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Turn Claude into a disciplined research analyst — verify facts against primary sources, brief any topic, draft flagship
中文版本见本文件下半部分 / Chinese version below
Turn Claude — or any LLM terminal — into a disciplined research analyst.
A collection of Claude Skills covering the full market / equity / industry research workflow: verify a fact against primary sources, spin up a topic briefing, or draft a flagship report — each step is its own skill. Every skill enforces the discipline real analysts live by: cite the original source, flag what can't be verified, never fabricate a number. Use any skill standalone, or chain them into an end-to-end pipeline. Runs in Claude Code / Desktop and ports to other LLM terminals (Codex / Gemini / Copilot).
| Skill | Purpose | Typical triggers |
|---|---|---|
| verifying | Trace a statement back to whitelisted primary sources | "verify X" / "is this true" / "find the original source" |
| topic-brief | Generate a thematic observation briefing (HTML, paste-into-WeChat-ready) for any subject (region / industry / issue / institution) | "做一份 XX 观察" / "generate a briefing on Y" / "/topic-brief" |
| analyst-research | Three-mode end-to-end research workflow. User picks scope at trigger: light (4-5 page memo, 0 charts, ~15 min), medium (12-15p topic analysis, 6-10 charts, ~1 h), or heavy (flagship 30-40p / 15k+ word report, 25-35+ charts, ~2-3 h, full multi-stage workflow: framing → sourcing → analysis → drafting → review, multi-LLM optional). Reports default to English; the AI replies in the user's chat language. Battle-tested on the Saudi Vision 2030 deep-dive. | "research report" / "投研报告" / "深度分析" / "做研报" / "5-page memo" / "/analyst-research" |
| local-vault | Build & query a local Markdown knowledge base: convert PDF / Office / images / code into retrieval-friendly Markdown (local-first, cloud OCR fallback), then answer questions over the vault with retrieval discipline (coverage self-checks, lossy-content flags, MOC proposals) | "build/sync my local knowledge base" / "convert these files to markdown for AI" / "整理我的资料库" / "本地知识库" |
The list grows with each release. Full change history in CHANGELOG.md.
/plugin install (recommended for Claude Code users)This repo ships with .claude-plugin/marketplace.json — it is itself a marketplace. In Claude Code:
/plugin marketplace add https://github.com/genli-ai/market-research-skills.git
/plugin install market-research-skills@market-research-skills
Future updates in one command:
/plugin update market-research-skills@market-research-skills
~/.claude/skills/ (works for Claude Desktop / other LLM terminals)git clone https://github.com/genli-ai/market-research-skills.git
cp -r market-research-skills/skills/* ~/.claude/skills/
Subsequent updates:
cd market-research-skills && git pull
cp -r skills/* ~/.claude/skills/
Use sparse-checkout to pull a single subdirectory:
git clone --filter=blob:none --no-checkout https://github.com/genli-ai/market-research-skills.git
cd market-research-skills
git sparse-checkout init --cone
git sparse-checkout set skills/verifying
git checkout
cp -r skills/verifying ~/.claude/skills/
.zip fileEach Release attaches a .zip file for every skill. After download:
unzip verifying.zip -d ~/.claude/skills/verifying
A .zip file simply contains SKILL.md and any resources — the format is portable across every LLM terminal. To install on non-Claude terminals:
.zip file:
unzip verifying.zip -d ./verifying
~/.codex/skills/ (verify in current Codex docs)~/.gemini/skills/ (verify in Gemini docs)Tool-call action verbs inside each SKILL.md (read full text / fetch web body / search-engine query / image recognition / database query) are described in generic semantic terms, so each terminal's LLM can map them to its own local tool set.
npx skills / skills.sh (one command, 60+ terminals)This repo is indexed by skills.sh (the open npx skills tool by Vercel Labs). One command installs straight from this repo — no clone, no manual copy — and always resolves the latest from main, so there is no version to pin:
# Install all skills into Claude Code
npx skills add genli-ai/market-research-skills -a claude-code -s '*'
# Pick one skill, and/or a different terminal (cursor / github-copilot / gemini-cli / codex / ...)
npx skills add genli-ai/market-research-skills -s verifying -a cursor
# Preview what's available without installing
npx skills add genli-ai/market-research-skills --list
Update later with npx skills update. Omit -a to auto-detect installed terminals and choose interactively.
