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Agent skill

Product Tool Stack

by RefoundAI in RefoundAI/lenny-skills

Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.

MITAuto-check passed

Install Product Tool Stack

skills CLI
$ npx skills add RefoundAI/lenny-skills --skill product-tool-stack -a claude-code

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

GitHub CLI
$ gh skill install RefoundAI/lenny-skills product-tool-stack --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/product-tool-stack .claude/skills/product-tool-stack && 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
product-tool-stack
GitHub stars
1.4k
Token cost
~1.1k tokens
SKILL.md length
593 words
Files
3 (incl. references)
Skills in repo
76
Repo updated
First seen
Licence
MIT

At a glance

Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.

  • Works in 4 steps: Audit and Consolidate - Review current… → Define the Foundation - Establish the… → Apply Selection Frameworks - Distinguish… → …
  • SKILL.md covers How to Help, Core Principles, Questions to Help Users and Common Mistakes to Flag, plus 2 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Product Tool Stack is an agent skill from RefoundAI/lenny-skills. Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.

Its SKILL.md is about 1.1k 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`).

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.

Example prompts

  • “/product-tool-stack”

Workflow steps

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

  1. Audit and Consolidate - Review current software spending and identifying opportunities to move toward all-in-one platforms that reduce…
  2. Define the Foundation - Establish the core data and communication layers required for early-stage stability and cross-functional alignment.
  3. Apply Selection Frameworks - Distinguish between 'safe bet' industry standards for stability and 'early-adopter' tools for competitive…
  4. Integrate AI-Native Workflows - Identify specific opportunities to automate administrative tasks, ticket drafting, and video editing with…

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

Product Tool Stack loads about 1.1k tokens when it runs, and up to ~15k if it reads all its reference files. Until then it costs about 39 tokens; SKILL.md has 593 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~39
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~15k

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). 593 words, ~1,137 tokens.

Download SKILL.mdSave it as .claude/skills/product-tool-stack/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
product-tool-stack
description
Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.

Product Stack Strategy

Build a high-performance product toolkit by balancing established standards with AI-native speed.

Help the user with product stack strategy using insights from 6 guests and posts across Lenny's Podcast and Newsletter.

How to Help

  1. Audit and Consolidate - Review current software spending and identifying opportunities to move toward all-in-one platforms that reduce workflow friction.
  2. Define the Foundation - Establish the core data and communication layers required for early-stage stability and cross-functional alignment.
  3. Apply Selection Frameworks - Distinguish between 'safe bet' industry standards for stability and 'early-adopter' tools for competitive productivity gains.
  4. Integrate AI-Native Workflows - Identify specific opportunities to automate administrative tasks, ticket drafting, and video editing with agentic tools.

Core Principles

Consolidate for seamless workflows

From "A year free of PostHog ($16,500 value): The all-in-one analytics, experimentation, feature flag, surveys, session replay, error tracking, data warehouse, LLM analytics platform": "Being able to follow an issue from a session recording, to its impact in analytics, to shipping a fix as a feature flag, to testing a variant, to collecting feedback with surveys—that’s the holy grail."

Moving toward all-in-one platforms reduces the technical and operational friction of managing multiple point solutions, enabling better integration between discovery and shipping.

Commit to engineering-backed data

From "Five steps to starting your product-led growth motion, part 2": "Tools such as Amplitude and Mixpanel are commonly used here, but, as the saying goes, “garbage in, garbage out.” Companies need to dedicate engineering resources to instrument tracking properly. Many B2B companies are significantly lacking in product analytics—watching product usage closely is less important when you sell via human touch—but without a strong foundation of product analytics, PLG will never work."

Product analytics requires dedicated engineering resources for proper instrumentation; successful PLG is impossible without a robust data foundation.

Buy instead of build experimentation

From "Five steps to starting your product-led growth motion, part 2": "The most common mistake I see is that companies skip buying and jump right into building. In other words, they bypass the option of using a third-party experimentation tool, often because the engineering and product teams feel like they can build anything. But building an experimentation platform requires not only engineering resources but also data science and statistical expertise."

Small teams should choose third-party experimentation tools over homegrown platforms to avoid massive engineering and statistical overhead.

Show full SKILL.md (207 more words)Show less

Questions to Help Users

  • "Which tools in your current stack are creating the most manual data transfer work between teams?"
  • "Are you currently using a 'safe bet' for your mission-critical data, or are you over-extended on unproven tools?"
  • "What percentage of your PMs' time is spent on administrative tasks like ticket drafting that could be handled by AI agents?"
  • "Is your product instrumentation handled by a dedicated engineering resource or as an afterthought?"
  • "Do you have a single source of truth for user behavioral data that bridges into your CRM?"

Common Mistakes to Flag

  • Building homegrown experimentation platforms - Small teams often underestimate the statistical and engineering maintenance required compared to buying a specialized third-party tool.
  • Over-complicating early-stage process - Using heavy tools like long-term backlogs and story points early on can kill velocity and distract from immediate shipping.
  • Failing to instrument granular data - Without engineering-backed tracking of specific feature interactions, you cannot identify the 'aha moments' that drive growth.
  • Neglecting the data warehouse connection - Failing to sync product usage data with sales and marketing tools prevents the cross-functional intelligence needed for effective scaling.

Deep Dive

For all 25 sourced insights from 6 guests, see references/guest-insights.md

  • Writing Prds
  • Shipping Velocity
  • Ai Assisted Prototyping
  • Building With Ai Agents

© 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/product-tool-stack of RefoundAI/lenny-skills.

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

Open the folder on GitHubat commit 13598cc

Compare with similar skills

Product Tool Stack 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.

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Product Tool Stack this skillRefoundAI/lenny-skills1.4k—~1.1kAutomated safety check: PassMIT
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Database Optimizerdavila7/claude-code-templates32k8 repos~2.5kAutomated safety check: PassMIT
SQL Optimizationgithub/awesome-copilot40k2 repos~2.3kAutomated safety check: PassMIT
Implementcodewhale-hq/Codewhale41k—~190Automated safety check: PassMIT
Agent Performance Optimizerruvnet/ruflo74k2 repos~3.6kAutomated safety check: PassMIT

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Questions about Product Tool Stack

What does Product Tool Stack do?

Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles. Product Tool Stack is an agent skill from RefoundAI/lenny-skills. Help users select, implement, and optimize a modern product tool stack that reduces operational friction and accelerates delivery cycles.

How do I install Product Tool Stack in Claude Code?

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

How do I install Product Tool Stack in Codex?

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

Can I use Product Tool Stack 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 product-tool-stack -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/product-tool-stack, .gemini/skills/product-tool-stack, .github/skills/product-tool-stack and .opencode/skills/product-tool-stack in your project.

What does Product Tool Stack need to run?

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

Does Product Tool Stack 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 Product Tool Stack 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 Product Tool Stack use?

Product Tool Stack 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 Product Tool Stack use?

About 1.1k tokens (SKILL.md is roughly 4.5k 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 13k tokens, read only when the agent opens those files.

What are the alternatives to Product Tool Stack?

Skills that share tags, products or a category with Product Tool Stack: Implement (sickn33/agentic-awesome-skills, 47k stars), Database Optimizer (davila7/claude-code-templates, 32k stars), SQL Optimization (github/awesome-copilot, 40k stars) and Implement (codewhale-hq/Codewhale, 41k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Product Tool Stack?

RefoundAI (a GitHub organization) maintains it in RefoundAI/lenny-skills, which has 1,377 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.