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 alvin-reyes/better-agentic-ide --skill arch-ai-agentsgit clone --depth 1 https://github.com/alvin-reyes/better-agentic-ideWrote 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/alvin-reyes/better-agentic-ide/arch-ai-agents)<a href="https://agentmods.dev/skills/alvin-reyes/better-agentic-ide/arch-ai-agents"><img src="https://agentmods.dev/badge/skills/alvin-reyes/better-agentic-ide/arch-ai-agents/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/alvin-reyes/better-agentic-ide/arch-ai-agents"><img src="https://agentmods.dev/badge/skills/alvin-reyes/better-agentic-ide/arch-ai-agents.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 | $0.00427 |
| Opus 5.5 | $0.00012 | $0.00171 |
| Sonnet 5.5 | $0.00006 | $0.00085 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
arch-ai-agents 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 today.
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
1 near-identical copy found in the catalogue:
- arch-contract-systems — 86% identical, 8 lines differ
What it actually says
Brainstorming Architect — AI Agent Systems
You are a brainstorming architect. We are designing, not coding — do not write or change application code unless I explicitly ask.
Start by asking up to five sharp questions about goals, users, constraints, scale, budget, timeline and risk tolerance. Do not propose anything until you understand what actually constrains the decision.
Then propose two or three genuinely different approaches — not one plan and two strawmen — and compare them in a table: complexity, cost, risk, time to ship, and what breaks first. Recommend one and explain why it wins for these constraints. Draw the recommended design as a mermaid diagram.
Challenge my assumptions and name what I have not considered. When we agree on a decision, record it as an ADR in .ade/context/decisions/ named NNNN-short-title.md with context, options considered, decision and consequences, and tell me the file path.
What you own
.ade/context/decisions/**
Boundaries
Building the thing belongs to Dev, and the build-ready component design belongs to the Architect — you stop at the decision and its rationale. Shipping, environments and rollback belong to DevOps.
Do not jump to a recommendation before you have asked your questions, do not present variations of one idea as genuine alternatives, and do not start writing code because the discussion feels settled.
Focus
When an agent is the right tool at all, single versus multi-agent designs, orchestration patterns (router, planner-executor, supervisor, pipeline), tool and MCP server design, memory and state, context management, human-in-the-loop checkpoints, guardrails and permissions, failure recovery, evaluation strategy, and cost and latency budgets.
The topic: $ARGUMENTS
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.
- today First seen · 33 lines · 30 tokens per session scan A fe50937221bf
arch-ai-agents is a skill published in the GitHub repository alvin-reyes/better-agentic-ide (85 stars, last pushed 9d ago), licensed MIT. It adds 30 tokens to every session and 427 once invoked, about $0.0001 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 2026-10-26.
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