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Agent skill

AI Product Strategy

by RefoundAI in RefoundAI/lenny-skills

Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

MITAuto-check passedProduct & Project Management

Install AI Product Strategy

skills CLI
$ npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install RefoundAI/lenny-skills ai-product-strategy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ai-product-strategy .claude/skills/ai-product-strategy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ai-product-strategy
GitHub stars
1.4k
Token cost
~1.9k tokens
SKILL.md length
1,051 words
Files
3 (incl. references)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

  • Works in 4 steps: Define the wedge - Identify… → Select the architecture - Choose between… → Scale agency safely - Design a graduated… → …
  • Tasks that involve Product strategy
  • SKILL.md covers How to Help, Core Principles, Templates & Frameworks and Questions to Help Users, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

AI Product Strategy is an agent skill from RefoundAI/lenny-skills. Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/artifacts.md` and `references/guest-insights.md`).

It sits in Product & Project Management, covering Product strategy. The repository describes itself as: 86 product management skills from Lenny's Podcast for Claude Code and AI agents. Hiring, user research, strategy, shipping, and more. The licence is MIT.

When your agent uses it

  • Tasks that involve Product strategy

Example prompts

  • “/ai-product-strategy”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Define the wedge - Identify high-friction chores where AI can provide a disproportionate payoff for the user.
  2. Select the architecture - Choose between retrieval-augmented generation (RAG) and fine-tuning based on the need for live data vs. specific…
  3. Scale agency safely - Design a graduated approach to autonomy that keeps humans in the loop before moving to full automation.
  4. Build for the curve - Align product roadmaps with future model capabilities rather than building complex scaffolding for today's…

What it can do on your machine

Read from SKILL.md and the folder at commit 13598cc. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

AI Product Strategy loads about 1.9k tokens when it runs, and up to ~32k if it reads all its reference files. Until then it costs about 54 tokens; SKILL.md has 1,051 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~54
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~32k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from RefoundAI/lenny-skills at commit 13598cc, republished under its MIT licence (© RefoundAI). 1,051 words, ~1,871 tokens.

Download SKILL.mdSave it as .claude/skills/ai-product-strategy/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
ai-product-strategy
description
Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

AI Product Strategy

Prioritize high-impact workflows and navigate non-deterministic development to build defensible AI products.

Help the user with ai product strategy using insights from 26 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Define the wedge - Identify high-friction chores where AI can provide a disproportionate payoff for the user.
  2. Select the architecture - Choose between retrieval-augmented generation (RAG) and fine-tuning based on the need for live data vs. specific behavior.
  3. Scale agency safely - Design a graduated approach to autonomy that keeps humans in the loop before moving to full automation.
  4. Build for the curve - Align product roadmaps with future model capabilities rather than building complex scaffolding for today's limitations.

Core Principles

Account for squishy outputs

Alex Komoroske: "LLMs allow writing shitty software to be significantly cheaper, not necessarily good software, but good enough in certain contexts. And also it means that there's certain software now that isn't plain old computing that can be run cheaply. It's relatively expensive marginal cost."

Design product experiences that assume AI is non-deterministic and imperfect rather than trying to force 100% accuracy into your UI.

Treat products as living organisms

Asha Sharma: "Because these models are so effective at this point, you want to start to tune them to certain types of outcomes. All of a sudden, these are these living organisms that just get better with the more interactions that happen. I think this is the new IP of every single company products that think and live and learn."

Measure success by the team's metabolism in ingesting data and improving learning loops rather than static feature releases.

Find defensibility in verticalization

Logan Kilpatrick: "We're not going to launch some of these varied verticalized products. We're not going to launch an AI sales agent. That's just not what we're building towards. And companies who are and have some domain specific knowledge and they're really excited about that problem space, they can go into that and leverage our models and end up continuing to be on the cutting edge without having to do all that R&D effort themselves."

Avoid competing with foundational models by targeting specific industry niches where domain expertise provides a structural advantage.

Incubate specific superpowers

Noah Weiss: "I think in the AI space, we're trying to hear from customers, what do you wish Slack could do if it had these new superpowers? Let's incubate a couple teams or prototype, give them space to run and pilot and then get something to launch that's amazing. Blows people away. That's the formula that we've seen."

Avoid generic AI features by identifying specific customer needs and giving dedicated teams space to prototype them independently.

Build for the model's future

Sherwin Wu V2: "The field and the models themselves are just changing so, so quickly. They tend to disrupt themselves. The models will eat your scaffolding for breakfast."

Design for the capabilities expected in 12 to 18 months to avoid building custom scaffolding that will eventually be absorbed natively by the models.

Adopt a graduated approach to autonomy

Aishwarya Naresh Reganti + Kiriti Badam: "You need to be deliberately starting in places where there is minimal impact and more human control so that you have a good grip of what are the current capabilities and what can I do with them and then slowly lean into the more agency and lesser control."

Safely deploy agentic systems by starting with human-in-the-loop suggestions before scaling to full autonomous interactions.

