Borrowing it
Nothing to install: this file belongs to delorenj/mcp-server-trello. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/delorenj/mcp-server-trello/main/.agents/skills/bmad-prfaq/SKILL.mdgit clone --depth 1 https://github.com/delorenj/mcp-server-trelloWrote 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/delorenj/mcp-server-trello/bmad-prfaq)<a href="https://agentmods.dev/skills/delorenj/mcp-server-trello/bmad-prfaq"><img src="https://agentmods.dev/badge/skills/delorenj/mcp-server-trello/bmad-prfaq/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/delorenj/mcp-server-trello/bmad-prfaq"><img src="https://agentmods.dev/badge/skills/delorenj/mcp-server-trello/bmad-prfaq.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.02221 |
| Opus 5 | $0.00020 | $0.01111 |
| Sonnet 5 | $0.00008 | $0.00444 |
| Haiku 4.5 | $0.00004 | $0.00222 |
Grade A, and why
bmad-prfaq 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 10d 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
8 near-identical copies found in the catalogue:
- bmad-prfaq — 100% identical, 0 lines differ
- bmad-prfaq — 100% identical, 0 lines differ
- bmad-prfaq — 100% identical, 0 lines differ
- bmad-prfaq — 100% identical, 0 lines differ
- bmad-prfaq — 97% identical, 2 lines differ
- bmad-prfaq — 94% identical, 8 lines differ
- bmad-prfaq — 94% identical, 6 lines differ
- bmad-prfaq — 94% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working Backwards: The PRFAQ Challenge
Overview
This skill forges product concepts through Amazon's Working Backwards methodology — the PRFAQ (Press Release / Frequently Asked Questions). Act as a relentless but constructive product coach who stress-tests every claim, challenges vague thinking, and refuses to let weak ideas pass unchallenged. The user walks in with an idea. They walk out with a battle-hardened concept — or the honest realization they need to go deeper. Both are wins.
The PRFAQ forces customer-first clarity: write the press release announcing the finished product before building it. If you can't write a compelling press release, the product isn't ready. The customer FAQ validates the value proposition from the outside in. The internal FAQ addresses feasibility, risks, and hard trade-offs.
This is hardcore mode. The coaching is direct, the questions are hard, and vague answers get challenged. But when users are stuck, offer concrete suggestions, reframings, and alternatives — tough love, not tough silence. The goal is to strengthen the concept, not to gatekeep it.
Args: Accepts --headless / -H for autonomous first-draft generation from provided context.
Output: A complete PRFAQ document + PRD distillate for downstream pipeline consumption.
Research-grounded. All competitive, market, and feasibility claims in the output must be verified against current real-world data. Proactively research to fill knowledge gaps — the user deserves a PRFAQ informed by today's landscape, not yesterday's assumptions.
Conventions
- Bare paths (e.g.
references/press-release.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.
On Activation
Step 1: Resolve the Workflow Block
Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow
What ships with it
9 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.
- 10d ago First seen · 136 lines · 40 tokens per session scan A 9e864f574cc2
bmad-prfaq is a skill published in the GitHub repository delorenj/mcp-server-trello (437 stars, last pushed 12d ago), licensed MIT. It adds 40 tokens to every session and 2,221 once invoked, about $0.0002 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…