pm-clarify

pm-clarify is a skill for Claude Code from konglong87/superPM. It costs 63 tokens per session (2,565 once invoked), scanned A, original, MIT.

A requirements-clarification workflow that turns a rough feature list into specific scenarios, user actions, boundaries, and acceptance checks. Acceptance checks are the conditions used to decide whether the work is complete.

In plain words
What is it for?
Use it to refine product requirements one at a time, describe how users will use each feature, define edge cases, and write clear completion criteria.
Why use it?
It reduces misunderstandings before development by making unclear requests testable and defining what is included or excluded. It can read an existing research report or collect a short list manually.

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 — 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-clarify
Any agent
npx skills add konglong87/superPM --skill pm-clarify
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-clarify

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-clarify.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-clarify)
Your own site
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-clarify"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-clarify.svg" alt="Measured on agentmods" height="20"></a>
Per session 63 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,565 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 $0.00063 $0.02565
Opus 5 $0.00032 $0.01282
Sonnet 5 $0.00013 $0.00513
Haiku 4.5 $0.00006 $0.00257

Measured 6d ago against content hash 3862ad6cdb4a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

pm-clarify 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/01-demand-insight/pm-clarify/SKILL.md · 379 lines

How it starts

The opening of the file, as written. The whole thing — 379 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
# 创建需求调研目录
mkdir -p docs/01-需求调研

# 检查是否有需求调研报告
if [ ! -f "docs/01-需求调研/需求调研报告.md" ]; then
  echo "⚠️  未找到需求调研报告"
  echo ""
  echo "建议先执行 /pm-demand 完成需求调研"
  echo ""
  echo "您可以选择:"
  echo "A) 执行 /pm-demand 先完成需求调研(推荐)"
  echo "B) 手动输入需求列表(快速模式)"
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: 读取前置数据

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:

  • 产品名称
  • 目标用户
  • 核心痛点
  • 初步需求清单

如果有创意方案库

使用 Read 工具读取 docs/01-需求调研/创意方案库.md

提取:

  • 核心创意方案

如果没有前置文档

进入快速模式,使用 AskUserQuestion 收集需求列表:

📝 快速模式 - 请列出核心需求:

请逐个输入需求,每个需求一行: 例如:

  • 快速下单
  • 订单追踪
  • 会员体系

输入"完成"结束


步骤 2: 逐个细化需求

关键原则

  • 一次只细化一个需求
  • 明确使用场景
  • 明确边界条件
  • 明确验收标准

对每个需求,AI 询问以下问题:

问题 1: 使用场景

需求"{需求名称}"的使用场景是什么?

A) 用户首次使用时 B) 用户日常使用时 C) 用户遇到特定问题时 D) 用户完成特定任务后 E) 其他(请手动输入)

记录到变量 SCENARIO


问题 2: 具体行为

用户在这个场景下具体要做什么?

例如:"用户打开APP,选择商品,点击立即购买,完成支付"

请描述具体步骤:

用户描述后,AI 整理成流程步骤。


问题 3: 边界条件

这个需求的边界条件是什么?

A) 时间限制 - 如"订单30分钟内未支付自动取消" B) 数量限制 - 如"每个用户最多创建10个项目" C) 权限限制 - 如"仅会员可使用" D) 状态限制 - 如"仅未完成的订单可修改" E) 其他(请手动输入)

记录到变量 BOUNDARY_CONDITIONS


问题 4: 异常处理

如果出现异常情况,如何处理?

A) 提示用户并引导解决 B) 自动重试或降级处理 C) 记录日志并通知管理员 D) 回滚操作并提示失败 E) 其他(请手动输入)

记录到变量 EXCEPTION_HANDLING


问题 5: 验收标准

这个需求的验收标准是什么?

例如:"用户能在3步内完成下单,支付成功率>95%"

请描述可衡量的标准:

用户描述后,记录到变量 ACCEPTANCE_CRITERIA


步骤 3: 汇总细化结果

AI 将每个需求的细化信息整理成结构化格式。


步骤 4: 生成确认需求清单

使用 Write 工具创建 docs/01-需求调研/确认需求清单.md

# 确认需求清单

## 一、基础信息

- **产品名称**: {PRODUCT_NAME}
- **目标用户**: {TARGET_USER}
- **生成时间**: {当前时间}

---

## 二、需求详情

### 需求 1: {需求名称}

**使用场景**:
{SCENARIO}

**具体步骤**:
1. {步骤1}
2. {步骤2}
3. {步骤3}

**边界条件**:
{BOUNDARY_CONDITIONS}

**异常处理**:
{EXCEPTION_HANDLING}

**验收标准**:
{ACCEPTANCE_CRITERIA}

**优先级**: 待定(需通过 /pm-priority 确定)

---

### 需求 2: {需求名称}

**使用场景**:
{SCENARIO}

**具体步骤**:
1. {步骤1}
2. {步骤2}

**边界条件**:
{BOUNDARY_CONDITIONS}

**异常处理**:
{EXCEPTION_HANDLING}

**验收标准**:
{ACCEPTANCE_CRITERIA}

**优先级**: 待定

---

## 三、需求汇总表

| 序号 | 需求名称 | 场景 | 边界条件 | 验收标准 | 状态 |
|------|----------|------|----------|----------|------|
| 1 | {需求1} | {场景简述} | {边界简述} | {标准简述} | ✅ 已细化 |
| 2 | {需求2} | {场景简述} | {边界简述} | {标准简述} | ✅ 已细化 |

---

## 四、下一步建议

建议执行:

1. **/pm-market** - 市场分析,了解竞品和市场规模(推荐)
2. **/pm-priority** - 优先级排序,决定先做哪个需求
3. **/pm-mvp** - MVP规划,确定第一版要做什么

---

**项目状态**: 需求细化完成
**生成时间**: {时间戳}
**生成工具**: super-pm

Read the full file on GitHub · 379 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 · 379 lines · 63 tokens per session scan A 3862ad6cdb4a

Subscribe to this mod's changes

pm-clarify is a skill published in the GitHub repository konglong87/superPM (61 stars, last pushed 2d ago), licensed MIT. It adds 63 tokens to every session and 2,565 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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