Langgraph Agent Patterns
soba-labs/langchain-agent-skills
Implement multi-agent coordination patterns (supervisor-subagent, router, orchestrator-worker, handoffs) for LangGraph applications.
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
Install Agent Builder
$ npx skills add shareAI-lab/learn-claude-code --skill agent-builder -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install shareAI-lab/learn-claude-code agent-builder --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/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/agent-builder .claude/skills/agent-builder && 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 "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .claude/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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/shareAI-lab/learn-claude-code/tree/main/skills/agent-builderType 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 shareAI-lab/learn-claude-code --skill agent-builder -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install shareAI-lab/learn-claude-code agent-builder --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/agent-builder .agents/skills/agent-builder && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .agents/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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 shareAI-lab/learn-claude-code --skill agent-builder -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install shareAI-lab/learn-claude-code agent-builder --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/agent-builder .cursor/skills/agent-builder && 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 "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .cursor/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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/shareAI-lab/learn-claude-code.git --path skills/agent-builder--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 shareAI-lab/learn-claude-code --skill agent-builder -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install shareAI-lab/learn-claude-code agent-builder --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/agent-builder .gemini/skills/agent-builder && 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 "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .gemini/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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 shareAI-lab/learn-claude-code agent-builderInstalls 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 shareAI-lab/learn-claude-code --skill agent-builder -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/agent-builder .github/skills/agent-builder && 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 "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .github/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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 shareAI-lab/learn-claude-code --skill agent-builder -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install shareAI-lab/learn-claude-code agent-builder --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/agent-builder .opencode/skills/agent-builder && 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 "agent-builder" agent skill from https://github.com/shareAI-lab/learn-claude-code/tree/main/skills/agent-builder into .opencode/skills/agent-builder/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agent-builder", 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
agent-builder- GitHub stars
- 78k
- Used in
- 6 other repos
- Token cost
- ~1.2k tokens
- SKILL.md length
- 505 words
- Files
- 6 (incl. scripts, references)
- Skills in repo
- 4
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code.
- Works in 3 steps: Capabilities (What can it DO?) → Knowledge (What does it KNOW?) → Context (What has happened?)
- Ask to create an agent
- SKILL.md covers The Core Philosophy, The Three Elements, Agent Design Thinking and Progressive Complexity, plus 5 more sections
- Runs Python scripts from its folder
What it does
Agent Builder is an agent skill from shareAI-lab/learn-claude-code. Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/agent-philosophy.md`, `references/minimal-agent.py` and `references/subagent-pattern.py`).
It sits in AI & LLM Engineering, covering Building AI agents and Subagents. The repository describes itself as: Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1. The licence is MIT.
When your agent uses it
- Ask to create an agent
- Build an assistant
- Design an AI system
- Want to understand agent architecture
Example prompts
- “create an agent”
- “build an assistant”
- “design an AI system”
- “/agent-builder”
Requirements
- Python 3
Workflow steps
3 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit ce8f9f1. 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
Ships 1 file in scripts/ (Python), which the agent can run.
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
Agent Builder loads about 1.2k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 132 tokens; SKILL.md has 505 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); the scripts in this folder are not scanned.
SKILL.md
The full file from shareAI-lab/learn-claude-code at commit ce8f9f1, republished under its MIT licence (© shareAI-lab). 505 words, ~1,179 tokens.
Download SKILL.mdSave it as
.claude/skills/agent-builder/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.- name
- agent-builder
- description
- Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about Claude Code, Cursor, or similar agent internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
Agent Builder
Build AI agents for any domain - customer service, research, operations, creative work, or specialized business processes.
The Core Philosophy
The model already knows how to be an agent. Your job is to get out of the way.
An agent is not complex engineering. It's a simple loop that invites the model to act:
LOOP:
Model sees: context + available capabilities
Model decides: act or respond
If act: execute capability, add result, continue
If respond: return to userThat's it. The magic isn't in the code - it's in the model. Your code just provides the opportunity.
The Three Elements
1. Capabilities (What can it DO?)
Atomic actions the agent can perform: search, read, create, send, query, modify.
