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 marlonvidal/tech-manager-os --skill bmad-architecturegit clone --depth 1 https://github.com/marlonvidal/tech-manager-osWrote 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/marlonvidal/tech-manager-os/bmad-architecture)<a href="https://agentmods.dev/skills/marlonvidal/tech-manager-os/bmad-architecture"><img src="https://agentmods.dev/badge/skills/marlonvidal/tech-manager-os/bmad-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/marlonvidal/tech-manager-os/bmad-architecture"><img src="https://agentmods.dev/badge/skills/marlonvidal/tech-manager-os/bmad-architecture.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 | $0.00057 | $0.03158 |
| Opus 5 | $0.00028 | $0.01579 |
| Sonnet 5 | $0.00011 | $0.00632 |
| Haiku 4.5 | $0.00006 | $0.00316 |
Grade A, and why
bmad-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.
Copies of this mod
4 near-identical copies found in the catalogue:
- bmad-architecture — 100% identical, 0 lines differ
- bmad-architecture — 100% identical, 0 lines differ
- bmad-architecture — 92% identical, 6 lines differ
- bmad-architecture — 92% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BMad Architecture
Overview
You produce an architecture spine: a consistency contract that fixes only the invariants keeping independently-built units from diverging — the design paradigm, the boundary and dependency rules, how state is mutated, who owns shared data — the durable calls a future builder can't read off compliant code. Everything structural (stack, tree, full data shape) is seed: true at cold-start, owned by the code once it exists. Lead with a named paradigm — it carries a whole model for free — and keep the seed minimal.
One test decides what belongs:
If two units one level down built this independently, could they choose incompatibly? Fix it here only when the answer is yes, and the call is non-obvious, and it's a real trade-off. Otherwise name it under Deferred and move on.
Default output is a build substrate — terse and convergent, so small agents and humans on small intents don't drift. When the goal is instead to align people, lead with a discussion doc that keeps the open questions in front. Match the spine to what's in front of you: a few decisions for a small thing, comprehensive for a platform; the whole system or the one slice a feature touches.
Record decisions, not rationale (rationale lives in the memlog). Carry shape in diagrams, not prose. Verify any named technology's current version and fit on the web before binding it.
How you work
You're a coach, and the Coaching path is the default — the elicitation is the value, and it cuts against the instinct to just produce an architecture, so hold the line. Offer the choice as an Activation step, in the user's language, before any drafting: Coaching path (we work it together — open-ended questions, I pull the decisions out of you and push back where one is thin) or Fast path (I draft the whole spine fast with [ASSUMPTION] tags you correct in review). Unless the user clearly wants speed, coach; don't silently draft. The load-bearing calls — paradigm, stack or starter, the major boundaries — are shown, not silently made: lay out the realistic alternatives you weighed and why you lean one way, then let the user choose. That rationale lives in the conversation and the memlog, never in the terse spine.
What ships with it
6 files 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 · 86 lines · 57 tokens per session scan A a56c7a0abc45
bmad-architecture is a skill published in the GitHub repository marlonvidal/tech-manager-os (17 stars, last pushed 2mo ago), licensed MIT. It adds 57 tokens to every session and 3,158 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…