agent-builder

name	agent-builder
description	Use when building a new agent harness, designing tool systems, or structuring multi-agent workflows. Provides patterns, templates, and decision trees for harness engineering.

Agent Builder Skill

When to use this skill

Load this skill when the user wants to:

• Build a new agent from scratch
• Design a tool for an agent
• Structure a multi-agent system
• Debug an agent loop that isn't working
• Choose between agent architectures

Core principle

The agent is always the model. Your job is the harness.

Harness = Tools + Knowledge + Observation + Action + Permissions

Never try to encode intelligence in your harness code. Give the model clean tools, clear context, and get out of the way.

The minimal agent (always start here)

from anthropic import Anthropic
client = Anthropic()

def agent_loop(messages, tools, dispatch, system):
    while True:
        response = client.messages.create(
            model="claude-sonnet-4-20250514",
            system=system, messages=messages,
            tools=tools, max_tokens=8000,
        )
        messages.append({"role": "assistant", "content": response.content})
        if response.stop_reason != "tool_use":
            return
        results = []
        for block in response.content:
            if block.type == "tool_use":
                output = dispatch[block.name](block.input)
                results.append({"type": "tool_result",
                                 "tool_use_id": block.id, "content": output})
        messages.append({"role": "user", "content": results})

Do not add anything until you need it. Every mechanism should earn its place.

Tool design checklist

Before writing a tool, ask:

• Is the name a verb? (bash, read, write — not "file_manager")
• Does the description say WHEN to use it, not just what it does?
• Is the input schema minimal? No optional fields unless truly needed.
• Does it return plain text the model can reason about?
• Does it have a hard timeout?
• Is output truncated to a safe length (≤50k chars)?

Tool description formula

"[Action verb] [what it does]. Use when [specific situation].
[What it returns]. [Any important limits]."

Example:

"Read a file and return numbered lines. Use when you need to inspect
file content or reference specific line numbers. Returns up to 50,000
characters. Use start_line/end_line for large files."

Architecture decision tree

One task, one user, no persistence needed?
    → s01: minimal loop + bash

Need file read/write/search?
    → s02: extended tool dispatch

Need the agent to plan before acting?
    → s03: add todo_write tool

Task too big for one context window?
    → s04: subagent isolation

Need domain-specific knowledge?
    → s05: skill loading

Long-running session, context will overflow?
    → s06: compression + memory file

Complex multi-step project spanning sessions?
    → s07: task graph with dependencies

Slow operations (builds, tests)?
    → s08: background tasks

Work that parallelises across specialties?
    → s09+: agent teams with mailboxes

Need isolation between parallel tasks?
    → s12/s23: git worktrees

Common mistakes

Putting logic in the harness instead of trusting the model Bad: if "error" in output: retry_with_different_approach() Good: return the error to the model and let it decide

Giant system prompts Bad: 5,000-word system prompt covering every scenario Good: load domain knowledge on-demand via skills (s05)

Blocking the loop on slow operations Bad: output = subprocess.run("npm test", timeout=300) Good: run in background thread, notify when done (s08)

Shared mutable state between subagents Bad: subagents writing to the same dict/file without locks Good: each subagent has its own isolated context (s04, s12)

Subagent pattern template

def spawn_subagent(prompt: str, tools=EXTENDED_TOOLS, dispatch=EXTENDED_DISPATCH) -> str:
    messages = [{"role": "user", "content": prompt}]
    while True:
        response = client.messages.create(
            model=MODEL, system=SUBAGENT_SYSTEM,
            messages=messages, tools=tools, max_tokens=8000,
        )
        messages.append({"role": "assistant", "content": response.content})
        if response.stop_reason != "tool_use":
            break
        results = dispatch_tools(response.content, dispatch)
        messages.append({"role": "user", "content": results})
    return "".join(b.text for b in messages[-1]["content"] if hasattr(b, "text"))