Strategic AI product decision-making guided by frameworks from 94 product leaders and practitioners.
Works with
Helps distinguish genuine user problems from \"AI for AI's sake\" by starting with problem definition, not technology
Guides critical architecture decisions including build vs buy, model selection, human-AI boundaries, and multi-model systems
Emphasizes designing for AI failure modes, non-determinism, and rapid iteration through feedback loops and evals
Flags common mistakes like s
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionai-product-strategyExecute the skills CLI command in your project's root directory to begin installation:
Fetches ai-product-strategy from refoundai/lenny-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate ai-product-strategy. Access via /ai-product-strategy in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners.
When the user asks for help with AI product strategy:
Aishwarya Naresh Reganti: "In all the advancements of AI, one slippery slope is to keep thinking about solution complexity and forget the problem you're trying to solve. Start with minimal impact use cases to gain a grip on current capabilities."
Adriel Frederick: "When working on algorithmic products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions." This boundary is the core PM decision.
Alex Komoroske: "LLMs are magical duct tape—distilled intuition of society. They make writing 'good enough' software significantly cheaper but increase marginal inference costs." Understand the new cost structure.
Asha Sharma: "You have to build for the slope instead of the snapshot of where you are." AI capabilities change fast—build flexible architectures that can swap models as they improve.
Alex Komoroske: "Even at 99% accuracy, if it punches the user in the face 1% of the time, that's not a viable product. Design assuming the AI will be squishy and not fully accurate."
Aishwarya Naresh Reganti: "It's not about being first to have an agent. It's about building the right flywheels to improve over time." Log human actions to create data loops for system improvement.
Amjad Masad: "Future products will be made of many different models—it's quite a heavy engineering project." Use specialized models for different tasks (reasoning vs speed vs coding).
Albert Cheng: "We run chess engines for evaluations. LLMs translate that into natural language. Use the right technology for the right task." Don't use LLMs where deterministic algorithms excel.
Alexander Embiricos: "The current limiting factor is human typing speed and multitasking on prompts. Build systems that are 'default useful' without constant prompting."
Aishwarya Naresh Reganti: "Most people ignore the non-determinism. You don't know how users will behave with natural language, and you don't know how the LLM will respond." Build for variability.
Aparna Chennapragada: "Effective agents have (1) increasing autonomy to handle higher-order tasks, (2) ability to handle complex multi-step workflows, and (3) natural, often asynchronous interaction."
Aishwarya Naresh Reganti: "Leaders have to get hands-on—not implementing, but rebuilding intuitions. Be comfortable that your intuitions might not be right." Block time daily to stay current.
For all 179 insights from 94 guests, see references/guest-insights.md
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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Solid pick for teams standardizing on skills: ai-product-strategy is focused, and the summary matches what you get after install.
ai-product-strategy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
ai-product-strategy is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in ai-product-strategy — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: ai-product-strategy is the kind of skill you can hand to a new teammate without a long onboarding doc.
ai-product-strategy has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for ai-product-strategy matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend ai-product-strategy for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
ai-product-strategy reduced setup friction for our internal harness; good balance of opinion and flexibility.
ai-product-strategy reduced setup friction for our internal harness; good balance of opinion and flexibility.
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