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 selvarajmurugesan90/ops-engineering-skills --skill agent-architecture-designgit clone --depth 1 https://github.com/selvarajmurugesan90/ops-engineering-skillsWrote 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/selvarajmurugesan90/ops-engineering-skills/agent-architecture-design)<a href="https://agentmods.dev/skills/selvarajmurugesan90/ops-engineering-skills/agent-architecture-design"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/agent-architecture-design/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/selvarajmurugesan90/ops-engineering-skills/agent-architecture-design"><img src="https://agentmods.dev/badge/skills/selvarajmurugesan90/ops-engineering-skills/agent-architecture-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 25 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
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 | $0.00091 | $0.02771 |
| Opus 5.5 | $0.00036 | $0.01108 |
| Sonnet 5.5 | $0.00018 | $0.00554 |
| Haiku 4.5 | $0.00009 | $0.00277 |
Grade A, and why
agent-architecture-design 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.
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Architecture Design
Purpose
An "agent" is a loop: an LLM repeatedly observes state, decides on an action (call a tool, ask the user, or finish), and updates state based on the result, until some termination condition is met. Getting this loop's shape wrong is the single biggest source of production incidents in agentic systems — not model quality. Agents that loop forever, that accumulate unbounded context, that hold too many high-privilege tools in one prompt, or that have no checkpoint for a human to intervene, fail in ways that are expensive, hard to debug, and sometimes destructive. This skill defines a small set of proven architecture patterns (ReAct-style loop, plan-and-execute, finite-state/graph) and the state, memory, and control-flow decisions that make an agent safe and debuggable to operate, independent of which model or vendor SDK is driving it.
When to use
- Starting a new agent project and deciding "should this be one prompt with tools, a ReAct loop, or a directed graph of steps?"
- An existing agent occasionally loops, stalls, or takes an unexpected destructive action, and you need to redesign its control flow.
- Deciding whether a task needs one agent with many tools or several narrower agents (see multi-agent-orchestration).
- Designing how an agent's memory persists across sessions (vs. what lives only in the current context window).
- Code review of an agent's main loop before it is given write access to production systems (files, cloud APIs, payment systems, ticketing).
- Adding a human-in-the-loop approval checkpoint to an agent that currently runs fully autonomously.
Prerequisites & environment
- Working knowledge of an LLM API that supports structured tool/function calling (the concept is portable across Anthropic, OpenAI, Google, and open models — exact request/response shapes differ by vendor).
- A chosen orchestration surface: a raw API loop you write yourself, or a framework/runtime (e.g. an agent SDK, LangGraph-style graph runtime, or a CLI agent host like Claude Code). This skill is framework-agnostic; adapt the patterns to whichever runtime you use.
- Access to the tools/APIs the agent will call, ideally in a sandboxed or staging environment before granting production credentials.
- A way to capture traces/logs of each loop iteration (even a structured log file is enough to start).
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 · 242 lines · 91 tokens per session scan A 7205ce4f9743
agent-architecture-design is a skill published in the GitHub repository selvarajmurugesan90/ops-engineering-skills (43 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 91 tokens to every session and 2,771 once invoked, about $0.0004 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.
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