pm-decision

pm-decision is a skill for Claude Code from konglong87/superPM. It costs 56 tokens per session (4,674 once invoked), scanned A, original, MIT.

A structured guide for making major business and product choices, such as building, outsourcing, buying, investing, or entering a market.

In plain words
What is it for?
Use it to compare several plans, assess investments, choose partners, or decide how a product or capability should be developed.
Why use it?
It turns a broad strategic choice into a comparison of options, constraints, risks, and expected effects.

Skill for Claude Code

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

Part of the super-pm plugin — 55 skills, 1 hook shipped together

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.

agentmods
npx agentmods add skills/konglong87/superpm/pm-decision
Any agent
npx skills add konglong87/superPM --skill pm-decision
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 55 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-decision

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-decision.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-decision)
Your own site
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-decision"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-decision.svg" alt="Measured on agentmods" height="20"></a>
Per session 56 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,674 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00056 $0.04674
Opus 5 $0.00028 $0.02337
Sonnet 5 $0.00011 $0.00935
Haiku 4.5 $0.00006 $0.00467

Measured 6d ago against content hash f399c4ff8c80, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

pm-decision 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 6d 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/05-product-strategy/pm-decision/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)

bash "$(dirname "${BASH_SOURCE[0]}")/../../check-update.sh" 2>/dev/null || true
# 创建目录
mkdir -p docs/05-产品战略

# 检查是否有相关前置文档
if [ -f "docs/05-产品战略/产品组合战略.md" ]; then
  echo "✅ 检测到产品组合战略文档"
fi

if [ -f "docs/05-产品战略/资源分配方案.md" ]; then
  echo "✅ 检测到资源分配方案文档"
fi

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。

执行流程

步骤 1: 明确决策问题

询问方式:使用 AskUserQuestion 逐个询问


问题 1: 决策类型

使用 AskUserQuestion 询问:

请问您面临的决策类型是?

A) 自研 vs 外包决策 B) 自研 vs 收购决策 C) 产品投资决策 D) 市场进入决策 E) 业务转型决策 F) 合作伙伴选择 G) 其他(请手动输入)


问题 2: 决策背景

询问:

请简要描述决策背景

引导用户输入:

  • 决策触发因素
  • 面临的问题或机会
  • 决策时间要求
  • 决策影响范围

问题 3: 决策约束条件

询问:

决策面临的主要约束条件是?(可多选)

A) 资金预算限制 B) 时间紧迫性 C) 技术能力限制 D) 人才资源限制 E) 风险承受能力 F) 战略一致性要求 G) 其他(请手动输入)


步骤 2: 识别决策选项

根据决策类型,识别可选方案:


场景A: 自研 vs 外包决策

可选方案

  1. 完全自研
  2. 部分外包
  3. 完全外包
  4. 混合模式(自研核心,外包非核心)

场景B: 自研 vs 收购决策

可选方案

  1. 完全自研
  2. 收购成熟公司
  3. 投资参股
  4. 战略合作
  5. 专利授权

场景C: 产品投资决策

可选方案

  1. 重大投资(全力以赴)
  2. 中等投资(稳健推进)
  3. 小额试探(快速验证)
  4. 暂不投资(观望等待)

步骤 3: 决策矩阵评估

对每个决策选项,进行多维度评估:


评估维度定义

询问:

请选择决策评估维度

推荐维度:

  1. 财务维度:成本、收益、ROI
  2. 时间维度:上市时间、开发周期
  3. 技术维度:技术风险、技术可控性
  4. 战略维度:战略一致性、竞争优势
  5. 资源维度:人力需求、资金需求
  6. 风险维度:市场风险、技术风险、执行风险

逐项评估

针对每个维度,使用 AskUserQuestion 询问每个选项的得分(1-10分):

示例(自研 vs 外包)

请为"完全自研"方案在"财务成本"维度打分(1-10分,分数越低成本越高)

A) 1-2分(成本极高) B) 3-4分(成本较高) C) 5-6分(成本适中) D) 7-8分(成本较低) E) 9-10分(成本极低)

依次评估所有选项、所有维度。


权重设定

询问:

各评估维度的权重如何分配?

引导用户分配权重(总和100%):

维度 权重
财务成本 [X]%
上市时间 [X]%
技术风险 [X]%
战略一致性 [X]%
长期竞争力 [X]%
合计 100%

步骤 4: 决策树分析

针对复杂决策,构建决策树:


决策树构建

询问关键决策节点:

决策的关键不确定性因素是什么?

示例

  1. 市场需求是否达标?(概率:[X]%)
  2. 技术难题能否攻克?(概率:[X]%)
  3. 竞品是否会提前入场?(概率:[X]%)

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. 6d ago First seen · 661 lines · 56 tokens per session scan A f399c4ff8c80

Subscribe to this mod's changes

pm-decision is a skill published in the GitHub repository konglong87/superPM (61 stars, last pushed 2d ago), licensed MIT. It adds 56 tokens to every session and 4,674 once invoked, about $0.0003 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.

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