platform-infrastructure▌
refoundai/lenny-skills · updated Apr 8, 2026
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Design and scale internal platforms by abstracting common capabilities and planning infrastructure before hitting limits.
- ›Focuses on understanding platform purpose, assessing organizational readiness, identifying leverage points, and designing for actual developer adoption rather than theoretical needs
- ›Emphasizes invisible infrastructure qualities like reliability, performance, and privacy as critical success factors alongside visible features
- ›Recommends proactive infrastructure scal
Platform Infrastructure
Help the user design and scale internal platforms and shared technical infrastructure using insights from 5 product and engineering leaders.
How to Help
When the user asks for help with platform infrastructure:
- Understand the platform's purpose - Ask whether they're building for internal developers, external partners, or both
- Assess organizational readiness - Determine if they have the adoption and governance structures to support a platform
- Identify the leverage points - Help them find where platform investment creates the most value multiplication
- Design for adoption - Ensure the platform solves real developer problems, not theoretical ones
Core Principles
Abstract common capabilities into shared infrastructure
Daniel Lereya: "We actually stopped for the first time and say, 'What is the column like?' And we also organized all the product architecture around it... making the work of adding a new column just thinking about the specific." Scaling feature velocity requires abstracting repetitive components into a shared infrastructure so developers only focus on unique logic.
Invisible infrastructure often matters most
Asha Sharma: "It wasn't the hundreds of features, it was all in the infrastructure and the platform... performance, reliability, privacy, safety, all of those things." The success of major platforms often depends on "invisible" qualities like reliability and speed rather than visible features.
Plan for scale before you need it
Ivan Zhao: "During COVID, we just couldn't scale up our infrastructure. For the longest time, Simon's really good at don't do premature optimization... we're running off even the largest instance there is for Postgres." While avoiding premature optimization is good, infrastructure must be planned far enough ahead to avoid "doomsday" scenarios when usage spikes.
Build discoverability into the architecture
Eli Schwartz: "If you create a categorized sitemap where you can say, 'These are all the questions on health and from the sitemap... then a search engine can navigate through the entire site, and all of the questions and answers are discoverable.'" For large-scale platforms, structural decisions like HTML sitemaps and internal linking are critical for search engine discoverability.
Default to server-side tracking
Vijay: "The biggest mistake is setting up analytics using client side SDKs... start tracking events from your servers instead of from your clients." Server-side tracking is superior to client-side SDKs for data reliability, cross-platform consistency, and developer maintenance.
Questions to Help Users
- "Who are the 'users' of this platform and what problems are they trying to solve today?"
- "What's the current developer experience pain point that's costing the most productivity?"
- "How will you measure whether this platform is actually being adopted?"
- "Is this a build vs buy decision, or should this remain a manual process for now?"
- "What's your 'doomsday clock' - when will current infrastructure hit its limits?"
Common Mistakes to Flag
- Building for the abstract future - Creating capabilities based on anticipated needs rather than current developer pain
- Platform without product ownership - Treating infrastructure as a technical project without dedicated product management
- Avoiding premature optimization until it's too late - Not monitoring infrastructure limits to trigger scaling projects before failure
- Client-side tracking by default - Using browser SDKs instead of server-side event tracking
- Ignoring the migration cost - Building new platforms without accounting for the effort to move teams off existing solutions
Deep Dive
For all 6 insights from 5 guests, see references/guest-insights.md
Related Skills
- platform-strategy
- product-operations
- scoping-cutting
How to use platform-infrastructure on Cursor
AI-first code editor with Composer
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 platform-infrastructure
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches platform-infrastructure from GitHub repository refoundai/lenny-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate platform-infrastructure. Access the skill through slash commands (e.g., /platform-infrastructure) 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
Submit your Claude Code skill and start earning
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★45 reviews- ★★★★★Kiara Li· Dec 28, 2024
Solid pick for teams standardizing on skills: platform-infrastructure is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Dec 16, 2024
Keeps context tight: platform-infrastructure is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Olivia Huang· Dec 16, 2024
Registry listing for platform-infrastructure matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Pratham Ware· Dec 4, 2024
Useful defaults in platform-infrastructure — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Lucas Thomas· Dec 4, 2024
Useful defaults in platform-infrastructure — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Lucas Gonzalez· Nov 19, 2024
platform-infrastructure is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Oshnikdeep· Nov 7, 2024
platform-infrastructure has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kabir Verma· Nov 7, 2024
platform-infrastructure fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Ganesh Mohane· Oct 26, 2024
Solid pick for teams standardizing on skills: platform-infrastructure is focused, and the summary matches what you get after install.
- ★★★★★Olivia Zhang· Oct 26, 2024
We added platform-infrastructure from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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