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Industry Research

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chuwu57
industry-research

初五的决策级行业研究Skill。围绕行业进入、年度战略、投资/并购初筛、竞争定位或出海,研究行业边界、规模周期、情景、客户采用、技术成本、利润池、单位经济、公司竞争、GTM、渠道、政策与资本,交付可复核报告、证据底稿及风险机会判断。消费者专项使用consumer-research,媒体呈现研究使用industry-media-research。

Overview

Publisherchuwu57
RepositoryChuwu-PR-OS
Skill nameindustry-research
Stars
84
Forks
11
Bundled files
11
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 11 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by chuwu57 on GitHub. Read the source before you install it.

Installation

Install the Industry Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chuwu57/Chuwu-PR-OS/tree/main/PROS-05/Skills/Research/industry-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chuwu57/Chuwu-PR-OS.git /tmp/Chuwu-PR-OS
mkdir -p .claude/skills
cp -r /tmp/Chuwu-PR-OS/PROS-05/Skills/Research/industry-research .claude/skills/industry-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Industry Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Industry Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Industry Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Industry Research

归属说明:本Skill由初五维护,属于初五的个人Skill体系。分享或再分发时请保留本归属说明及已有来源、作者和许可声明。

目标

把行业研究做成可复核的决策系统:明确边界,恢复历史,解释机制,建模利润,验证公司,暴露缺口,并给出会随新证据更新的行动。

开始前

先写一份研究合同,至少固定:

  • 决策、决策人和使用时间;
  • 行业、客户、价值链、地域和时间边界;
  • 核心层、相邻层和排除层;
  • 必答P0、应答P1和非目标;
  • 可用数据、访问限制与完成标准。

先确定核心问题和可修正的假设,最终结论随证据形成。交付按读者任务选择决策简报、数据查阅、研究综述或公众解读;呈现方式与研究深度分别确定,不因格式调整降低证据要求。

如果用户要求「全面行业研究」,默认覆盖下方所有P0与P1。若裁剪模块,要说明对结论的影响。

研究档位

  • 快速版:服务一个明确决策,完成最小P0闭环,只纳入会改变该决策的P1。研究合同、指标字典、证据账本和样本登记可放在报告附录,不强制拆成四个独立文件。不得省略边界、口径、反证、缺口和完成状态。
  • 标准版:覆盖全部P0和相关P1,四类底稿独立保存,至少完成一次来源/计算复核和一次红队。
  • 深度版:覆盖全部P0与P1,建立完整历史、情景、单位经济、样本索引、监测系统与一手研究缺口,可并行分工并进行独立红队。

报告模板是最大结构。快速版可合并章节;技术、资本、政策等模块只在影响决策时保留。裁剪不能删除决定结论成立与否的证据链。

证据纪律

1.先建指标字典,再搜索规模。区分收入、GMV、出货、装机、激活、活跃、ASP、毛利、利润与现金流。 2.每条核心主张同时评价「来源可追溯性」与「对总体的外推能力」。论坛原帖可证明个案,不能证明行业比例;公司官网可证明公司自述,不能独立证明竞争优势。 3.将结论标为「已核事实」「有边界的样本信号」「研究推断」「情景模型」或「待验证假设」。 4.口径冲突要解释或保留多版本,不选择最顺眼的数字。 5.没有真实参数时只给公式、敏感变量和取数计划,不伪造行业平均单位经济。 6.公开VOC与媒介样本必须披露窗口、n、平台、纳入/排除、偏差和商业关系盲区。

开始研究时完整读取research-contract-and-evidence.md。标准版和深度版复制assets/中的研究合同、指标字典、证据账本和样本登记模板;快速版可把四类底稿内嵌在报告附录,但字段要求不变。

执行顺序

P0:决定行业机会是否成立

1.历史与情景:建立同口径历史序列,拆数量、价格、组合、地域、渠道与利用率;把第三方预测、本报告模型和公司目标分开,给保守/基准/积极情景及失效信号。 2.利润池与单位经济:画价值流和现金流;用公司分部、费用、库存和现金验证设备/产品、耗材、软件、服务、渠道和售后;为关键参与者建立参数化单位经济。 3.客户与采用:还原情境、触发、任务、替代、选择、购买/采用、激活、使用、复购/扩产、放弃;区分机制、发生率和因果。 4.竞争战场:按客户任务、技术路线、价格带、渠道或区域分战场;公司只设一个主表,比较可持续优势、收入质量和执行能力。 5.需求形成与商业化:按认知、考虑、验证、转化、激活和留存拆解品牌、PR、媒介、达人、电商、直销、经销、门店、社区和售后。