market-research-skills/
├── README.md # This file
├── LICENSE # MIT
├── CHANGELOG.md # Version history
├── .claude-plugin/
│ ├── plugin.json # Plugin manifest
│ └── marketplace.json # Marketplace manifest (self-registering)
├── skills/
│ └── <skill-name>/
│ ├── SKILL.md # Canonical English version (loaded by LLM)
│ ├── SKILL.zh.md # Chinese reference (kept in sync; not loaded)
│ ├── README.md # Skill-specific docs (optional)
│ ├── references/ # Reference materials (optional)
│ ├── scripts/ # Skill-specific scripts (optional)
│ └── assets/ # Static assets (optional)
├── scripts/
│ └── pack.sh # Package a skill as a .zip file
└── releases/ # Locally generated .zip artifacts (gitignored)
└── <skill-name>.zip # Easy to copy and share directly
Every skill must have a SKILL.md (English, canonical, loaded by LLMs) whose frontmatter contains at minimum:
---
name: <skill-name> # Must match the folder name
description: <When to trigger and what the skill does — written in English>
---
A SKILL.zh.md may co-exist as a Chinese reference. The two files must be kept in sync when edited.
.zip./scripts/pack.sh verifying
# Output: releases/verifying.zip
The packaged .zip only includes the canonical SKILL.md plus other resources — the Chinese reference SKILL.zh.md is excluded.
.zipThere are two distribution paths:
releases/<skill-name>.zip and send to anyone directly (chat, email, Slack, etc.)..zip file. Users download it from the Releases page.The releases/ folder is .gitignored — the repo does not store binary artifacts. Each Release on GitHub carries the .zip as an attached asset.
skills/. Folder name = skill name (lowercase + hyphens).SKILL.md (English, canonical) with proper frontmatter.SKILL.zh.md Chinese reference — keep both in sync when modified.CHANGELOG.md.把 Claude(或任意 LLM 终端)变成一个讲纪律的研究分析师。
一组覆盖市场研究 / 投研 / 行业分析完整工作流的 Claude Skills:核实一个事实、产出一份主题简报、撰写一篇旗舰研报——每个环节都是一个独立 skill。每个 skill 都贯彻真实分析师的纪律:引用一手来源、标注无法核实的内容、绝不编造数字。可单独用任一 skill,也可串成端到端流水线。支持 Claude Code / Desktop,并可移植到其他 LLM 终端(Codex / Gemini / Copilot)。
| Skill | 用途 | 典型触发语 |
|---|---|---|
| verifying | 信息核实:把陈述追溯到白名单内的一手来源 | 「帮我核实 X」「这条信息是真的吗」「找一下原始出处」 |
| topic-brief | 主题观察简报生成器:为任意主题(区域 / 行业 / 议题 / 机构)产出可粘贴公众号的 HTML 简报 | 「做一份 XX 观察 / 简报」「/topic-brief」 |
| analyst-research | 三档端到端研究工作流。用户触发时选档:light(4-5 页备忘、0 图、约 15 分钟)/ medium(12-15 页主题分析、6-10 图、约 1 小时)/ heavy(30-40 页 / 1.5 万字+ 旗舰报告、25-35+ 图、约 2-3 小时、完整多阶段工作流:框定 → 取数 → 分析 → 起草 → 复盘、可选多 LLM 协作)。报告默认英文,AI 按用户聊天语言回复。已在沙特 Vision 2030 深度报告项目跑通。 | 「写研报」「投研报告」「深度分析」「主题分析」「5 页 memo」「/analyst-research」 |
| local-vault | 本地 Markdown 知识库:把 PDF / Office / 图片 / 代码转成带检索 frontmatter 的 Markdown(本地优先、云端 OCR 兜底),再基于 vault 负责任地回答问题(覆盖度自检、有损内容标注、MOC 沉淀) | 「整理我的资料库」「把文件转成 md 给 AI 读」「本地知识库」「读我的本地 vault 回答」 |
列表会随版本更新。完整变更见 CHANGELOG.md。
/plugin install(推荐 Claude Code 用户)本 repo 自带 .claude-plugin/marketplace.json,本身就是一个 marketplace。在 Claude Code 里:
