Foundation Build Risk Review logo

Foundation Build Risk Review

Organization
product-on-purpose
foundation-build-risk-review

Runs a fast pre-build risk review on a product idea, feature request, or scope change, naming the single assumption most likely to make it fail and returning a clear verdict (build small, validate first, pivot first, or don't build yet) with a no-code validation step. Use before committing build effort, when triaging whether to honor a feature request, or when deciding whether to expand scope, ahead of writing a PRD. For a launched product's pivot-or-persevere decision, use iterate-pivot-decision instead.

Overview

Publisherproduct-on-purpose
Repositorypm-skills
Skill namefoundation-build-risk-review
Stars
717
Forks
92
Bundled files
7
LicenseApache-2.0
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.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by product-on-purpose on GitHub. Read the source before you install it.

Installation

Install the Foundation Build Risk Review 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/product-on-purpose/pm-skills/tree/main/skills/foundation-build-risk-review
  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/product-on-purpose/pm-skills.git /tmp/pm-skills
mkdir -p .claude/skills
cp -r /tmp/pm-skills/skills/foundation-build-risk-review .claude/skills/foundation-build-risk-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Foundation Build Risk Review 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 Foundation Build Risk Review 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 Foundation Build Risk Review 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.

Build Risk Review

Don't build it yet. First name the one assumption most likely to make it fail.

foundation-build-risk-review is a fast, pre-commitment gate for product decisions. Given an idea, a feature request, or a scope change, it returns a Build Risk Review: the single biggest risk, the evidence behind it, a verdict, and a concrete no-code validation step, then routes you to the skill that does the next piece of work. It is a foundation hub: its job is to triage and dispatch, not to duplicate the deeper skills.

Hard gate

Do not write code, scaffold a project, recommend a stack, or design implementation. First answer three things: should this be built, what is most likely to make it fail, and what must be validated before committing.

If the user says the work is for learning, a portfolio, or internal practice, do not judge it by market standards; still flag scope and clarity risks.

When to Use

  • A product idea, MVP, or new bet is about to turn into build work.
  • A feature request or scope change has arrived and you need to separate real demand from a polite ask, founder anxiety, or competitor-copying.
  • Someone wants a fast "should we build this?" verdict before a PRD, roadmap row, or ticket exists.

When NOT to Use

If the ask isUse instead
A launched product's pivot-or-persevere call, weighing usage or market dataiterate-pivot-decision
You have chosen the assumption and need to design the testdefine-hypothesis
Framing a confirmed problem for the team or leadershipdefine-problem-statement
The full nine-block business model, not a single-risk readfoundation-lean-canvas
Ranking many features or initiatives against each otherdefine-prioritization-framework

The boundary that matters most: this skill is forward-looking and pre-commitment (low or no data); iterate-pivot-decision is retrospective and post-launch (it weighs market feedback on something already shipped).

Modes (route first; state the mode at the top)

  1. Pre-build - a new idea, product, or MVP not yet built. The usual primary risks: demand and distribution.
  2. Feature-change - a feature request, scope expansion, requirement change, or competitor-copy on an in-progress product. The primary tool here is the demand hierarchy.

If the product is already launched and the question is whether to change direction, hand off to iterate-pivot-decision. If the request is too broad to review responsibly, ask exactly one clarifying question (complete the sentence: "this is for [who] in [situation] to solve [problem]"), then proceed. Never run a long questionnaire; at most two questions before a constrained review.

The review (the contract)

Produce a Build Risk Review with these parts:

  1. Biggest risk (R1). Exactly one primary risk, tagged from references/risk-taxonomy.md. Not a long inventory. Add at most three to five supporting risks (R2, R3, ...).
  2. Demand level (feature-change mode). Place the request on the hierarchy: L0 founder anxiety or "competitors have it"; L1 one user asked; L2 repeated asks, no behavior proof; L3 workflow blocker; L4 revenue or retention blocker. Build-now is usually justified only at L3 or L4.
  3. Evidence ledger. List the signal that exists and grade each entry on the strength ladder in references/risk-taxonomy.md. Likes, compliments, waitlists, and market-size numbers are NOT demand. Real files, booked calls, payment, repeated manual use, or switching from an existing alternative are.
  4. Verdict (exactly one): Build small / Validate first / Pivot first / Don't build yet. Do not use "Kill".
  5. Validation step. A specific, no-code or low-code next action (talk to the ten users who do X; manually deliver the result for three of them; collect a preorder, paid call, or deposit), never generic advice like "build an MVP" or "do user research".
  6. Routing. Send the user to the skill that does the next piece of work (see below).

