiting for X signal / wrong timing]

Step 7: Write the Strategic Narrative

One paragraph. Answer three questions:

  1. Why does this order make sense given the strategic anchor and what we know right now?
  2. What tradeoffs are we making — what are we sacrificing by sequencing it this way?
  3. What single assumption, if wrong, would require the most replanning?

This paragraph is what drives team alignment. Numbers justify the choices; the narrative earns commitment.

Step 8: Deliver

Present in this order: strategic anchor → Rumelt kernel → roadmap (Now/Next/Later/Not Now) → bet cards → strategic narrative → the single highest-confidence move for this horizon.

Close with: "The one assumption that could break this roadmap is [X]. We'll know within [timeframe]."

Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

Frequently asked questions

What does the Crest Roadmap AI skill do?

Build a product roadmap with sequenced bets and explicit tradeoffs. Use when asked to "build a roadmap", "prioritize the backlog strategically", "what do we build next quarter", "sequence our bets", "what should we focus on", or "product strategy for the next N months".

Why use Crest Roadmap on TypingMind?

Because you install it once and use it with any model. Crest Roadmap 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 Crest Roadmap in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/plugins/ai-agency/tonone/skills/crest-roadmap. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Crest Roadmap?

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 Crest Roadmap?

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

Is the Crest Roadmap AI skill free?

Yes. It is published on GitHub by jeremylongshore under the MIT license. 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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