P1:决定如何进入、执行和监测

1.技术与成本:解释性能—成本边界、替代技术、规模化约束、BOM/材料/软件/后处理和供应链风险。 2.区域与政策:比较需求、供给、价格、渠道、服务、法规、关税、数据和本地化;草案与生效规则分开。 3.资本与组织:核对IPO、融资、并购、重组和破产的交易事实;用研发、销售、交付、本地服务和现金解释战略可执行性。 4.监测系统:为未来12—24个月设置指标、基线、频率、信号源、阈值、责任人和触发动作。每个阈值标注为「监管硬阈值」「行业基准」「企业内部基线」或「建议决策门槛」;建议门槛可以用于验证设计,但不能冒充行业事实。缺企业基线的异常阈值标「方案待实施」。

具体模块问题、计算和证据要求见module-playbook.md。聚焦任务只读取相关章节;全面研究完整读取该文件。

分工与并行

深度研究可按以下独立任务并行:

  • 历史/预测/区域政策;
  • 公司经营/利润池/单位经济;
  • 客户/VOC/媒介达人/渠道;
  • 最终红队。

主执行者统一行业边界、指标字典、来源版本和断言强度。并行任务不得各自发明口径;红队在合稿后检查冲突、漏项和过度断言。

合稿原则

需要跨报告、报道或披露形成共同判断时,读取跨来源综合底稿,复用既有证据账本,按问题组织支持、冲突与未决解释。关键判断依赖调查、实验或效果数字时,再按需读取统计与效果主张审读;不将普通规模数据摘录升级为整套统计审查。

  • 结论先行,每章只保留一个主问题;同一判断只放一个主要叙事位置。
  • 摘要每条判断都能回溯到正文;正文数字能回溯到来源、页码或计算。
  • 每个机会同时写需求证据、利润条件、进入壁垒、反证、领先指标和放弃条件。
  • P0/P1完成度只使用「公开证据闭环」「公开证据部分闭环」「参数模型闭环」「公式框架」「机制部分闭环」「待一手研究」「方案待实施」。篇幅不能替代证据。
  • 公开缺口以及缺口会影响的决策,给出最小验证方法。
  • 标准/深度研究说明相对既有认识的增量;对会改变决策的机制争议,比较候选解释和能区分它们的证据。具体要求见输出与质量门;含图表时按其中图表要求检查独立阅读边界。

完成状态判定

  • 公开证据闭环:事实、口径、反证和覆盖边界都可复核;只有这一状态可写「已验证」。
  • 公开证据部分闭环:主要事实可复核,但区域、战场、时期或关键变量仍有实质缺口。
  • 参数模型闭环:有公式、数值输入及来源、至少三组情景或敏感性、数值盈亏点和失效条件。
  • 公式框架:只有公式、变量、敏感方向和取数要求,缺可辩护的数值输入或盈亏点。
  • 机制部分闭环:机制有多源支持,发生率或因果仍未验证。
  • 待一手研究/方案待实施:公开资料无法回答,或监测设计尚缺内部基线、阈值与责任人。

按本次研究范围选用industry-research-report-template.md中的必要模块。交付前读取output-and-quality-gates.md,并运行:

bash
python3 scripts/audit_research.py /absolute/path/to/report.md --mode quick
# 标准/深度任务将quick改为standard/deep。
# 自定义模块范围时:
python3 scripts/audit_research.py /absolute/path/to/report.md --contract /absolute/path/to/contract.json

按本次模式运行结构初筛;quick只检查摘要、范围、限制和完成状态,standard/deep默认检查全部模块,自定义合同可覆盖模块范围(见research-contract-and-evidence.md)。修正范围内FAIL,对相关WARN核查并说明。脚本通过仅表示结构检查通过,不能证明证据、来源可访问性、计算或任务完成;仍须按output-and-quality-gates.md核实关键结论。

路由

  • 消费者研究、问卷、访谈、产品测试:consumer-research。
  • 行业媒体生态、文章样本、同题叙事:industry-media-research。
  • 已有研究证据转成PR主张与行动:pr-consumer-research。
  • 单篇报道分析、舆情或最终公关取舍:使用对应PR专项Skill。

不要机械串联其他Skill。只有下游任务职责不同且用户需要时才交接。

分发版路径与运行条件

先读取依赖说明。本Skill由初五维护;本副本为PROS 05。references/、assets/和scripts/相对本Skill目录解析;Domains/、Skills/前缀从合集根目录解析。包内Markdown链接相对所在文件解析。未附带的个人案例、文献底稿、状态与来源配置只能由使用者提供,不能声称已读取。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Industry Research AI skill do?

初五的决策级行业研究Skill。围绕行业进入、年度战略、投资/并购初筛、竞争定位或出海,研究行业边界、规模周期、情景、客户采用、技术成本、利润池、单位经济、公司竞争、GTM、渠道、政策与资本,交付可复核报告、证据底稿及风险机会判断。消费者专项使用consumer-research,媒体呈现研究使用industry-media-research。

Why use Industry Research on TypingMind?

Because you install it once and use it with any model. Industry Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Industry Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chuwu57/Chuwu-PR-OS/tree/main/PROS-05/Skills/Research/industry-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Industry Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Industry Research?

As many as you like. As long as a model supports skills, you can use Industry Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Industry Research AI skill free?

Yes. It is published on GitHub by chuwu57 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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