Awesome Rebuttal
xiongqi123123/awesome-rebuttal
Global-installable, project-level academic rebuttal strategy skill for AI/ML/CV/NLP/Robotics papers.
Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review.
Install Building With AI Agents
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agents --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/building-with-ai-agents .claude/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.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/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .claude/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agentsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agents --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/building-with-ai-agents .agents/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .agents/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agents --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/building-with-ai-agents .cursor/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .cursor/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/RefoundAI/lenny-skills.git --path skills/building-with-ai-agents--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agents --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/building-with-ai-agents .gemini/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .gemini/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agentsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/building-with-ai-agents .github/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .github/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install RefoundAI/lenny-skills building-with-ai-agents --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/RefoundAI/lenny-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/building-with-ai-agents .opencode/skills/building-with-ai-agents && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "building-with-ai-agents" agent skill from https://github.com/RefoundAI/lenny-skills/tree/main/skills/building-with-ai-agents into .opencode/skills/building-with-ai-agents/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "building-with-ai-agents", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
building-with-ai-agents- GitHub stars
- 1.4k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 1,003 words
- Files
- 3 (incl. references)
- Skills in repo
- 76
- Repo updated
- First seen
- Licence
- MIT
At a glance
Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review.
- Works in 4 steps: Identify Tasks - Use the Junior Intern… → Define Instructions - Draft precise,… → Manage Parallel Threads - Direct… → …
- Development work in your project
- 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
Building With AI Agents is an agent skill from RefoundAI/lenny-skills. Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review.
Its SKILL.md is about 1.7k 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 Development. 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
- Development work in your project
Example prompts
- “/building-with-ai-agents”
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 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.
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
Building With AI Agents loads about 1.7k tokens when it runs, and up to ~17k if it reads all its reference files. Until then it costs about 51 tokens; SKILL.md has 1,003 words of instructions outside code blocks.
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). 1,003 words, ~1,722 tokens.
Download SKILL.mdSave it as
.claude/skills/building-with-ai-agents/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- building-with-ai-agents
- description
- Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review.
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.
Asynchronous Coordination
Scott Wu: "Our whole team is only like 15 engineers a year. We use a ton of Devin when we're building Devin. Most folks on the team are definitely working with up to five Devins at once, and so Devin merges like several hundred pull requests into production in the Devin code bases every month."
Move from synchronous single-tasking to coordinating a parallel team by assigning distinct tasks to multiple agent instances at once.
Eliminate Manual Escape Hatches
Sherwin Wu V2: "There's a team that's actually doing an experiment right now within OpenAI where they are maintaining a 100% Codex-written code base. They run into the exact problems that you're describing. And so usually you're like, 'All right, I'll roll up my sleeves and figure it out.' This team doesn't have that escape hatch."
Resist the urge to manually fix code when agents struggle; instead, commit to mastering the model steering required to solve issues through AI alone.
Multi-Model Peer Review
Zevi Arnovitz: "It's very difficult for me to catch mistakes. What I'll do is basically /review. This tells Claude to start reviewing its own code, but what's even cooler is I have Codex as well as Cursor open. I will have each of them review the code."
Compensate for technical knowledge gaps by forcing different AI models to cross-check each other for logic errors before deployment.
Show full SKILL.md (466 more words)Show less
Templates & Frameworks
- AI Agent Builder Meta-Prompt (Make product management fun again with AI agents) - A comprehensive prompt to paste into an LLM with deep research capabilities (o3 Deep Research or Perplexity Deep Research) that generates platform-specific, ste
- 10 Use Cases for Devin (Autonomous AI Engineer) (A free year of Devin: the world’s most advanced autonomous AI software engineer) - A list of 10 specific ways teams can use Devin, progressing from straightforward engineering tasks to broader product and analytics work.
- Junior Intern Test for Task Delegation (Make product management fun again with AI agents) - A mental model for identifying which tasks to delegate to AI agents: ask yourself what you'd assign to a smart, motivated junior intern with zero experience.
- /peer review command (Zevi Arnovitz) - A prompt that frames Claude as the dev lead receiving code review feedback from other team leads (other AI models), instructing it to either defend its decision
- AI Project Planning PRDs (Markdown Files) (Lazar Jovanovic) - A suite of markdown documents used to provide persistent, dynamic context to AI coding agents so they don't lose track of the project scope.
- 4x4 Debugging Framework (Lazar Jovanovic) - A four-step sequential process for fixing broken AI-generated code without knowing how to code.
