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mb-from-prdmb 来自 珠三角

Agent Skill

mb-from-prd 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

685

周安装

28

GitHub Stars

38

下载量

220
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mb-from-prd(mb 来自 珠三角)
来源仓库:https://github.com/mrvladd-d/memobank
仓库路径:skills/mb-from-prd
安装命令:
npx skills add https://github.com/mrvladd-d/memobank --skill mb-from-prd
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mrvladd-d/memobank --skill mb-from-prd

简介

mb-from-prd 用于查找、检索和筛选相关信息,支持关键词定位。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中需要快速定位候选结果时使用。
  • 通过 GitHub 安装,使用 npx skills add 命令添加指定仓库的 skill。
  • 安装前需确认权限范围、维护状态,以及是否会触发联网或文件读写。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

mb-from-prd — PRD → Memory Bank (greenfield)

  • What it does: converts a PRD into structured project knowledge and traceable planning artifacts.
  • Use it when: the project is greenfield and prd.md or equivalent requirements already exist.
  • Input: prd.md or user-provided PRD text plus an initialized .memory-bank/.
  • Output: product brief, RTM, epics, features, and concept docs ready for /prd-to-tasks.

Preconditions

  • You are in the repo root.
  • prd.md exists (or the user provides PRD text).
  • .memory-bank/ exists. If not, run mb-init first (or create the skeleton manually).

Process

1) Load and sanity-check PRD

  1. Read prd.md.
  2. Identify missing information and contradictions.
  3. Start a task protocol folder:

- .protocols/PRD-BOOTSTRAP/ - plan.md (steps) - decision-log.md (Q/A and choices)

2) Skills / tooling discovery (optional but recommended)

If the PRD mentions “use skills / tools / CLIs”:

  • run /find-skills (project-installed first; marketplace second)
  • propose a minimal set of relevant skills to use (do not install without confirmation)

3) Deep Questioning (rounds)

Use ./references/shared-deep-questioning.md.

  • Ask questions in rounds of 3–5.
  • After each round: summarize, update decision-log.md, and ask the next round.
  • If user is temporarily unavailable: record Open questions in decision-log.md and stop. Do not proceed by inventing facts.

If the target mode is full autonomous:

  • non-blocking gaps may be recorded as explicit Assumptions
  • blocking gaps (security/compliance/external contracts/data-loss risks) must halt the run

4) Write L1 Product brief

Update .memory-bank/product.md (use the user’s wording).

5) Requirements + RTM

Update .memory-bank/requirements.md:

  • Enumerate REQ-IDs.
  • Define “out of scope”.
  • Fill RTM: REQ → Epic → Feature → Test.

6) Create Epics (L2, draft-first)

For each epic:

  • Create .memory-bank/epics/EP-<NNN>-<slug>.md
  • Use references/epic-template.md.
  • Ensure business value + success metrics.
  • Default status: draft until open questions are resolved.

7) Create Features (L3, draft-first)

For each feature:

  • Create .memory-bank/features/FT-<NNN>-<slug>.md
  • Use references/feature-template.md.
  • Ensure autonomy and explicit acceptance criteria.
  • Default status: draft until acceptance criteria + verification plan are solid.

8) Tasks planning (per-feature, no “everything at once”)

Do not generate a full-task backlog “в лоб” for all features in one pass.

Instead:

  1. Seed .memory-bank/tasks/backlog.md with a short structure (waves + placeholders).
  2. For each selected feature, run /prd-to-tasks FT-<NNN> to produce:

- .memory-bank/tasks/plans/IMPL-FT-<NNN>.md - atomic TASK-* items grouped by waves

This keeps planning accurate and avoids speculative task explosions.

9) Duo docs for key concepts

For every concept that would otherwise require “reading many files” to understand later:

  • .memory-bank/architecture/<concept>.md (WHAT/WHY)
  • .memory-bank/guides/<concept>.md (HOW)

10) Update index

Update .memory-bank/index.md with annotated links to everything new.

11) Review gate

Run a fresh-context review (preferably mb-review).

12) Autonomous handoff (optional)

If the goal is “PRD → done without more user interaction”:

  • do not execute tasks from here manually
  • hand off to generated project command /autonomous

Definition of done

  • product.md + requirements.md are coherent.
  • Every REQ maps to an Epic/Feature in RTM.
  • Epics and features exist with acceptance criteria.
  • backlog.md exists as a plan skeleton; feature-level tasks are produced via /prd-to-tasks.
  • index.md is updated.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

34.46%
按下载量换算76

Claude

33.39%
按下载量换算73

Cursor

18.1%
按下载量换算40

Gemini CLI

8.8%
按下载量换算19

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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