prioritize-assumptions
by phurynPrioritize-assumptions helps teams rank assumptions with an Impact × Risk matrix and recommend experiments for each item. Use prioritize-assumptions for Strategic Planning to turn uncertain ideas into a clear test plan, especially when you need a practical prioritize-assumptions guide, usage flow, and next-step validation.
This skill scores 78/100, which means it is a solid directory candidate: users can likely trigger it reliably and get a useful assumption-prioritization workflow without starting from a generic prompt. The repository provides a clear purpose, matrix-based method, and explicit instruction flow, though it still lacks supporting assets and deeper operational scaffolding that would make adoption even easier.
- Clear triggerability: the frontmatter says when to use it, and the body frames it for triaging assumptions and prioritizing what to test first.
- Concrete workflow: it defines Impact and Risk, then maps assumptions into an Impact × Risk matrix with actionable outcomes.
- Agent leverage: it also suggests targeted experiments for each assumption, which gives an agent a more executable path than a high-level prompt.
- No supporting files or scripts: there are no references, resources, or automation aids, so users must rely on the markdown instructions alone.
- Some framework guidance is partial: it references ICE/RICE and another skill, but the excerpt shows no full formulas, templates, or worked examples in this repository item.
Overview of prioritize-assumptions skill
The prioritize-assumptions skill helps you decide which assumptions deserve attention first by ranking them with an Impact × Risk lens and suggesting experiments for each. It is best for product teams, founders, researchers, and strategic planners who have a long list of uncertainties and need a defensible order of operations, not just a brainstorming list.
Use prioritize-assumptions when you need to turn messy discovery notes into a practical test plan, especially for prioritize-assumptions for Strategic Planning. The core value is speed with structure: it helps you separate assumptions that are worth validating now from ones that can wait, and it pushes the work toward action, not vague prioritization.
What prioritize-assumptions actually does
This skill takes assumptions, estimates their impact and risk, and maps them to a decision category. It also recommends an experiment or test for each assumption so the output is immediately usable in planning or discovery.
Who it fits best
It fits people who already have candidate assumptions, user insights, or product hypotheses and need help sequencing them. It is less useful if you only have a broad problem statement and no concrete assumptions to evaluate yet.
Why this skill is different
Unlike a generic prompt, the prioritize-assumptions skill is opinionated about the decision frame: impact versus risk, with targeted experiments attached. That makes it better for strategic planning than a simple “rank these ideas” request.
How to Use prioritize-assumptions skill
prioritize-assumptions install
Install the skill from the repository path using the documented skills command for your environment, then point it at the pm-product-discovery/skills/prioritize-assumptions folder. If your toolchain supports direct skill installation, verify that the skill name is exactly prioritize-assumptions so the agent can trigger it reliably.
What to provide in your prompt
Give the skill a list of assumptions, not just a goal. Strong inputs look like this:
- “Assumption: users will trust AI-generated summaries if we show source citations.”
- “Assumption: SMB buyers will convert faster if onboarding is under 5 minutes.”
- “Assumption: procurement will block usage unless data retention settings are explicit.”
Add any known context that changes impact or risk, such as audience size, urgency, confidence, known constraints, or business goal. For prioritize-assumptions usage, the more specific the assumption language, the better the prioritization.
Best workflow for the first pass
Start by pasting your assumptions in plain language, then ask the skill to sort them by impact and risk and propose one experiment per item. If your assumptions are vague, rewrite them into testable statements before asking for prioritization. That usually improves the quality of the matrix more than tweaking the scoring wording.
Files to read first
Read SKILL.md first because it contains the operating rules, context, and framework. If the repository grows later, check README.md, AGENTS.md, metadata.json, and any rules/, resources/, references/, or scripts/ folders for organization-specific guidance. For the current repo, SKILL.md is the main source of truth.
prioritize-assumptions skill FAQ
Is this better than a normal prompt?
Usually yes, if you want consistent prioritization instead of ad hoc ranking. A normal prompt can list assumptions, but prioritize-assumptions adds a repeatable structure for impact, risk, and experiment design.
When should I not use it?
Do not use it when you only need a quick opinion, when the assumptions are not yet stated clearly, or when the main problem is framework selection rather than prioritization. In those cases, a discovery prompt or a broader planning skill may be a better first step.
Is it suitable for beginners?
Yes, if the user can provide a simple list of assumptions. Beginners get the most value when they treat the skill as a decision aid and supply concrete statements instead of asking it to invent the assumptions for them.
How does it compare with Strategic Planning work?
For prioritize-assumptions for Strategic Planning, the skill is strongest as a filtering layer: it helps identify which uncertainties should shape the plan first. It does not replace roadmap design, resource allocation, or market analysis; it makes those decisions sharper by narrowing the assumption set.
How to Improve prioritize-assumptions skill
Write stronger assumptions upfront
The best results come from assumptions that are specific, observable, and tied to a real decision. “Users want AI” is too vague; “power users will accept AI suggestions if they can edit them before publishing” is much better for prioritize-assumptions because it gives the skill something concrete to score and test.
Add context that changes impact or risk
Tell the skill who is affected, what the stakes are, and what would happen if the assumption were wrong. Audience size, revenue sensitivity, compliance exposure, and delivery effort all change prioritization more than generic confidence language.
Ask for experiments, not just ranks
A useful prioritize-assumptions guide should end with what to test next. If the first output only orders assumptions, ask for a second pass that converts the top items into low-cost experiments, validation questions, or prototype tests.
Iterate on unclear or overloaded items
Common failure mode: one assumption contains multiple claims. Split “users will adopt it and recommend it and pay for it” into separate statements, then rerun the skill. That gives you cleaner rankings and more actionable experiments.