- /exploration phase command (Zevi Arnovitz) - A prompt that tells Claude to deeply explore a problem before any code is written — fetches context from Linear, analyzes the codebase, and asks clarifying ques
- /create plan command (Zevi Arnovitz) - A prompt that generates a structured markdown plan file from the exploration exchange, with status trackers on each task, TLDR, critical decisions, and task bre
See references/artifacts.md for the full list with details.
Questions to Help Users
- "Which repetitive engineering tasks are currently slowing your team down the most?"
- "Do you have existing documentation or markdown files that explain your codebase structure to a new joiner?"
- "How comfortable are you resisting the urge to manually fix a bug instead of re-prompting the agent?"
- "What communication tools like Slack or Linear would you like these agents to integrate with?"
- "Do you have a clear definition of success or a template of a perfect pull request for the agent to follow?"
Common Mistakes to Flag
- Vibe Coding - Generating code without maintaining a thorough understanding of the implementation details leads to unmaintainable systems.
- Vague Prompting - Assuming the AI knows what you mean without providing granular, specific technical constraints results in misaligned output.
- Manual Intervention - Reverting to manual coding when the AI goes off-rails prevents you from learning how to steer the model effectively for long-term scale.
- Synchronous Management - Treating agents as chat tools rather than asynchronous team members prevents you from realizing the gains of parallel development.
Deep Dive
For all 31 sourced insights from 15 guests, see references/guest-insights.md
Related Skills
- Writing Prds
- Shipping Velocity
- Ai Assisted Prototyping
- Product Tool Stack
© 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/building-with-ai-agents of RefoundAI/lenny-skills.
- SKILL.md
- references/artifacts.md
- references/guest-insights.md
Open the folder on GitHubat commit 13598cc
Compare with similar skills
Building With AI Agents 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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Building With AI Agents this skillRefoundAI/lenny-skills | 1.4k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Awesome Rebuttalxiongqi123123/awesome-rebuttal | 306 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Jj Flowseandavi/GEOquery | 118 | — | ~981 | Automated safety check: Pass | Custom licence | |
| Light Project StructureLight0305/Light-skills | 640 | — | ~3k | Automated safety check: Notes | MIT | |
| Diagram312362115/claude | 107 | — | ~2.7k | Automated safety check: Pass | MIT | |
| Tiger Releasesafreita1/TIGER | 165 | — | ~1.8k | Automated safety check: Pass | MIT |
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Categories
Questions about Building With AI Agents
What does Building With AI Agents do?
Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review. Building With AI Agents is an agent skill from RefoundAI/lenny-skills. Help users master the transition from manual coding to managing AI-driven development workflows by focusing on high-level direction, parallel tasking, and rigorous automated review.
When should I use Building With AI Agents?
Building With AI Agents fits situations like: development work in your project.
How do I install Building With AI Agents in Claude Code?
Run `npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a claude-code`. Or copy the skill folder (skills/building-with-ai-agents in RefoundAI/lenny-skills) into .claude/skills/building-with-ai-agents in your project. Claude Code loads it when a task matches its description.
How do I install Building With AI Agents in Codex?
Run `npx skills add RefoundAI/lenny-skills --skill building-with-ai-agents -a codex`. Or copy the skill folder (skills/building-with-ai-agents in RefoundAI/lenny-skills) into .agents/skills/building-with-ai-agents in your project. Codex loads it when a task matches its description.
Can I use Building With AI Agents 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 building-with-ai-agents -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/building-with-ai-agents, .gemini/skills/building-with-ai-agents, .github/skills/building-with-ai-agents and .opencode/skills/building-with-ai-agents in your project.
What does Building With AI Agents need to run?
SKILL.md names no scripts, command-line tools or credentials: Building With AI Agents is instructions for the agent only.
Does Building With AI Agents 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 Building With AI Agents 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 Building With AI Agents use?
Building With AI Agents 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 Building With AI Agents use?
About 1.7k tokens (SKILL.md is roughly 6.9k 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 15k tokens, read only when the agent opens those files.
What are the alternatives to Building With AI Agents?
Skills that share tags, products or a category with Building With AI Agents: Awesome Rebuttal (xiongqi123123/awesome-rebuttal, 306 stars), Jj Flow (seandavi/GEOquery, 118 stars), Light Project Structure (Light0305/Light-skills, 640 stars) and Diagram (312362115/claude, 107 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Building With AI Agents?
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.