/plugin marketplace add https://github.com/genli-ai/market-research-skills.git
/plugin install market-research-skills@market-research-skills
未来更新一键完成:
/plugin update market-research-skills@market-research-skills
git clone https://github.com/genli-ai/market-research-skills.git
cp -r market-research-skills/skills/* ~/.claude/skills/
后续更新:
cd market-research-skills && git pull
cp -r skills/* ~/.claude/skills/
不想要全套时,用 sparse-checkout 只拉某个子目录:
git clone --filter=blob:none --no-checkout https://github.com/genli-ai/market-research-skills.git
cd market-research-skills
git sparse-checkout init --cone
git sparse-checkout set skills/verifying
git checkout
cp -r skills/verifying ~/.claude/skills/
.zip 文件每个 Release 会附上每个 skill 单独的 .zip 文件。下载后:
unzip verifying.zip -d ~/.claude/skills/verifying
.zip 文件里装着 SKILL.md 与资源文件——zip 是通用格式,任意 LLM 终端都能直接解压使用。安装到非 Claude 终端的步骤:
.zip 文件:
unzip verifying.zip -d ./verifying
~/.codex/skills/(以最新 Codex 文档为准)~/.gemini/skills/(以 Gemini 文档为准)每个 SKILL.md 里的工具调用动作(通读全文 / 抓取网页正文 / 搜索引擎检索 / 图像识别 / 数据库查询)都用通用语义动词描述,让各终端的 LLM 映射到自家工具。
npx skills / skills.sh(一条命令,支持 60+ 终端)本 repo 已被 skills.sh(Vercel Labs 的开源 npx skills 工具)收录。一条命令直接从本 repo 安装——无需克隆、无需手动拷贝——且每次都拉取 main 的最新版,没有版本号要锁定:
# 全部 skill 装进 Claude Code
npx skills add genli-ai/market-research-skills -a claude-code -s '*'
# 只装某一个,或换一个终端(cursor / github-copilot / gemini-cli / codex / ...)
npx skills add genli-ai/market-research-skills -s verifying -a cursor
# 不安装,仅预览有哪些 skill
npx skills add genli-ai/market-research-skills --list
之后用 npx skills update 更新。省略 -a 会自动检测已装终端并让你交互选择。
market-research-skills/
├── README.md # 本文件
├── LICENSE # MIT
├── CHANGELOG.md # 版本变更
├── .claude-plugin/
│ ├── plugin.json # 本 repo 作为 Claude Code plugin 的清单
│ └── marketplace.json # 本 repo 同时作为 marketplace(自我注册)
├── skills/
│ └── <skill-name>/
│ ├── SKILL.md # 英文权威版(被 LLM 加载)
│ ├── SKILL.zh.md # 中文参考版(与 SKILL.md 同步;不被 LLM 加载)
│ ├── README.md # 该 skill 的独立说明(可选)
│ ├── references/ # 引用的资料(可选)
│ ├── scripts/ # 该 skill 自带的脚本(可选)
│ └── assets/ # 静态资源(可选)
├── scripts/
│ └── pack.sh # 打包某个 skill 为 .zip 文件
└── releases/ # 本地生成的 .zip 产物(.gitignored)
└── <skill-name>.zip # 方便你直接拷贝发给别人
每个 skill 必须有一个 SKILL.md(英文权威版,被 LLM 加载),文件顶部 frontmatter 至少包含:
---
name: <skill-name> # 与文件夹名一致
description: <一句话英文:什么时候该触发、做什么事>
---
可同时维护一个 SKILL.zh.md 中文参考版。两份在修改时必须保持同步。
.zip./scripts/pack.sh verifying
# 输出 releases/verifying.zip
打包好的 .zip 只包含权威的 SKILL.md 及其他资源——中文参考版 SKILL.zh.md 不会被打进 zip。
.zip两条分发路径:
releases/<skill-name>.zip 拷出来,微信 / 邮件 / Slack 直接发给任何人.zip 作为附件挂上去,用户在 Releases 页一键下载releases/ 文件夹本身在 .gitignore 里——repo 历史不存二进制产物。每次发版的 .zip 走 GitHub Release 的 attachment 路径。
skills/ 下新建一个文件夹,名字 = skill name(小写 + 短横线)SKILL.md(英文权威版),写好 frontmatterSKILL.zh.md 中文参考——修改时两份同步CHANGELOG.md 记录本次新增Claude Code skill for YouTube creators — channel audits, video SEO, retention scripts, thumbnails, content strategy, Sho
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