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 huangjia2019/sdd-in-action --skill bmad-product-briefgit clone --depth 1 https://github.com/huangjia2019/sdd-in-actionWrote 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/huangjia2019/sdd-in-action/bmad-product-brief)<a href="https://agentmods.dev/skills/huangjia2019/sdd-in-action/bmad-product-brief"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-product-brief/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/huangjia2019/sdd-in-action/bmad-product-brief"><img src="https://agentmods.dev/badge/skills/huangjia2019/sdd-in-action/bmad-product-brief.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 | $0.01562 |
| Opus 5 | $0.00015 | $0.00781 |
| Sonnet 5 | $0.00006 | $0.00312 |
| Haiku 4.5 | $0.00003 | $0.00156 |
Grade A, and why
bmad-product-brief 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.
This is a copy
100% identical to bmad-product-brief — 0 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 — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Product Brief
Overview
This skill helps you create compelling product briefs through collaborative discovery, intelligent artifact analysis, and web research. Act as a product-focused Business Analyst and peer collaborator, guiding users from raw ideas to polished executive summaries. Your output is a 1-2 page executive product brief — and optionally, a token-efficient LLM distillate capturing all the detail for downstream PRD creation.
The user is the domain expert. You bring structured thinking, facilitation, market awareness, and the ability to synthesize large volumes of input into clear, persuasive narrative. Work together as equals.
Design rationale: We always understand intent before scanning artifacts — without knowing what the brief is about, scanning documents is noise, not signal. We capture everything the user shares (even out-of-scope details like requirements or platform preferences) for the distillate, rather than interrupting their creative flow.
Conventions
- Bare paths (e.g.
prompts/finalize.md) resolve from the skill root. {skill-root}resolves to this skill's installed directory (wherecustomize.tomllives).{project-root}-prefixed paths resolve from the project working directory.{skill-name}resolves to the skill directory's basename.
Activation Mode Detection
Check activation context immediately:
-
Autonomous mode: If the user passes
--autonomous/-Aflags, or provides structured inputs clearly intended for headless execution:- Ingest all provided inputs, fan out subagents, produce complete brief without interaction
- Route directly to
prompts/contextual-discovery.mdwith{mode}=autonomous
-
Yolo mode: If the user passes
--yoloor says "just draft it" / "draft the whole thing":- Ingest everything, draft complete brief upfront, then walk user through refinement
- Route to Stage 1 below with
{mode}=yolo
-
Guided mode (default): Conversational discovery with soft gates
- Route to Stage 1 below with
{mode}=guided
- Route to Stage 1 below with
What ships with it
11 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.
- agents/artifact-analyzer.md 2.4 KB
- agents/opportunity-reviewer.md 1.7 KB
- agents/skeptic-reviewer.md 1.5 KB
- agents/web-researcher.md 1.6 KB
- bmad-manifest.json 496 B
- customize.toml 1.8 KB
- prompts/contextual-discovery.md 2.6 KB
- prompts/draft-and-review.md 4.0 KB
- prompts/finalize.md 4.1 KB
- prompts/guided-elicitation.md 3.4 KB
- resources/brief-template.md 2.3 KB
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 · 118 lines · 30 tokens per session scan A 044459f71dd9
bmad-product-brief is a skill published in the GitHub repository huangjia2019/sdd-in-action (147 stars, last pushed 18d ago), licensed MIT. It adds 30 tokens to every session and 1,562 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 bmad-product-brief, differing in 0 lines, and is treated as a copy.
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…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
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…