bmad-prd

bmad-prd is a skill for Claude Code, Codex from bsahane/memory-mcp-server. It costs 31 tokens per session (3,163 once invoked), scanned A, a copy of bmad-prd, Apache-2.0.

A guided tool for creating, editing, or checking a product requirements document (PRD), which describes what a product should do and why. It records decisions and changes in a project workspace as the document develops.

In plain words
What is it for?
Drafting a new PRD, revising an existing one, or validating whether a PRD is complete and suitable for its intended level of detail.
Why use it?
It helps turn unclear product needs into a reviewed written plan and keeps the reasoning behind decisions available for later work.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit Drafting a new PRD, revising an existing one, or validating whether a PRD is complete and suitable for its intended level of detail.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/bsahane/memory-mcp-server/bmad-prd
Install

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.

Any agent
npx skills add bsahane/memory-mcp-server --skill bmad-prd
Clone the repo
git clone --depth 1 https://github.com/bsahane/memory-mcp-server

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for bmad-prd

README.md
[![agentmods](https://agentmods.dev/badge/skills/bsahane/memory-mcp-server/bmad-prd/github.svg)](https://agentmods.dev/skills/bsahane/memory-mcp-server/bmad-prd)
Your own site
<a href="https://agentmods.dev/skills/bsahane/memory-mcp-server/bmad-prd"><img src="https://agentmods.dev/badge/skills/bsahane/memory-mcp-server/bmad-prd/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.

agentmods 80×15 button for bmad-prd

Your own site · 80×15
<a href="https://agentmods.dev/skills/bsahane/memory-mcp-server/bmad-prd"><img src="https://agentmods.dev/badge/skills/bsahane/memory-mcp-server/bmad-prd.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,163 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00031 $0.03163
Opus 5.5 $0.00012 $0.01265
Sonnet 5.5 $0.00006 $0.00633
Haiku 4.5 $0.00003 $0.00316

Measured 1mo ago against content hash 5c220a51dc6f, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

bmad-prd 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.

Origin

This is a copy

100% identical to bmad-prd — 2 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.

.agent/skills/bmad-prd/SKILL.md · 95 lines

How it starts

The opening of the file, as written. The whole thing — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.

BMad PRD

You are a master facilitator and coach helping the user create, edit, or validate a high quality PRD scoped to the level and rigor appropriate to their stated needs. Fight the urge to do the thinking for them unless they put you into Fast path.

Conventions

  • Bare paths resolve from skill root; {skill-root} is this skill's install dir; {project-root} is the project working dir.
  • {workflow.<name>} resolves to fields in customize.toml's [workflow] table (overrides win per BMad merge rules).
  • {doc_workspace} is the bound run folder.
  • File roles. .memlog.md is the run's canonical memory and audit trail — every decision, change, and override (including headless overrides) lands as one append-only line as the conversation unfolds. All writes go through the shared script, never by hand: uv run {project-root}/_bmad/scripts/memlog.py append --workspace {doc_workspace} --type <decision|change|override|assumption|event> --text "<one-line gist, reason included>" (atomic; read it back only to resume or audit). The PRD is distilled toward it; whatever isn't logged is lost on resume. addendum.md preserves user-contributed depth that belongs in a downstream document (architecture, solution design, UX spec) or earned a place but does not fit the PRD itself — rejected-alternative rationale, options-considered matrices, mechanism/transport decisions, technical-how, in-depth personas, sizing data. Capture to the addendum during the conversation when the user volunteers such content — do not wait for finalize. Audit and override information never goes in the addendum.

On Activation

Forwarded activation: if a caller invoked you with a stated intent and pre-resolved customization fields (e.g. the bmad-create-prd / bmad-edit-prd / bmad-validate-prd shims), honor them verbatim — skip your own intent inference, use the supplied values for those named fields, and resolve only the remaining fields from your own customize.toml.

  1. Resolve customization: uv run {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow. On failure, read {skill-root}/customize.toml directly and use defaults.
  2. Run {workflow.activation_steps_prepend}. Treat {workflow.persistent_facts} as foundational context (entries prefixed file: are loaded). {workflow.external_sources} is an org-configured registry of internal tools (knowledge bases, MCP tools); consult them alongside generic web research on the same triggers, org tools preferred when their directive matches. Research itself fires during Discovery — see Research subagents.
  3. Load {project-root}/_bmad/bmm/config.yaml (+ config.user.yaml if present). Resolve {user_name}, {communication_language}, {document_output_language}, {planning_artifacts}, {project_name}, {date}. Missing keys → neutral defaults; never block.
  4. If headless, follow references/headless.md for the whole run. Otherwise greet the user by name using {user_name} and in their language using {communication_language} — and stay in {communication_language} for every turn for the entire run, not just the greeting. In the greeting, let the user know that at any point they can invoke bmad-party-mode for multi-agent perspectives or bmad-advanced-elicitation for deeper exploration on a specific section. Then scan for misroute on the first message: if the signal points elsewhere (game → BMad GDS; express build → bmad-build; one-pager → bmad-product-brief; vet product idea → bmad-prfaq; agent skill or custom agent → bmad-workflow-builder), suggest they might want the other options before continuing.
  5. Detect intent: Create (no PRD), Update (existing PRD), Validate (critique only). If ambiguous, ask. For Create intent, before binding a fresh workspace, scan {workflow.prd_output_path} for prior in-progress runs (folders matching {workflow.run_folder_pattern} whose prd.md frontmatter status is not final); if any exist, offer to resume rather than starting over.

Read the full file on GitHub · 95 lines

Files

What ships with it

7 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.

Changes

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.

  1. 1mo ago First seen · 95 lines · 31 tokens per session scan A 5c220a51dc6f

Subscribe to this mod's changes

bmad-prd is a skill published in the GitHub repository bsahane/memory-mcp-server (1 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 31 tokens to every session and 3,163 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to bmad-prd, differing in 2 lines, and is treated as a copy.

Related

Other skills, from other repositories

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…

microsoft/ai-agents-for-beginners · 200 tokens

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

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…

vercel/next.js · 95 tokens

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.

microsoft/vscode · 53 tokens

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…

microsoft/vscode · 71 tokens

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…

vercel/next.js · 83 tokens