davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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 agentmods add commands/davepoon/buildwithclaude/new-agentgit clone --depth 1 https://github.com/davepoon/buildwithclaudeWrote 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/commands/davepoon/buildwithclaude/new-agent)<a href="https://agentmods.dev/commands/davepoon/buildwithclaude/new-agent"><img src="https://agentmods.dev/badge/commands/davepoon/buildwithclaude/new-agent.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00020 | $0.00554 |
| Opus 5 | $0.00010 | $0.00277 |
| Sonnet 5 | $0.00004 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
new-agent 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 5d 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are scaffolding a new AG2 (AutoGen) agent. Follow the AG2 framework patterns exactly.
Instructions
-
Ask the user for:
- Agent name and purpose
- What tools/capabilities it needs
- Which LLM model to use (default: gpt-4o-mini)
- Whether it needs external API access
-
Create the agent following this exact structure:
File Structure
agents/<agent-name>/
<agent_name>.py # Agent definition + tools
README.md # Capabilities documentation
Agent Code Pattern
import json
from autogen import ConversableAgent
from autogen.tools import tool
# --- Tool Functions ---
# Each tool returns a JSON string with {"success": bool, "data": ..., "error": ...}
@tool()
def tool_name(param1: str, param2: int = 10) -> str:
"""Clear description of what this tool does.
Args:
param1: Description of param1
param2: Description of param2 (default: 10)
"""
try:
# Implementation
result = {"key": "value"}
return json.dumps({"success": True, "data": result})
except Exception as e:
return json.dumps({"success": False, "error": str(e)})
# --- Agent Definition ---
agent = ConversableAgent(
name="AgentName",
description="One-line description for orchestrator routing",
system_message="""You are a [role description].
Your capabilities:
- Capability 1
- Capability 2
Guidelines:
- Always use the appropriate tool for the task
- Return structured responses
- Handle errors gracefully and explain what went wrong
""",
llm_config={"model": "gpt-4o-mini"},
functions=[tool_name],
)
Key Rules
- Tool functions MUST return JSON strings, not dicts or raw values
- Tool functions MUST have docstrings (used for LLM function calling schema)
- System messages should be specific about the agent's role and boundaries
- Agent
descriptionis used by orchestrators to route tasks -- keep it concise - Use
@tool()decorator fromautogen.tools - Group related tools in the same file
- Never use bare
except:-- always catch specific exceptions orException
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.
- 5d ago First seen · 83 lines · 20 tokens per session scan A 501f2cf0430e
new-agent is a command published in the GitHub repository davepoon/buildwithclaude (3,415 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 554 once invoked, about $0.0001 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 commands, from other repositories
convert-to-todowrite-tasklist-prompt
Purpose: Transform verbose, context-heavy slash commands into efficient TodoWrite tasklist-based methods with parallel subagent execution for 60-70% speed improvements.
security-audit
Perform a comprehensive security audit of the codebase to identify potential vulnerabilities, insecure patterns, and security best practice violations.
better-auth:add-plugin
Add a better-auth plugin to an existing project. Configures server and client plugins with proper imports.
audit
Perform security audit on codebase.
off
Turn claude-bionify off so Claude's replies render normally.
organize-files
Organize and rename files based on content analysis.