Identify leverage, moat, and AI-native opportunities in product plans.
npx skills add https://github.com/anasstissir/lenshub --skill plan-altman
Please help me install this Agent Skill. Skill: plan-altman Source: https://github.com/anasstissir/lenshub/tree/main/skills/plan-altman Command: npx skills add https://github.com/anasstissir/lenshub --skill plan-altman
Sam Altman's review framework helps product teams quickly assess where bets on plans can compound in value, whether a feature is AI-native, and how to choose between reframing, accelerating, or executing.
Provide your plan details and run the Altman review to receive a leverage, moat, and iteration plan.
High-intent search queries and answers about installing and using this skill.
You can evaluate product bets for compounding leverage by applying an Altman-style review framework to your feature plans. This process audits leverage potential, identifies AI-native opportunities, and scores your moat to guide prioritization.
A Moat Scorecard rates the defensive barriers of a product plan, specifically evaluating data advantages, network effects, switching costs, and brand barriers. It helps product teams assess whether their feature bets can sustain competitive advantage.
To decide whether to reframe, accelerate, or execute on a roadmap, run an Altman-style review that audits leverage and AI-native potential. The resulting assessments guide your prioritization by highlighting which bets will compound the most value.
Yes, you can use this AI evaluation framework for feature bets, roadmaps, or experiments. It assesses compounding value and AI-native opportunities, outputting an iteration plan with first signals and velocity metrics to learn fast after shipping.
The best way to assess Phase 2 readiness is to map future platform extensions enabling growth beyond the initial launch. An Altman-style review evaluates your iteration plan and leverage audit to determine if your product is ready for expansion.
You should avoid using an Altman-style review for simple, low-leverage tasks lacking AI-native potential or moat-building opportunities. This framework requires structured prompts to evaluate compounding leverage, making it better suited for complex feature bets.
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