Design principle: Start with 3-5 capabilities. Add more only when the agent consistently fails because a capability is missing.
2. Knowledge (What does it KNOW?)
Domain expertise injected on-demand: policies, workflows, best practices, schemas.
Design principle: Make knowledge available, not mandatory. Load it when relevant, not upfront.
3. Context (What has happened?)
The conversation history - the thread connecting actions into coherent behavior.
Design principle: Context is precious. Isolate noisy subtasks. Truncate verbose outputs. Protect clarity.
Agent Design Thinking
Before building, understand:
- Purpose: What should this agent accomplish?
- Domain: What world does it operate in? (customer service, research, operations, creative...)
- Capabilities: What 3-5 actions are essential?
- Knowledge: What expertise does it need access to?
- Trust: What decisions can you delegate to the model?
CRITICAL: Trust the model. Don't over-engineer. Don't pre-specify workflows. Give it capabilities and let it reason.
Progressive Complexity
Start simple. Add complexity only when real usage reveals the need:
| Level | What to add | When to add it |
|---|---|---|
| Basic | 3-5 capabilities | Always start here |
| Planning | Progress tracking | Multi-step tasks lose coherence |
| Subagents | Isolated child agents | Exploration pollutes context |
| Skills | On-demand knowledge | Domain expertise needed |
Most agents never need to go beyond Level 2.
Show full SKILL.md (217 more words)Show less
Domain Examples
Business: CRM queries, email, calendar, approvals Research: Database search, document analysis, citations Operations: Monitoring, tickets, notifications, escalation Creative: Asset generation, editing, collaboration, review
The pattern is universal. Only the capabilities change.
Key Principles
- The model IS the agent - Code just runs the loop
- Capabilities enable - What it CAN do
- Knowledge informs - What it KNOWS how to do
- Constraints focus - Limits create clarity
- Trust liberates - Let the model reason
- Iteration reveals - Start minimal, evolve from usage
Anti-Patterns
| Pattern | Problem | Solution |
|---|---|---|
| Over-engineering | Complexity before need | Start simple |
| Too many capabilities | Model confusion | 3-5 to start |
| Rigid workflows | Can't adapt | Let model decide |
| Front-loaded knowledge | Context bloat | Load on-demand |
| Micromanagement | Undercuts intelligence | Trust the model |
Resources
Philosophy & Theory:
references/agent-philosophy.md- Deep dive into why agents work
Implementation:
references/minimal-agent.py- Complete working agent (~80 lines)references/tool-templates.py- Capability definitionsreferences/subagent-pattern.py- Context isolation
Scaffolding:
scripts/init_agent.py- Generate new agent projects
The Agent Mindset
From: "How do I make the system do X?" To: "How do I enable the model to do X?"
From: "What's the workflow for this task?" To: "What capabilities would help accomplish this?"
The best agent code is almost boring. Simple loops. Clear capabilities. Clean context. The magic isn't in the code.
Give the model capabilities and knowledge. Trust it to figure out the rest.
© shareAI-lab, 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 5 other files (scripts, references) in skills/agent-builder of shareAI-lab/learn-claude-code.
- SKILL.md
- references/agent-philosophy.md
- references/minimal-agent.py
- references/subagent-pattern.py
- references/tool-templates.py
- scripts/init_agent.py
Open the folder on GitHubat commit ce8f9f1
Used in 7 other repositories
We found 7 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 6 other GitHub owners. This page covers the copy in shareAI-lab/learn-claude-code, which our catalogue first saw on October 7, 2026.
