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 majiayu000/claude-skill-registry --skill agent-designergit clone --depth 1 https://github.com/majiayu000/claude-skill-registryWrote 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/majiayu000/claude-skill-registry/agent-designer)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-designer"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-designer/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/majiayu000/claude-skill-registry/agent-designer"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-designer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00030 | $0.02306 |
| Opus 5 | $0.00015 | $0.01153 |
| Sonnet 5 | $0.00006 | $0.00461 |
| Haiku 4.5 | $0.00003 | $0.00231 |
Grade A, and why
agent-designer 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 9d 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.
This is a copy
98% identical to agent-designer — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 279 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Designer - Multi-Agent System Architecture
Tier: POWERFUL
Category: Engineering
Tags: AI agents, architecture, system design, orchestration, multi-agent systems
Overview
Agent Designer is a comprehensive toolkit for designing, architecting, and evaluating multi-agent systems. It provides structured approaches to agent architecture patterns, tool design principles, communication strategies, and performance evaluation frameworks for building robust, scalable AI agent systems.
Core Capabilities
1. Agent Architecture Patterns
Single Agent Pattern
- Use Case: Simple, focused tasks with clear boundaries
- Pros: Minimal complexity, easy debugging, predictable behavior
- Cons: Limited scalability, single point of failure
- Implementation: Direct user-agent interaction with comprehensive tool access
Supervisor Pattern
- Use Case: Hierarchical task decomposition with centralized control
- Architecture: One supervisor agent coordinating multiple specialist agents
- Pros: Clear command structure, centralized decision making
- Cons: Supervisor bottleneck, complex coordination logic
- Implementation: Supervisor receives tasks, delegates to specialists, aggregates results
Swarm Pattern
- Use Case: Distributed problem solving with peer-to-peer collaboration
- Architecture: Multiple autonomous agents with shared objectives
- Pros: High parallelism, fault tolerance, emergent intelligence
- Cons: Complex coordination, potential conflicts, harder to predict
- Implementation: Agent discovery, consensus mechanisms, distributed task allocation
Hierarchical Pattern
- Use Case: Complex systems with multiple organizational layers
- Architecture: Tree structure with managers and workers at different levels
- Pros: Natural organizational mapping, clear responsibilities
- Cons: Communication overhead, potential bottlenecks at each level
- Implementation: Multi-level delegation with feedback loops
What ships with it
1 file 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.
- 9d ago First seen · 279 lines · 30 tokens per session scan A d19d74ea9d8b
agent-designer is a skill published in the GitHub repository majiayu000/claude-skill-registry (604 stars, last pushed yesterday), licensed MIT. It adds 30 tokens to every session and 2,306 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to agent-designer, differing in 2 lines, and is treated as a copy.
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