post-mortems-retrospectives

refoundai/lenny-skills · updated Apr 8, 2026

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$npx skills add https://github.com/refoundai/lenny-skills --skill post-mortems-retrospectives
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summary

Guide teams through learning-focused post-mortems and retrospectives using frameworks from 11 product leaders.

  • Helps frame exercises as blameless learning opportunities rather than blame-focused reviews, with emphasis on psychological safety and honest sharing
  • Covers core principles including pre-mortems with kill criteria, reframing failure as growth, and institutionalizing weekly impact and learnings reviews
  • Guides users through structured questioning to surface systemic insights,
skill.md

Post-mortems & Retrospectives

Help the user run effective post-mortems and retrospectives that drive genuine learning using frameworks from 11 product leaders.

How to Help

When the user asks for help with post-mortems or retrospectives:

  1. Understand the context - Ask whether this is after a failure, a success, or a routine checkpoint
  2. Set the right tone - Help them frame the exercise as learning-focused rather than blame-focused
  3. Structure for insights - Guide them toward formats that surface actionable learnings
  4. Ensure follow-through - Help them create mechanisms to act on what they learn

Core Principles

Pre-mortems need kill criteria

Annie Duke: "A pre-mortem is only effective if it results in 'kill criteria' - pre-determined signals that will trigger a pivot or shutdown." Identify early signals that a project is failing during the pre-mortem and pre-commit to specific actions if those signals are met.

Reframe failure as growth

Carole Robin: "The acronym is A-F-O-G, another F-ing... Another Fucking Opportunity for Growth. My question, when something has gone wrong or a person has experienced a failure, my first question is always, so what did you learn?" When a failure occurs, immediately ask "What is the lesson here?" to maintain perspective during painful setbacks.

Call them retrospectives, not post-mortems

Eeke de Milliano: "Instead of calling something a postmortem, call it a retrospective, so that it's a positive thing. Like, 'Hey, we're learning from this thing.'" Reframing helps normalize failure and focuses the team on learning rather than blame.

Institutionalize learning reviews

Ben Williams: "We have these team level impact and learnings reviews... The teams continuously document any learnings from data exploration, from experimentation, from user research." Hold weekly "Impact and Learnings" reviews focused on insights rather than status updates, and socialize learnings across the entire company.

Grade OKRs for learning, not performance

Christina Wodtke: "What matters is, why 80%? Really focus on the learning... Make sure your grading is secondary to retrospective." The value of grading OKRs lies in the retrospective analysis of why a goal was or wasn't hit, not the number itself. Use end-of-quarter retrospectives to identify systemic blockers.

Make it blameless

The goal is to understand what happened and why, not to assign blame. Create psychological safety so people can share honestly without fear of punishment.

Questions to Help Users

  • "What did we learn that we didn't know before starting this project?"
  • "If we had to do this again with the same information we had at the start, what would we do differently?"
  • "What signals did we see early that we ignored or missed?"
  • "What systemic issues contributed to this outcome that we should address?"
  • "What kill criteria should we set for similar projects in the future?"
  • "How will we ensure these learnings actually influence future decisions?"

Common Mistakes to Flag

  • Blame-focused framing - Turning the exercise into finding fault rather than understanding systems
  • No follow-through - Running retrospectives but never acting on the learnings
  • Only reviewing failures - Missing the opportunity to learn from successes and understand what drove them
  • Optimizing the score, not the learning - Focusing on what percentage of OKRs were achieved rather than why
  • One-time events - Running retrospectives only for big failures instead of making them a regular practice

Deep Dive

For all 13 insights from 11 guests, see references/guest-insights.md

Related Skills

  • running-effective-meetings
  • running-decision-processes
  • planning-under-uncertainty
how to use post-mortems-retrospectives

How to use post-mortems-retrospectives 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 post-mortems-retrospectives
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 post-mortems-retrospectives

The skills CLI fetches post-mortems-retrospectives 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/post-mortems-retrospectives

Reload or restart Cursor to activate post-mortems-retrospectives. Access the skill through slash commands (e.g., /post-mortems-retrospectives) 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.

List & Monetize Your Skill

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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

4.672 reviews
  • Ganesh Mohane· Dec 28, 2024

    I recommend post-mortems-retrospectives for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Camila Mensah· Dec 28, 2024

    post-mortems-retrospectives fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • James Verma· Dec 28, 2024

    post-mortems-retrospectives is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Shikha Mishra· Dec 24, 2024

    post-mortems-retrospectives is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Benjamin Park· Dec 24, 2024

    We added post-mortems-retrospectives from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Nia Garcia· Dec 20, 2024

    I recommend post-mortems-retrospectives for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Benjamin Khan· Dec 12, 2024

    Solid pick for teams standardizing on skills: post-mortems-retrospectives is focused, and the summary matches what you get after install.

  • Kiara Sanchez· Dec 12, 2024

    Keeps context tight: post-mortems-retrospectives is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Camila Okafor· Nov 23, 2024

    I recommend post-mortems-retrospectives for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Chen Tandon· Nov 19, 2024

    post-mortems-retrospectives reduced setup friction for our internal harness; good balance of opinion and flexibility.

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