Be skeptical but useful. Always separate "can be built" from "should be built". Do not flatter the idea or default to encouragement; do not say "this has potential" unless the path is specific.

Verdict routing

VerdictRoutes to
Build smalldefine-problem-statement, then deliver-prd / deliver-user-stories
Validate firstdefine-hypothesis, then measure-experiment-design
Pivot firstfoundation-lean-canvas (re-frame the model)
Don't build yetstop; or discover-competitive-analysis / discover-market-sizing for an evidence check
Several competing requestsdefine-prioritization-framework

Full map, including the per-risk routing: references/routing-map.md.

If a routed skill is not available, do not ship a bare pointer. The library is often installed in part rather than whole, so the skill you route to may not exist in the user's environment. When you cannot confirm it is available, say so plainly and inline the minimal version of its output so the review stays executable:

Routed skill absentInline instead
define-hypothesisOne testable hypothesis in believe / for / will / as-measured-by form
measure-experiment-designA three-line experiment sketch: the one decision metric, the sample or duration needed, and the win/lose rule set before running
define-problem-statementA two-sentence problem frame: who, in what situation, blocked by what
foundation-lean-canvasThe three riskiest boxes only: problem, customer segment, unfair advantage
define-prioritization-frameworkA single ranked list with the one criterion that actually decides
discover-competitive-analysisThe two closest alternatives and the one axis on which you would lose to each today
discover-market-sizingOne bottom-up estimate: reachable accounts, times a realistic attach rate, times annual revenue per account, labelling each of the three as sourced or assumed. All three factors are required: accounts times attach rate is a customer count, not a market size
deliver-prdThe problem, the one success metric with its baseline, and what is explicitly out of scope
deliver-user-storiesThe three stories that carry the risk, each with one acceptance criterion that could fail

A verdict whose next step the user cannot execute is not a finished review. Naming the gap and supplying the minimum is always better than routing into an environment that cannot follow.

Output Format

A single Build Risk Review artifact, built from references/TEMPLATE.md. Section order: decision header (verdict + one-line rationale), the biggest risk (R1), supporting risks, demand level (feature mode), evidence ledger, validation plan, routing, Sources. A fully worked case is in references/EXAMPLE.md.

Quality Checklist

  • Exactly one primary risk is named (R1) and tagged from the taxonomy.
  • Feature-change mode places the request on L0 through L4.
  • Every evidence entry is graded; no like, waitlist, or market-size number is counted as demand.
  • Exactly one of the four verdicts is returned.
  • The next step is specific and low or no-code, not generic advice.
  • A routing target is named.
  • No code, stack recommendation, or implementation design is produced (the hard gate held).

Attribution

Adapted from bin1874/before-you-build-skill (Apache-2.0), repositioned PM-neutral. The source skill's external case-memory API call and translate-to-user-language behavior are removed.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Foundation Build Risk Review AI skill do?

Runs a fast pre-build risk review on a product idea, feature request, or scope change, naming the single assumption most likely to make it fail and returning a clear verdict (build small, validate first, pivot first, or don't build yet) with a no-code validation step. Use before committing build effort, when triaging whether to honor a feature request, or when deciding whether to expand scope, ahead of writing a PRD. For a launched product's pivot-or-persevere decision, use iterate-pivot-decision instead.

Why use Foundation Build Risk Review on TypingMind?

Because you install it once and use it with any model. Foundation Build Risk Review 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 Foundation Build Risk Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/product-on-purpose/pm-skills/tree/main/skills/foundation-build-risk-review. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Foundation Build Risk Review?

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 Foundation Build Risk Review?

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

Is the Foundation Build Risk Review AI skill free?

Yes. It is published on GitHub by product-on-purpose under the Apache-2.0 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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