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Agent Skill · 研究检索

pm%3aprd(下午%3aprd)

pm%3aprd 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。

pm%3aprd 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,暂无第三方扫描结果,按安装量在研究检索类的 73,675 个 Skill 里排第 64,557。

总安装 297周安装 12GitHub Stars 公开资料未说明下载 93核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:pm%3aprd(下午%3aprd)
来源仓库:https://github.com/cloudvoyant/codevoyant
仓库路径:skills/pm%3Aprd
安装命令:
npx skills add https://github.com/cloudvoyant/codevoyant --skill pm: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/cloudvoyant/codevoyant --skill pm:prd

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选产品需求文档相关资料。适合根据关键词快速定位功能描述、用户故事或验收标准。使用时需明确产品版本和目标用户。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。安装前建议确认权限范围和维护状态。

pm%3aprd 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/pm%3Aprd

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/cloudvoyant/codevoyant --skill pm:prd;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

pm%3aprd 怎么安装?

在终端执行 npx skills add https://github.com/cloudvoyant/codevoyant --skill pm:prd。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

pm%3aprd 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

pm%3aprd 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

pm%3aprd 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

pm%3aprd 用的人多吗?

公开数据是累计安装 297 次,近一周安装 12 次,下载 93 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

pm%3aprd 安全吗?

没有查到第三方扫描结果,安全性无法从公开资料判断,安装前请自行审查源码、依赖和命令行为。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 没有第三方扫描结果,安全性只能靠自己读源码判断。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 要求有安全扫描结论才肯安装的团队。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
pm%3aprd

下午%3aprd · 本页

297—需要联网未明确未收录

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Compatibility: AskUserQuestion falls back to numbered list on non-Claude-Code platforms.

Skill Requirements

command -v npx >/dev/null 2>&1 || echo "MISSING: npx"

Critical Rules

  • One PRD per invocation — never generate multiple PRDs in one run
  • Consume research from .codevoyant/explore/{slug}/summary.md if available
  • No markdown tables — use bullets, definition lists, and plain prose
  • Problem statements describe user pain, not solutions
  • Success metrics must be measurable (baseline + target)
  • Out-of-scope section is required
  • Requirements must use P0/P1/P2 prioritization — never a flat unordered list
  • Acceptance criteria required for every user story
  • Problem statements must cite at least one evidence source (research artifact, user quote, support ticket, or analytics datum)
  • Prefer Mermaid diagrams for any visual structure in the PRD. Never use ASCII art.

Step 0: Parse arguments

FEATURE="${1:-}"
BG_FLAG=false; SILENT=false
[[ "$*" =~ --bg|-b ]] && BG_FLAG=true
[[ "$*" =~ --silent ]] && SILENT=true

If FEATURE looks like a Linear URL, extract the issue ID and fetch issue details.

Step 1: Gather context

If FEATURE is empty, ask:

AskUserQuestion:
  question: "What feature or capability is this PRD for?"
  header: "Feature"
  options:
    - label: "I'll describe below"
    - label: "Link a Linear issue (paste URL)"

Derive SLUG from FEATURE. Check for research artifact at .codevoyant/explore/{SLUG}/summary.md. If found, read it as RESEARCH_CONTEXT (also read any files in .codevoyant/explore/{SLUG}/research/) and inform the user it will be used.

Ask:

AskUserQuestion:
  questions:
    - question: "What is the primary user problem this feature solves?"
      header: "User problem"
      options:
        - label: "I'll describe below"
        - label: "Pull from research artifact"
    - question: "What does success look like? (measurable outcome)"
      header: "Success metric"
      options:
        - label: "I'll describe below"
        - label: "Pull from research artifact"

Step 1.5: Research backfill (if no research artifact)

Check for research artifacts at .codevoyant/explore/{SLUG}/summary.md or .codevoyant/explore/{SLUG}/research/.

If research exists: load it as RESEARCH_CONTEXT and skip this step.

If no research exists: tell the user "No prior research found for this feature — I'll run a quick web search to ground the PRD."

Ask one round of clarifying questions:

AskUserQuestion:
  questions:
    - question: "Who is the primary user this feature is for?"
      header: "Target user"
      options:
        - label: "I'll describe below"
    - question: "What existing solutions do users use today (if known)?"
      header: "Status quo"
      options:
        - label: "I'll describe below"
        - label: "Unknown — research it"

Then launch 2 Sonnet agents in parallel (both run_in_background: false, model: claude-sonnet-4-6):

Agent A — Market and user research:

Prompt: Search for evidence about the user problem for "{FEATURE}". Run WebSearch("{FEATURE} user problems {year}"), WebSearch("{FEATURE} why users need"), and WebSearch("{TARGET_USER} pain points {FEATURE}"). Fetch 2+ relevant URLs. Write 4–6 key findings to .codevoyant/explore/{SLUG}/research/prd-backfill-market.md with source citations. Flag every unverifiable claim as [UNVERIFIED].

