pm-priority

pm-priority is a skill for Claude Code from konglong87/superPM. It costs 59 tokens per session (5,775 once invoked), scanned A, original, MIT.

A Chinese-language procedure for ranking several product requests when time or resources are limited. It supports scoring or grouping methods such as RICE, KANO, and MoSCoW to decide what should be done first.

In plain words
What is it for?
Use it to prioritize multiple requirements and record the resulting research in a project documentation folder.
Why use it?
It provides an objective comparison when requests compete or their priority is unclear. It first gathers the request list and asks which ranking method to use.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: mentions subagents; names the AskUserQuestion tool.

Part of the super-pm plugin — 57 skills, 1 hook shipped together

Good fit Use it to prioritize multiple requirements and record the resulting research in a project documentation folder.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/konglong87/superpm/pm-priority
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 konglong87/superPM --skill pm-priority
Clone the repo
git clone --depth 1 https://github.com/konglong87/superPM

Made for: Claude Code.

Or install super-pm, the plugin that ships this one along with the rest of its 57 skills, 1 hook.

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 pm-priority

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-priority/github.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-priority)
Your own site
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-priority"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-priority/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 pm-priority

Your own site · 80×15
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-priority"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-priority.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,775 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found 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 $0.00059 $0.05775
Opus 5.5 $0.00024 $0.02310
Sonnet 5.5 $0.00012 $0.01155
Haiku 4.5 $0.00006 $0.00577

Measured 26d ago against content hash eced8c0c746b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pm-priority 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 26d 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.

skills/01-demand-insight/pm-priority/SKILL.md · 661 lines

How it starts

The opening of the file, as written. The whole thing — 661 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preamble (run first)

# 检查是否有确认需求清单
if [ ! -f "docs/01-需求调研/确认需求清单.md" ]; then
  echo "⚠️  未找到确认需求清单"
  echo ""
  echo "建议先执行 /pm-clarify 细化需求"
  echo ""
  echo "您可以选择:"
  echo "A) 执行 /pm-clarify 先细化需求(推荐)"
  echo "B) 手动输入需求列表(快速模式)"
  echo "C) 从需求池导入(执行过 /pm-pool)"
fi

写入时边界

只讨论时不写文件,启动检查不创建目录。用户要求保存产物时,先确认当前产品、精确输出路径及已有文件的覆盖/追加选择;确认后仅在实际 Write 前创建该输出文件的父目录。完整安装可参见 skills/_shared/artifact-policy.md,单独安装本 skill 也按本段执行。


跨 Agent 交互规则

当流程要求与用户交互时:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 已有 docs 文件不能替代本轮用户回答。

产出要求:每项排序保留需求标识、证据来源及日期、评分依据和置信度;缺失数据标为假设,不以估算冒充事实。将最终取舍及责任人传递给 MVP 方案。

缺数据的 RICE 门禁:如果需求只有 A/B/C 之类占位代号,且缺少 Reach(覆盖人数)或 Effort(工作量)估计,不得替 A/B/C 填任何精确数值或计算示例分数;即使写“假设”“仅示例”也会让占位符看起来已有排序。只给空白输入表、公式、待补的数据和一个关键澄清问题,STOP and WAIT。获得各需求含义和同口径的估计后,才可计算或展示标注假设的区间。

执行流程

步骤 1: 读取前置数据(主 agent)

如果有确认需求清单:

使用 Read 工具读取 docs/01-需求调研/确认需求清单.md

提取需求列表。

如果有需求调研报告:

使用 Read 工具读取 docs/01-需求调研/需求调研报告.md

提取初步需求列表和背景信息。

如果有市场调研报告:

使用 Read 工具读取 docs/01-需求调研/市场调研报告.md(如存在)

提取市场数据、竞品信息,为后续评分提供数据支撑。

如果没有前置文档:

进入快速模式,使用 AskUserQuestion 收集需求列表。


步骤 2: 选择排序模型(主 agent - 用户交互)

使用 AskUserQuestion 询问:

🎯 选择需求优先级排序模型:

A) RICE评分 - 综合Reach、Impact、Confidence、Effort(推荐) B) KANO模型 - 基于用户满意度分类需求 C) MoSCoW法则 - Must/Should/Could/Won't分类 D) 自定义权重 - 自定义评分维度

用户选择后,记录到变量 PRIORITY_MODEL


步骤 3: Subagent 并行预分析(v2.0 核心优化)

优化说明:

  • 每个需求派发一个 subagent,读取所有前置文档,基于数据和所选模型生成初始评分建议
  • 多个需求并行分析,总耗时等于单个需求分析时间
  • 预分析结果包含评分 + 数据依据,用户只需确认或微调
  • 大幅减少逐维度询问的交互轮次

3.1 构建预分析任务

对每个需求,构建一个 subagent 任务:

[
  {
    "task_id": "prescore_req_1",
    "requirement": "{需求1名称}",
    "model": "{PRIORITY_MODEL}",
    "docs": ["docs/01-需求调研/确认需求清单.md", "docs/01-需求调研/需求调研报告.md"]
  },
  {
    "task_id": "prescore_req_2",
    "requirement": "{需求2名称}",
    "model": "{PRIORITY_MODEL}",
    "docs": ["docs/01-需求调研/确认需求清单.md", "docs/01-需求调研/需求调研报告.md"]
  }
]

Read the full file on GitHub · 661 lines

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. 26d ago Changed · +6 lines eced8c0c746b
  2. 1mo ago First seen · 655 lines · 59 tokens per session scan A d6c33ab7d8eb

Subscribe to this mod's changes

pm-priority is a skill published in the GitHub repository konglong87/superPM (77 stars, last pushed 7d ago), licensed MIT. It adds 59 tokens to every session and 5,775 once invoked, about $0.0002 per session on Opus 5.5. 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.