learn-claude-code is an educational project that builds a small Claude Code-like agent harness from scratch, showing how model access is combined with the surrounding code that lets an agent perceive, reason, and act in an environment. It is for people learning to develop real AI agents. The catalogue add-ons support workflows built around this agent harness.
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 skills add shareAI-lab/learn-claude-code --skill agent-buildergit clone --depth 1 https://github.com/shareAI-lab/learn-claude-codeWrote 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/skills/shareai-lab/learn-claude-code/agent-builder)<a href="https://agentmods.dev/skills/shareai-lab/learn-claude-code/agent-builder"><img src="https://agentmods.dev/badge/skills/shareai-lab/learn-claude-code/agent-builder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shareai-lab/learn-claude-code/agent-builder"><img src="https://agentmods.dev/badge/skills/shareai-lab/learn-claude-code/agent-builder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 5.1 | $0.00125 | $0.01070 |
| Opus 5.5 | $0.00050 | $0.00428 |
| Sonnet 5.5 | $0.00025 | $0.00214 |
| Haiku 4.5 | $0.00013 | $0.00107 |
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 1mo 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.
Copies of this mod
8 near-identical copies found in the catalogue:
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
- agent-builder — 100% identical, 0 lines differ
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 |
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.
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.
- 1mo ago First seen · 130 lines · 125 tokens per session scan A 4b88e0f4c19c
agent-builder is a skill published in the GitHub repository shareAI-lab/learn-claude-code (78,643 stars, last pushed 18d ago), licensed MIT. It adds 125 tokens to every session and 1,070 once invoked, about $0.0005 per session on Opus 5.5. 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 skills, from other repositories
example-skill
An example skill for demonstration.
eval-dataset-design
A guide to creating reliable test sets for AI agents. Each evaluation task includes the request, the simulated user's behaviour, a reset starting state, and a success check that can be independently verified.
aatmf-t10-confidentiality-breach
AATMF T10 — Integrity & Confidentiality Breach. System prompt extraction, training-data extraction, model-weight leakage, private-key recovery.
mochi-remind
Handle due reminders — notify the user with natural language and mark them done.
ensembl-database
Query Ensembl genome database REST API for 250+ species. Gene lookups, sequence retrieval, variant analysis, comparative genomics, orthologs, VEP predictions, for genomic research.
ai-music
A workflow that turns a topic or script into a finished short video by combining writing, images or video clips, spoken narration, subtitles, background music, and final assembly.