running-decision-processes

refoundai/lenny-skills · updated Apr 8, 2026

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$npx skills add https://github.com/refoundai/lenny-skills --skill running-decision-processes
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summary

Structured decision-making frameworks and processes drawn from 65 product leaders.

  • Guides users through decision classification (reversible vs. irreversible), blocker identification, and framework selection to move from deliberation to action
  • Covers core principles including making implicit assumptions explicit, using curiosity loops for advice gathering, and distinguishing when data-driven analysis ends and conviction takes over
  • Flags common pitfalls: analysis paralysis, decision by
skill.md

Running Decision Processes

Help the user run effective decision-making processes using frameworks from 65 product leaders.

How to Help

When the user asks for help with decision processes:

  1. Understand the decision type - Ask if this is reversible or irreversible, high-stakes or routine
  2. Identify the blockers - Determine what's preventing the decision from being made
  3. Structure the process - Recommend an appropriate framework for the decision at hand
  4. Enable commitment - Help them move from deliberation to action

Core Principles

Hesitation is destructive

Ben Horowitz: "The worst thing that you do as a leader is you hesitate on the next decision. The thing that causes you to hesitate is both decisions are horrible." Failing to make an explicit decision causes organizational anxiety. Recognize when you're avoiding a decision because all options are bad.

Make implicit explicit

Annie Duke: "It's so incredibly necessary in improving decision quality to take what's implicit and make it explicit. It's not that intuition is crap... If you don't make it explicit, then you don't get to find out when it's wrong." Document the assumptions behind gut feelings so you can review them later and learn when intuition is right or wrong.

Use curiosity loops for advice

Ada Chen Rekhi: "A curiosity loop is essentially going to a whole bunch of people... asking them, 'Hey, here are nine topics... What are two or three of the topics that resonate with you and why?'" Gather contextual advice by asking specific questions that solicit rationale, not biased yes/no answers.

Act as a historian

Anneka Gupta: "I try to construct this past knowledge of what had happened and what were the decisions that were made and why were those decisions made, whether they were good or bad." Research past failed projects to understand the context of previous decisions and navigate current resistance.

High-conviction decisions require leaps of faith

Brandon Chu: "Know how to make really, really hard high conviction decisions that actually can't be solved. You got to take a leap of faith and how to do that and bring teams through that type of ambiguity." Some decisions cannot be solved with data - take the leap and maintain high accountability for the choice.

Distinguish decision types

Jeff Bezos: "Type 1 decisions are consequential and irreversible... Type 2 decisions are changeable, reversible." Spend more time on one-way doors. Move fast on reversible decisions.

Disagree and commit

Once a decision is made, the team must commit fully even if individuals disagreed during deliberation. Without commitment, decisions get relitigated endlessly.

Assign a clear decision-maker

Every decision needs a single accountable owner. Frameworks like DACI (Driver, Approver, Contributor, Informed) clarify who makes the call.

Questions to Help Users

  • "Is this a one-way door or a two-way door? How hard would it be to reverse this decision?"
  • "What's the cost of waiting another week to decide? What's the cost of being wrong?"
  • "Who is the single decision-maker here? Does everyone know who that is?"
  • "What information would change your mind? Can you get that information quickly?"
  • "What happened last time the team faced a similar decision?"
  • "If you had to decide right now with the information you have, what would you choose?"

Common Mistakes to Flag

  • Analysis paralysis - Gathering more data when enough information already exists to decide
  • Decision by committee - No clear owner leading to diffused accountability
  • Treating all decisions equally - Applying the same rigor to reversible and irreversible decisions
  • Relitigating decisions - Reopening settled decisions without new information
  • Implicit assumptions - Making gut decisions without documenting the reasoning for later learning

Deep Dive

For all 82 insights from 65 guests, see references/guest-insights.md

Related Skills

  • running-effective-meetings
  • planning-under-uncertainty
  • prioritizing-roadmap
how to use running-decision-processes

How to use running-decision-processes on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add running-decision-processes
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/refoundai/lenny-skills --skill running-decision-processes

The skills CLI fetches running-decision-processes from GitHub repository refoundai/lenny-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/running-decision-processes

Reload or restart Cursor to activate running-decision-processes. Access the skill through slash commands (e.g., /running-decision-processes) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.574 reviews
  • Fatima Nasser· Dec 28, 2024

    Useful defaults in running-decision-processes — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Naina Gonzalez· Dec 24, 2024

    running-decision-processes has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Evelyn Khan· Dec 24, 2024

    Registry listing for running-decision-processes matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Anika Anderson· Dec 20, 2024

    We added running-decision-processes from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Emma Park· Dec 12, 2024

    Keeps context tight: running-decision-processes is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • James Perez· Dec 8, 2024

    running-decision-processes is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • James Ndlovu· Dec 8, 2024

    Keeps context tight: running-decision-processes is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Neel Menon· Nov 27, 2024

    running-decision-processes reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Soo Sethi· Nov 27, 2024

    Registry listing for running-decision-processes matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kwame Haddad· Nov 19, 2024

    I recommend running-decision-processes for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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