agent-builder

A guide for designing and building AI agents for tasks such as research, customer service, operations, and creative work.

In plain words
What is it for?
Use it to plan capabilities such as searching, reading, creating, sending, querying, or modifying information, and to design agents with tools, knowledge, and subagents.
Why use it?
It helps break an agent into the actions it can take, the information it can use, and the conversation context it needs.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/shareai-lab/learn-claude-code/agent-builder
Any agent
npx skills add shareAI-lab/learn-claude-code --skill agent-builder
Clone the repo
git clone --depth 1 https://github.com/shareAI-lab/learn-claude-code

Made for: Claude Code, Codex.

Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,070 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00125 $0.01070
Opus 5 $0.00063 $0.00535
Sonnet 5 $0.00025 $0.00214
Haiku 4.5 $0.00013 $0.00107

Measured yesterday against content hash 4b88e0f4c19c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agent-builder 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 yesterday.

The scan reads SKILL.md. This mod also ships 4 executable files (references/minimal-agent.py, references/subagent-pattern.py, references/tool-templates.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

Copies of this mod

8 near-identical copies found in the catalogue:

skills/agent-builder/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 user

That'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

Read the full file on GitHub · 130 lines

Files

What ships with it

5 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.

Changes

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.

  1. yesterday First seen · 130 lines · 125 tokens per session scan A 4b88e0f4c19c

Subscribe to this mod's changes

agent-builder is a skill published in the GitHub repository shareAI-lab/learn-claude-code (75,745 stars, last pushed 5d ago), licensed MIT. It adds 125 tokens to every session and 1,070 once invoked, about $0.0006 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.

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