- shareAI-lab/mini-claude-codeskills/agent-builder
- Chris-debug-0225/learn-claude-code-javaskills/agent-builder
- wulawulu/learn-claude-code-rsskills/agent-builder
- Turing-Neo/Turing-coding-agentpackages/turing/skills/agent-builder
- keyuchen21/agentic-engineering-handbooktutorials/agent-loop/skills/agent-builder
- juliepy/AI-Engineer-from-scrach06-harnes/learn-claude-code/skills/agent-builder
- majiayu000/claude-skill-registryskills/agent/agent-builder
Compare with similar skills
Agent Builder 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 |
|---|---|---|---|---|---|---|
| Agent Builder this skillshareAI-lab/learn-claude-code | 78k | 6 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Langgraph Agent Patternssoba-labs/langchain-agent-skills | 107 | — | ~3.6k | Automated safety check: Pass | MIT | |
| Agent CreatorOpenHands/extensions | 158 | — | ~1.8k | Automated safety check: Pass | MIT | |
| Pydantic AI Harnesspydantic/pydantic-ai | 20k | — | ~4.9k | Automated safety check: Pass | MIT | |
| Agent BuildershareAI-lab/lab-skills | 314 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Agent BuilderNangoHQ/nango | 13k | 2 repos | ~5.2k | Automated safety check: Pass | Custom licence |
Similar skills
Agent Creator
OpenHands/extensions
Create file-based sub-agents as Markdown files following the OpenHands SDK format.
- Official
Pydantic AI Harness
pydantic/pydantic-ai
Adds optional capabilities to Pydantic AI agents from pydantic-ai-harness, led by Code Mode, which runs many tool calls as one sandboxed Python script.
Agent Builder
shareAI-lab/lab-skills
Helps design and build AI agents for any domain around a minimal loop of capabilities, knowledge and context, adding planning or subagents only when needed.
Agent Builder
NangoHQ/nango
A skill your agent uses when creating, improving, or troubleshooting Claude Code subagents.
Agent Harness Builder
FareedKhan-dev/claude-code-from-scratch
Gives patterns, a tool design checklist and an architecture decision tree for building agent harnesses, tools and multi-agent setups around a model.
More from shareAI-lab/learn-claude-code
Code Review Checklist
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
MCP Server Builder
shareAI-lab/learn-claude-code
Walks through building MCP servers in Python or TypeScript that expose tools, resources and prompts to Claude, with templates, registration and testing.
PDF Processing Guide
shareAI-lab/learn-claude-code
Gives the agent command-line and Python recipes for reading, creating, merging and splitting PDF files, plus tips for large and scanned documents.
Related
Categories
Questions about Agent Builder
What does Agent Builder do?
Design and build AI agents for any domain. An agent skill from shareAI-lab/learn-claude-code. Agent Builder is an agent skill from shareAI-lab/learn-claude-code. Design and build AI agents for any domain.
When should I use Agent Builder?
Agent Builder fits situations like: ask to create an agent; build an assistant; design an AI system; want to understand agent architecture.
How do I install Agent Builder in Claude Code?
Run `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -a claude-code`. Or copy the skill folder (skills/agent-builder in shareAI-lab/learn-claude-code) into .claude/skills/agent-builder in your project. Claude Code loads it when a task matches its description.
How do I install Agent Builder in Codex?
Run `npx skills add shareAI-lab/learn-claude-code --skill agent-builder -a codex`. Or copy the skill folder (skills/agent-builder in shareAI-lab/learn-claude-code) into .agents/skills/agent-builder in your project. Codex loads it when a task matches its description.
Can I use Agent Builder 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 shareAI-lab/learn-claude-code --skill agent-builder -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-builder, .gemini/skills/agent-builder, .github/skills/agent-builder and .opencode/skills/agent-builder in your project.
What does Agent Builder need to run?
Going by SKILL.md and its folder, Agent Builder needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Agent Builder 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 Agent Builder 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Agent Builder use?
Agent Builder 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 Agent Builder use?
About 1.2k tokens (SKILL.md is roughly 4.7k 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 7k tokens, read only when the agent opens those files.
What are the alternatives to Agent Builder?
Skills that share tags, products or a category with Agent Builder: Langgraph Agent Patterns (soba-labs/langchain-agent-skills, 107 stars), Agent Creator (OpenHands/extensions, 158 stars), Pydantic AI Harness (pydantic/pydantic-ai, 20k stars) and Agent Builder (shareAI-lab/lab-skills, 314 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Agent Builder?
shareAI-lab (a GitHub organization) maintains it in shareAI-lab/learn-claude-code, which has 78,138 GitHub stars. The repository holds 4 skills in this directory. The repository was last updated on September 28, 2026.
Source: shareAI-lab/learn-claude-code on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.