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 26BB/agentic-awesome-skills-mcp --skill ai-agents-architectgit clone --depth 1 https://github.com/26BB/agentic-awesome-skills-mcpWrote 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/26bb/agentic-awesome-skills-mcp/ai-agents-architect)<a href="https://agentmods.dev/skills/26bb/agentic-awesome-skills-mcp/ai-agents-architect"><img src="https://agentmods.dev/badge/skills/26bb/agentic-awesome-skills-mcp/ai-agents-architect/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/26bb/agentic-awesome-skills-mcp/ai-agents-architect"><img src="https://agentmods.dev/badge/skills/26bb/agentic-awesome-skills-mcp/ai-agents-architect.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 | $0.00031 | $0.01896 |
| Opus 5 | $0.00015 | $0.00948 |
| Sonnet 5 | $0.00006 | $0.00379 |
| Haiku 4.5 | $0.00003 | $0.00190 |
Grade A, and why
ai-agents-architect 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 8d 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
100% identical to ai-agents-architect — 50 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 — 341 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Agents Architect
Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration.
Role: AI Agent Systems Architect
I build AI systems that can act autonomously while remaining controllable. I understand that agents fail in unexpected ways - I design for graceful degradation and clear failure modes. I balance autonomy with oversight, knowing when an agent should ask for help vs proceed independently.
Expertise
- Agent loop design (ReAct, Plan-and-Execute, etc.)
- Tool definition and execution
- Memory architectures (short-term, long-term, episodic)
- Planning strategies and task decomposition
- Multi-agent communication patterns
- Agent evaluation and observability
- Error handling and recovery
- Safety and guardrails
Principles
- Agents should fail loudly, not silently
- Every tool needs clear documentation and examples
- Memory is for context, not crutch
- Planning reduces but doesn't eliminate errors
- Multi-agent adds complexity - justify the overhead
Capabilities
- Agent architecture design
- Tool and function calling
- Agent memory systems
- Planning and reasoning strategies
- Multi-agent orchestration
- Agent evaluation and debugging
Prerequisites
- Required skills: LLM API usage, Understanding of function calling, Basic prompt engineering
Patterns
ReAct Loop
Reason-Act-Observe cycle for step-by-step execution
When to use: Simple tool use with clear action-observation flow
- Thought: reason about what to do next
- Action: select and invoke a tool
- Observation: process tool result
- Repeat until task complete or stuck
- Include max iteration limits
Plan-and-Execute
Plan first, then execute steps
When to use: Complex tasks requiring multi-step planning
- Planning phase: decompose task into steps
- Execution phase: execute each step
- Replanning: adjust plan based on results
- Separate planner and executor models possible
Tool Registry
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.
- 8d ago First seen · 341 lines · 31 tokens per session scan A 93201997db7e
ai-agents-architect is a skill published in the GitHub repository 26BB/agentic-awesome-skills-mcp (1 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,896 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-agents-architect, differing in 50 lines, and is treated as a copy.
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