pm-market

pm-market is a skill for Claude Code from konglong87/superPM. It costs 60 tokens per session (2,673 once invoked), scanned A, original, MIT.

A guided market-research workflow for understanding an industry, its competitors, customers, risks, and opportunities. It can use existing product-research documents or ask for basic information first.

In plain words
What is it for?
Use it to investigate market size and growth, compare competitors, study customer needs, or review industry policies and risks.
Why use it?
It gives product teams a defined process for gathering market information instead of starting research without a clear scope or inputs.

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

Good fit Use it to investigate market size and growth, compare competitors, study customer needs, or review industry policies and risks.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/konglong87/superpm/pm-market
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-market
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-market

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/konglong87/superpm/pm-market"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-market.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 60 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,673 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 $0.00060 $0.02673
Opus 5 $0.00030 $0.01337
Sonnet 5 $0.00012 $0.00535
Haiku 4.5 $0.00006 $0.00267

Measured 10d ago against content hash f5e2ad668dfc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

pm-market 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 10d 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-market/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"
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(主 agent)

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
  • 行业赛道
  • 目标用户
  • 核心痛点

如果没有需求调研报告

进入快速模式,使用 AskUserQuestion 收集基础信息。


步骤 2: 确定搜索方向(主 agent - 用户交互)

使用 AskUserQuestion 询问:

🎯 我要重点分析哪些方面?

A) 市场规模与增长趋势 B) 竞品分析 C) 用户需求与行为 D) 行业政策与风险 E) 全部分析(推荐但耗时较长)

用户选择后,记录到变量 MARKET_FOCUS


步骤 3: 派发 subagent 执行市场调研(核心优化)

优化说明

  • 主 agent 只负责派发任务和接收结果
  • Subagent 执行所有搜索和分析工作
  • 搜索细节不占用主 agent 上下文

3.1 构建搜索任务清单

根据用户选择,构建任务清单:

如果选择"市场规模"

[
  {
    "task_id": "market_size",
    "keywords": ["{行业赛道} 市场规模 2025", "{行业赛道} 增长趋势"],
    "domains": ["iresearch.cn", "analysys.cn", "iimedia.cn"]
  }
]

如果选择"竞品分析"

[
  {
    "task_id": "competitor_analysis",
    "keywords": ["{行业赛道} 竞品分析", "{行业赛道} 主要玩家"],
    "domains": ["36kr.com", "huxiu.com", "qimai.cn"]
  }
]

如果选择"全部"

[
  {
    "task_id": "market_size",
    "keywords": ["{行业赛道} 市场规模"],
    "domains": ["iresearch.cn", "analysys.cn"]
  },
  {
    "task_id": "competitor_analysis",
    "keywords": ["{行业赛道} 竞品"],
    "domains": ["36kr.com", "huxiu.com"]
  },
  {
    "task_id": "user_behavior",
    "keywords": ["{目标用户} 消费行为"],
    "domains": ["questmobile.com.cn"]
  }
]

3.2 使用 Agent 工具派发 subagent

单个任务

使用 Agent 工具:

Tool: Agent
Parameters:
  subagent_type: "general-purpose"
  description: "市场调研-{task_id}"
  prompt: |
    你是一个市场调研专家。请执行以下任务:

    **任务 ID**: {task_id}
    **产品名称**: {产品名称}
    **行业赛道**: {行业赛道}
    **搜索关键词**: {keywords}
    **目标数据源**: {domains}

    **要求**:
    1. 搜索工具优先级:首选 AnySearch(Bash 调用 `anysearch_cli.py(先定位:检查 ~/.claude/skills/anysearch/scripts/ ~/.opencode/skills/anysearch/scripts/ ~/.openclaw/skills/anysearch/scripts/ ~/.cursor/skills/anysearch/scripts/ ~/.anysearch/scripts/ 或 which anysearch_cli.py,找到后 python3 <路径>)`search "查询词" --max_results 5 --domain finance --sub_domain finance.cn_stock),失败降级到 Exa MCP(mcp__exa__web_search_exa),最后才用 WebSearch。使用 WebSearch 时标注「⚠️ 降级模式」
    2. 搜索每个关键词
    3. 优先搜索目标数据源
    4. 提取关键数据(市场规模、增长率、主要玩家等)
    5. 返回结构化的 JSON 结果

    **输出格式**:
    ```json
    {
      "task_id": "{task_id}",
      "findings": [
        {
          "title": "数据标题",
          "source": "数据来源",
          "value": "数据值",
          "date": "数据日期"
        }
      ],
      "summary": "关键发现总结"
    }
    ```

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. 10d ago First seen · 379 lines · 60 tokens per session scan A f5e2ad668dfc

Subscribe to this mod's changes

pm-market is a skill published in the GitHub repository konglong87/superPM (65 stars, last pushed 6d ago), licensed MIT. It adds 60 tokens to every session and 2,673 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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