evaluating-new-technology

refoundai/lenny-skills · updated Apr 8, 2026

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$npx skills add https://github.com/refoundai/lenny-skills --skill evaluating-new-technology
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summary

Framework for evaluating emerging technologies using insights from 22 product leaders.

  • Start by clarifying the problem being solved, then assess technology maturity and stability for your specific use case
  • Adopt a \"build and buy\" mindset: purchase tools for standard 90% functionality, build custom solutions for your unique 10%
  • Prioritize mental bandwidth and core competencies over cost savings; constantly re-test assumptions about what new tools can actually do
  • Design for modula
skill.md

Evaluating New Technology

Help the user evaluate emerging technologies using frameworks from 22 product leaders who have made critical technology decisions at companies from Google to Shopify.

How to Help

When the user asks for help evaluating technology:

  1. Start with the problem - Clarify what problem they're solving before discussing tools
  2. Assess maturity - Determine if the technology is stable enough for their use case
  3. Consider build and buy - Help them find the right mix rather than forcing a binary choice
  4. Plan for change - Design for modularity since the landscape will shift

Core Principles

Tools solve problems, not the reverse

Austin Hay: "I have this adage I always say, which is tools are just meant to solve problems. And the problem set for marketing technologists and business technologists is you focus on the tools." Always define the problem and the people involved before selecting a system or tool.

Build AND buy, not build vs buy

Austin Hay: "Build and buy as opposed to build versus buy. Build and buy means that both of you can win." Buy tools to handle 90% of standard functionality and build the 'cool' 10% that is unique to your business.

Evaluate mental bandwidth, not just dollars

Dhanji R. Prasanna: "The savings and costs that there might be in replacing a vendor tool by something you build in-house is probably not worth it in the mental bandwidth that you've lost." Focus technical bandwidth on core competencies, not recreating vendor tools.

Update your priors constantly

Aparna Chennapragada: "The models couldn't do some things one year ago. My impression of it from trying it a few months ago - that prior needs to be updated. The baby just grew up to be a 15-year-old in a month." Re-test assumptions about what technology can do every few months.

Bet on abstraction layers

Asha Sharma: "You really need to bet on a platform or some app server type layer that allows you to swap things in and out and not really be beholden to any one technology." Invest in modularity as the AI stack evolves.

AI guardrails don't work

Sander Schulhoff: "AI guardrails do not work. If someone is determined enough to trick GPT-5, they're going to deal with that guardrail. When these guardrail providers say 'We catch everything,' that's a complete lie." Be skeptical of AI security vendor claims.

Use the tools yourself

Dhanji R. Prasanna: "I would say really try and use these tools yourself. We learn a lot about how our own workflow can change." Solve a specific, personal problem with new tools to understand their true strengths.

Context drives AI value

Jeanne Grosser: "Because this whole space is so nascent, often your own esoteric context, your content, your workflow is really key to unlocking the power of the agent." For AI agents, building internally often beats buying.

Questions to Help Users

  • "What specific problem are you trying to solve with this technology?"
  • "Is this technology stable enough for production, or still experimental?"
  • "What's the mental bandwidth cost of building vs maintaining a vendor relationship?"
  • "When did you last test your assumptions about what this technology can do?"
  • "How will you swap this out if something better comes along?"
  • "Have you actually used this tool to solve a real problem yourself?"

Common Mistakes to Flag

  • Tool bias - Picking tools because you've used them before, not because they solve the problem
  • Binary build vs buy thinking - Missing the opportunity to buy 90% and build the strategic 10%
  • Outdated priors - Making decisions based on what technology couldn't do six months ago
  • Vendor lock-in - Betting on specific tools without an abstraction layer for future flexibility
  • Trusting security marketing - Believing AI guardrail vendors who claim to 'catch everything'

Deep Dive

For all 27 insights from 22 guests, see references/guest-insights.md

Related Skills

  • AI Product Strategy
  • Building with LLMs
  • Platform Strategy
  • Vibe Coding
how to use evaluating-new-technology

How to use evaluating-new-technology 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 evaluating-new-technology
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 evaluating-new-technology

The skills CLI fetches evaluating-new-technology 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/evaluating-new-technology

Reload or restart Cursor to activate evaluating-new-technology. Access the skill through slash commands (e.g., /evaluating-new-technology) 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

4.760 reviews
  • Ganesh Mohane· Dec 24, 2024

    Solid pick for teams standardizing on skills: evaluating-new-technology is focused, and the summary matches what you get after install.

  • Nikhil Kapoor· Dec 24, 2024

    Registry listing for evaluating-new-technology matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Nikhil Kim· Dec 24, 2024

    Useful defaults in evaluating-new-technology — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Emma Ndlovu· Dec 12, 2024

    evaluating-new-technology has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Kwame Wang· Dec 4, 2024

    evaluating-new-technology reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Emma Reddy· Nov 23, 2024

    I recommend evaluating-new-technology for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Nikhil Jain· Nov 19, 2024

    evaluating-new-technology is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Rahul Santra· Nov 15, 2024

    We added evaluating-new-technology from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Hana Bansal· Nov 15, 2024

    Useful defaults in evaluating-new-technology — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Chinedu Wang· Nov 15, 2024

    Registry listing for evaluating-new-technology matched our evaluation — installs cleanly and behaves as described in the markdown.

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