Lenny Skills is a collection of product-management and engineering workflows for Claude Code and other AI agents, covering areas such as strategy, research, planning, shipping, growth, and hiring. Each skill gives an agent specialized guidance, frameworks, checklists, or templates for product work, and the catalogue contains many of these skills.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add RefoundAI/lenny-skills --skill building-with-ai-agentsgit clone --depth 1 https://github.com/RefoundAI/lenny-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/refoundai/lenny-skills/building-with-ai-agents)<a href="https://agentmods.dev/skills/refoundai/lenny-skills/building-with-ai-agents"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/building-with-ai-agents/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/refoundai/lenny-skills/building-with-ai-agents"><img src="https://agentmods.dev/badge/skills/refoundai/lenny-skills/building-with-ai-agents.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.01417 |
| Opus 5 | $0.00018 | $0.00709 |
| Sonnet 5 | $0.00007 | $0.00283 |
| Haiku 4.5 | $0.00004 | $0.00142 |
Grade A, and why
building-with-ai-agents scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building With AI Agents
Transition from writing lines of code to directing a parallel team of autonomous agents.
Help the user with building with ai agents using insights from 15 guests and posts across Lenny's Podcast and Newsletter.
How to Help
- Identify Tasks - Use the Junior Intern framework to find repetitive or well-defined engineering tasks suitable for delegation.
- Define Instructions - Draft precise, granular prompts and provide context through markdown files and past examples.
- Manage Parallel Threads - Direct multiple agents simultaneously across different pull requests or features to scale output.
- Review and Iterate - Maintain oversight by reviewing code logic and using AI-led peer reviews to ensure quality before deployment.
Core Principles
The Directorial Shift
Boris Cherny: "100% of my code is written by Claude Code. I have not edited a single line by hand since November. Every day, I ship 10, 20, 30 pull requests. So, at the moment I have, like, five agents running."
Stop manual code editing and transition to directing multiple AI agents simultaneously across different pull requests to maximize productivity.
Absolute Specificity
Lazar Jovanovic: "AI just don't understand what do you mean when you say, 'You know what I mean?' So you need to be specific. I'm optimizing 100% of my time today on good judgment, clarity, quality, taste."
Abandon the assumption that the tool understands your implicit intent and provide granular instructions as if you are talking to a technical co-founder.
High-Level Reasoning and Orchestration
Marc Andreessen: "Over the holiday break, it feels like the AI coding thing really hit critical mass and the world's best programmers, including Linus Torvalds, for the first time over the holiday break basically said, 'Yeah, AI is now coding better than we can.'"
Transform your role from manual execution to reasoning and orchestration, using AI to achieve 10x the output of a standard programmer.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 89 lines · 36 tokens per session scan A 89bc19b30c27
building-with-ai-agents is a skill published in the GitHub repository RefoundAI/lenny-skills (1,321 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,417 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
llama-cpp
Run LLM inference with llama.cpp on CPU, Apple Silicon, AMD/Intel GPUs, or NVIDIA — plus GGUF model conversion and quantization (2–8 bit with K-quants and imatrix). Covers CLI, Python bindings, OpenAI-compatible server, and Ollama/LM Studio integration. Use for edge deployment, M1/M2/M3/M4 Macs, CUDA-less…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior. 4-phase root cause investigation — NO fixes without understanding the problem first.
github-auth
Set up GitHub authentication for the agent using git (universally available) or the gh CLI. Covers HTTPS tokens, SSH keys, credential helpers, and gh auth — with a detection flow to pick the right method automatically.
ideation
Generate project ideas through creative constraints. Use when the user says 'I want to build something', 'give me a project idea', 'I'm bored', 'what should I make', 'inspire me', or any variant of 'I have tools but no direction'. Works for code, art, hardware, writing, tools, and anything that can be made.
notion
Notion API for creating and managing pages, databases, and blocks via curl. Search, create, update, and query Notion workspaces directly from the terminal.
ocr-and-documents
Extract text from PDFs and scanned documents. Use webextract for remote URLs, pymupdf for local text-based PDFs, marker-pdf for OCR/scanned docs. For DOCX use python-docx, for PPTX see the powerpoint skill.