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-design-architecturegit 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-design-architecture)<a href="https://agentmods.dev/skills/majiayu000/claude-skill-registry/agent-design-architecture"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-design-architecture/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-design-architecture"><img src="https://agentmods.dev/badge/skills/majiayu000/claude-skill-registry/agent-design-architecture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00062 | $0.03403 |
| Opus 5 | $0.00031 | $0.01702 |
| Sonnet 5 | $0.00012 | $0.00681 |
| Haiku 4.5 | $0.00006 | $0.00340 |
Grade A, and why
Agent Design Architecture 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.
How it starts
The opening of the file, as written. The whole thing — 481 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Design Architecture
Effective agent systems require thoughtful architecture that balances capability, safety, and maintainability. This skill guides designing agent systems from first principles.
Agent Fundamentals
What Makes an Effective Agent
An agent is a system that:
- Perceives its environment (through tools, data, human input)
- Reasons about goals and available actions
- Acts to change state toward objectives
- Reflects on outcomes to improve future actions
- Communicates intent and reasoning to humans
Key distinction from simple chatbots:
- Agents have goals beyond responding conversationally
- Agents persist across multiple interactions
- Agents compose multiple tools into workflows
- Agents reason about outcomes and adapt
- Agents operate with bounded autonomy (within constraints)
Agent Architecture Patterns
Pattern 1: Tool-Using Agent (Simplest)
User Request → Model Reasoning → Tool Selection → Tool Execution → Result → Response
When to use: Single-step tasks, straightforward tool selection Example: "Find the latest sales data and summarize" Complexity: Low | Autonomy: Low
Design considerations:
- Clear tool descriptions for LLM selection
- Explicit constraints on tool combinations
- Fallback handling for tool failures
Pattern 2: Agentic Loop (Iterative)
Goal → Reasoning → Action Selection → Execute → Observe → Success?
↑_____No_________↓
Reflect & Replan
When to use: Complex, multi-step tasks; planning required Example: "Analyze customer complaints, identify patterns, propose solutions" Complexity: Medium | Autonomy: Medium
Design considerations:
- Loop termination criteria (max iterations, goal achieved, resource exhausted)
- State tracking between iterations
- Reflection mechanism (what worked, what didn't)
- Tool selection constraints per iteration
Pattern 3: Planning Agent (Explicit Reasoning)
Goal → Decompose → Create Plan → Execute Step → Verify → Adapt Plan
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 · 481 lines · 62 tokens per session scan A a521a547b757
Agent Design Architecture is a skill published in the GitHub repository majiayu000/claude-skill-registry (606 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 3,403 once invoked, about $0.0003 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 2026-09-03.
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