Agent B — Competitive landscape:

Prompt: Search for existing solutions and competitors for "{FEATURE}". Run WebSearch("{FEATURE} alternatives"), WebSearch("{FEATURE} competitors"), and WebSearch("best {FEATURE} tools"). Fetch 2+ relevant URLs (competitor homepages or review pages). Write 4–6 key findings to .codevoyant/explore/{SLUG}/research/prd-backfill-competitive.md. Include for each competitor: target customer, core claim, one key strength, one gap. Cite sources.

Wait for both to complete. Read their outputs as RESEARCH_CONTEXT.

Executive decision protocol for gaps: When research is absent or ambiguous, make confident product design decisions rather than deferring or hedging. The guiding question: *what is most likely to create a sticky, resonant product?* Principles:

  • Prefer depth of value for a specific user over breadth across many
  • Prefer strong opinionated defaults over flexible-but-vague feature sets
  • Prefer clear differentiation over feature parity with incumbents
  • Prefer outcomes users care about over outputs teams can easily build

Label every such decision [DESIGN DECISION] in the PRD so the user can override it. Do not leave fields blank or write "TBD" — make a call and explain the reasoning briefly.

Step 2: Confirm scope

Present a one-paragraph summary (feature, user problem, success metric, research available). Ask:

AskUserQuestion:
  question: "Does this capture the PRD scope?"
  header: "Scope check"
  options:
    - label: "Yes — draft the PRD"
    - label: "Adjust scope (describe below)"

Step 3: Draft PRD

Set:

  • OUTPUT_PATH = .codevoyant/prds/{SLUG}/{SLUG}.md
mkdir -p ".codevoyant/prds/{SLUG}"

Write the PRD to OUTPUT_PATH using this structure (no markdown tables):

# PRD: {Feature Name}

**Status:** Draft
**Date:** {DATE}
**Author:** PM

## Problem Statement

{2–4 sentences describing the user pain, with evidence from research if available. Describes the problem, not the solution.}

## Goals

### Leading indicators (early signals, days to weeks)

- **{Metric name}**: baseline {X} → target {Y} by {timeframe}
  - Measurement method: {how we measure this}
  - Source: {evidence citation or "TBD"}

### Lagging indicators (outcome measures, weeks to months)

- **{Metric name}**: baseline {X} → target {Y} by {timeframe}
  - Measurement method: {how we measure this}
  - Source: {evidence citation or "TBD"}

## Non-Goals / Out of Scope

- {explicit deferral}
- {explicit deferral}

## User Stories

- **As a** {user type}, **I want to** {action} **so that** {outcome}
  - **Given** {context/precondition}
  - **When** {action taken}
  - **Then** {observable result}
- (repeat)

## Requirements

### P0 — Must-have (launch blocker)

- {requirement} — {rationale: why blocking}

### P1 — Nice-to-have (high value, ship when ready)

- {requirement} — {rationale: what it unlocks}

### P2 — Future (validate direction, don't build yet)

- {requirement} — {rationale: why deferred}

> If everything is P0, nothing is P0. Be ruthless about what truly blocks launch.

## Proposed Solution

{brief description of the approach — not implementation details}

### Key Decisions
- {decision}: {rationale}

## Dependencies

- {system or team}: {what is needed}

## Open Questions

- {question} — {proposed answer or "unresolved"}

## Risks

- {risk}: {mitigation}

Step 4: Quality checkpoint

Check the drafted PRD against these criteria:

  • Problem statement describes user pain, not a solution → flag if solution-phrased
  • Each goal has a baseline, target, and timeframe → flag if missing
  • Out-of-scope section is present and non-empty → flag if absent
  • User stories have "so that [outcome]" clauses → flag if missing
  • Problem statement is grounded in evidence (research, support data, user quotes) → flag if it reads as opinion without citation
  • Requirements have at least one P0 item → flag if P0 section is empty (product likely not scoped)
  • At least one leading and one lagging metric defined → flag if only one type present

Auto-fix what can be fixed without human judgment (add missing section skeletons with [TODO]).

Report what was auto-fixed and any remaining issues requiring user attention.

Step 5: Register + Notify

npx @codevoyant/agent-kit plans register \
  --name "{SLUG}-prd" \
  --plugin pm \
  --description "PRD: {FEATURE}" \
  --total "0"
if [ "$SILENT" != "true" ]; then
  npx @codevoyant/agent-kit notify \
    --title "pm:prd complete" \
    --message "PRD draft for '{FEATURE}' written to {OUTPUT_PATH}. Run /pm:approve to commit."
fi

Report: "PRD draft written to {OUTPUT_PATH}. Run /pm:approve to commit to docs/prd/{SLUG}/."

平台分布

Codex

34.95%
按下载量换算33

Claude

29.78%
按下载量换算28

Cursor

20.9%
按下载量换算19

Gemini CLI

10.85%
按下载量换算10

安全审计

暂无安全审计结果可展示。

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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。目前只有一个公开来源,建议同时核对源仓库。

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