Planning Under Uncertainty logo

Planning Under Uncertainty

Community
cnfeat
planning-under-uncertainty

Help users plan products and strategy when outcomes are unpredictable. Use when someone is dealing with ambiguous timelines, building in fast-moving markets, planning AI/ML projects, or asking how to make commitments when they don't know what will happen.

Overview

Publishercnfeat
Repositorytop-pm-skills
Skill nameplanning-under-uncertainty
Stars
55
Forks
6
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Planning Under Uncertainty 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/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/planning-under-uncertainty
  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.

Use it in TypingMind

Enable Planning Under Uncertainty 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 Planning Under Uncertainty 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 Planning Under Uncertainty 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.

Planning Under Uncertainty

Help the user navigate product planning when the future is unclear using adaptive planning frameworks from 44 product leaders.

How to Help

When the user asks for help with planning under uncertainty:

  1. Understand the uncertainty type - Ask what's driving the ambiguity: technical unknowns, market volatility, AI/ML unpredictability, or organizational change
  2. Assess planning horizon - Determine if they need short-term execution tactics or long-term strategic flexibility
  3. Match framework to context - Recommend appropriate planning approaches based on their uncertainty profile
  4. Build in adaptation mechanisms - Help them create checkpoints and decision criteria for pivoting

Core Principles

Embrace optionality over prediction

Amjad Masad: "Being agile, not being stuck with roadmaps, being able to just say, oh, we're just going to switch priorities right away, is going to be super important." In rapidly changing environments like AI, maintain flexibility to pivot when new capabilities emerge rather than committing to rigid long-term plans.

Build buffers for chaos

Upasna Gautam: "Any time we're planning we build in buffers for all of that chaos that's happening on a daily basis." In chaotic environments, planning must include explicit time buffers and contingency plans ranging from days to months depending on scope.

Use data as compass, not GPS

Shaun Clowes: "Data is more like a compass than a GPS. If you look at data as a way of giving you the answer, you're always wrong." Use data to validate or invalidate intuition rather than waiting for it to tell you exactly what to do.

Value learning over winning

Ramesh Johari: "Experimentation was never historically in science about winners and losers... Experimentation is always very hypothesis driven. It's about, what are you learning?" A healthy experimentation culture values learning from "failed" risky bets more than safe, incremental "wins."

Develop reproducible testing processes

Nikita Bier: "Develop a reproducible testing process, and that will actually influence the probability of your success more than anything." Success in uncertain markets is driven by the quality and speed of the testing process rather than the initial idea.

Diagnose before acting in crisis

Alex Hardimen: "There's this incredible humility that was needed to really understand and first diagnose what was actually happening on the platform." Managing through a crisis requires "wartime" humility to accurately diagnose problems before attempting solutions.

Create decision triggers, not fixed plans

Eric Ries: "Give yourself a fixed period of time to take some decisive action and see if it feels better." Build checkpoints into plans where you'll reassess based on what you've learned, not just calendar dates.

Distinguish reversible from irreversible decisions

Claire Hughes Johnson: "Type one, type two decisions. Is it high impact? Is it irreversible? Is it not?" Spend more time on one-way doors and move quickly on reversible decisions that can be adjusted later.

Questions to Help Users

  • "What would need to be true for your current plan to work? Which of those assumptions are you least confident about?"
  • "If this takes twice as long as expected, what would you do differently? What if it takes half as long?"
  • "What's the smallest thing you could ship to learn whether your core hypothesis is correct?"
  • "Is this a one-way door or a two-way door decision?"
  • "What signals would tell you to pivot or kill this initiative?"
  • "How much buffer have you built in for unexpected chaos?"

Common Mistakes to Flag

  • Over-planning - Creating detailed long-term roadmaps that create false confidence and resist necessary pivots
  • Analysis paralysis - Waiting for perfect information instead of making decisions with 70% confidence
  • Ignoring leading indicators - Not tracking intermediate signals that could tell you earlier if you're on track
  • Judging experiments by outcomes alone - Not valuing the learning from "failed" experiments that tested important hypotheses
  • Planning theater - Spending excessive time on documents and processes that don't reduce actual uncertainty

Deep Dive

For all 52 insights from 44 guests, see references/guest-insights.md

Related Skills

  • prioritizing-roadmap
  • running-decision-processes
  • scoping-cutting
  • problem-definition

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 Planning Under Uncertainty AI skill do?

Help users plan products and strategy when outcomes are unpredictable. Use when someone is dealing with ambiguous timelines, building in fast-moving markets, planning AI/ML projects, or asking how to make commitments when they don't know what will happen.

Why use Planning Under Uncertainty on TypingMind?

Because you install it once and use it with any model. Planning Under Uncertainty 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 Planning Under Uncertainty in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cnfeat/top-pm-skills/tree/main/参考skill/lenny-skills-main%20(2)/lenny-skills-main/skills/planning-under-uncertainty. 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 Planning Under Uncertainty?

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 Planning Under Uncertainty?

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

Is the Planning Under Uncertainty AI skill free?

Yes. It is published on GitHub by cnfeat 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