Show full SKILL.md (475 more words)Show less

Templates & Frameworks

  • AI Glossary - 20+ Key Terms (An AI glossary) - A comprehensive reference list of AI terms with 'explain it like I'm 5' definitions, designed to be kept handy for meetings
  • AI Product Builder's 12 Principles (Counterintuitive advice for building AI products) - A set of 12 counterintuitive principles for building AI products, compiled from 20+ AI product leaders across companies like GitHub, Canva, Superhuman, Perplexi
  • CC/CD (Continuous Calibration/Continuous Development) Framework (Why your AI product needs a different development lifecycle) - A six-step development lifecycle framework for AI products that accounts for non-determinism and the agency-control tradeoff. Replaces traditional CI/CD thinkin
  • AI Integration Decision Framework (Summary: AI and product management | Marily Nika (Meta, Google)) - A decision-making approach for when and how to add AI to your product
  • The Bitter Lesson Applied to AI Product Building (Sherwin Wu V2) - Extension of Rich Sutton's Bitter Lesson to building products with AI — scaffolding and workarounds get eaten by model improvements
  • AI Startup Defensibility Framework (Peter Deng) - Three pillars for building defensible AI startups: proprietary data flywheels, crafted workflows, and product craft that overcomes incumbent distribution.
  • AI Product Mindset Shift: Prototype-First vs. Design-First (Counterintuitive advice for building AI products) - A framework contrasting the traditional software development approach with the AI-native approach where feasibility is uncertain
  • AI Product Differentiation Stack: Data > Interface > Model (Counterintuitive advice for building AI products) - A hierarchy for where lasting competitive advantage lies in AI products
  • Stickiness Over Moats in AI (Scott Wu) - In AI products, defensibility comes from compounding stickiness (accumulated knowledge, team workflows, learning) rather than hard barriers to entry

See references/artifacts.md for the full list with details.

Questions to Help Users

  • "What is the high-friction chore in your product where automation offers the biggest payoff?"
  • "Does your use case require access to live, internal data or a specific, consistent behavior style?"
  • "How are you designing the interface to handle non-deterministic or incorrect AI outputs?"
  • "Are you building a feature that a foundational model update will likely render obsolete in a year?"
  • "What proprietary data do you have that competitors cannot easily access or replicate?"
  • "How will you measure the metabolism of your product's learning loops over time?"

Common Mistakes to Flag

  • Building for current model limitations - Creating complex scaffolding to solve today's model weaknesses is a losing strategy as foundational models will soon absorb that functionality.
  • Adding AI for its own sake - Generic AI features without a validated, data-backed user problem fail to provide meaningful value or defensibility.
  • Assuming 100% accuracy - Failing to account for the probabilistic nature of AI leads to broken user experiences when the model inevitably hallucinates.
  • Prioritizing prompt engineering over data - The effectiveness of AI features is often constrained more by the quality and timeliness of underlying data than the prompt itself.

Deep Dive

For all 45 sourced insights from 26 guests, see references/guest-insights.md

  • Ai Evals
  • Ai Native Ux

© RefoundAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/ai-product-strategy of RefoundAI/lenny-skills.

  • SKILL.md
  • references/artifacts.md
  • references/guest-insights.md

Open the folder on GitHubat commit 13598cc

Compare with similar skills

AI Product Strategy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

AI Product Strategy compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
AI Product Strategy this skillRefoundAI/lenny-skills1.4k—~1.9kAutomated safety check: PassMIT
Game Changing FeaturesopenstatusHQ/data-table-filters2.3k3 repos~2.1kAutomated safety check: PassMIT
Organic Growth Path Advisordeanpeters/Product-Manager-Skills7.2k2 repos~5.2kAutomated safety check: PassCustom licence
Product Strategistalirezarezvani/claude-skills28k2 repos~1.8kAutomated safety check: PassMIT
PlaidBuildGreatProducts/plaid218—~1.6kAutomated safety check: PassMIT
Product Competitive AnalysisFokkyp/claude-skills226—~1.2kAutomated safety check: PassNone

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Categories

Questions about AI Product Strategy

What does AI Product Strategy do?

Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data. AI Product Strategy is an agent skill from RefoundAI/lenny-skills. Help users decide where to apply AI effectively, manage the transition from deterministic to probabilistic software, and build long-term defensibility through verticalization and proprietary data.

When should I use AI Product Strategy?

AI Product Strategy fits situations like: tasks that involve Product strategy.

How do I install AI Product Strategy in Claude Code?

Run `npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a claude-code`. Or copy the skill folder (skills/ai-product-strategy in RefoundAI/lenny-skills) into .claude/skills/ai-product-strategy in your project. Claude Code loads it when a task matches its description.

How do I install AI Product Strategy in Codex?

Run `npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a codex`. Or copy the skill folder (skills/ai-product-strategy in RefoundAI/lenny-skills) into .agents/skills/ai-product-strategy in your project. Codex loads it when a task matches its description.

Can I use AI Product Strategy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add RefoundAI/lenny-skills --skill ai-product-strategy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-product-strategy, .gemini/skills/ai-product-strategy, .github/skills/ai-product-strategy and .opencode/skills/ai-product-strategy in your project.

What does AI Product Strategy need to run?

SKILL.md names no scripts, command-line tools or credentials: AI Product Strategy is instructions for the agent only.

Does AI Product Strategy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is AI Product Strategy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does AI Product Strategy use?

AI Product Strategy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does AI Product Strategy use?

About 1.9k tokens (SKILL.md is roughly 7.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 30k tokens, read only when the agent opens those files.

What are the alternatives to AI Product Strategy?

Skills that share tags, products or a category with AI Product Strategy: Game Changing Features (openstatusHQ/data-table-filters, 2.3k stars), Organic Growth Path Advisor (deanpeters/Product-Manager-Skills, 7.2k stars), Product Strategist (alirezarezvani/claude-skills, 28k stars) and Plaid (BuildGreatProducts/plaid, 218 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains AI Product Strategy?

RefoundAI (a GitHub organization) maintains it in RefoundAI/lenny-skills, which has 1,382 GitHub stars. The repository holds 76 skills in this directory. The repository was last updated on July 16, 2026.

Source: RefoundAI/lenny-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.