Eastmoney Market Data
HKUDS/Vibe-Trading
Index of Eastmoney's free, no-token market data interfaces for China A-shares and Hong Kong stocks: fund flows, dragon-tiger lists, margin trading, reports and news.
Runs a structured industry research workflow: tests an investment logic chain, maps the supply chain, scans listed companies and applies four investors' frameworks to segment leaders.
SKILL.md written in Chinese; this summary is our English description.
Install Industry Investment Research Framework
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire industry-research --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .claude/skills && cp -r skills-src/codex-skills/industry-research .claude/skills/industry-research && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .claude/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-researchType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire industry-research --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .agents/skills && cp -r skills-src/codex-skills/industry-research .agents/skills/industry-research && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .agents/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire industry-research --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/codex-skills/industry-research .cursor/skills/industry-research && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .cursor/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/xbtlin/ai-berkshire.git --path codex-skills/industry-research--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire industry-research --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/codex-skills/industry-research .gemini/skills/industry-research && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .gemini/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install xbtlin/ai-berkshire industry-researchInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .github/skills && cp -r skills-src/codex-skills/industry-research .github/skills/industry-research && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .github/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add xbtlin/ai-berkshire --skill industry-research -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install xbtlin/ai-berkshire industry-research --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/xbtlin/ai-berkshire.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/codex-skills/industry-research .opencode/skills/industry-research && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "industry-research" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/industry-research into .opencode/skills/industry-research/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "industry-research", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
industry-research- GitHub stars
- 17k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 441 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
Runs a structured industry research workflow: tests an investment logic chain, maps the supply chain, scans listed companies and applies four investors' frameworks to segment leaders.
- Works in 5 steps: 验证投资逻辑链的每一个环节 → 绘制完整产业链全景图 → 扫描全球所有上市公司（A股/港股/美股/国际） → …
- Researching an industry from an investment theme to listed companies
- SKILL.md covers Codex adapter note, 研究目标, 第一步：投资逻辑链构建与验证 and 第二步：产业链全景图绘制, plus 6 more sections
- Calls python3
What it does
The SKILL.md is mainly in Chinese and begins with a Codex adapter note: use the nearest Codex equivalent of Claude-only tools, run the date command first so the report states a data cutoff, use shared scripts such as financial_rigor.py from the repository's tools folder, cross-check financial data, use exact arithmetic for valuation, and label uncertainty and source gaps. The task then follows a set sequence for a named industry.
Step one draws the logic chain from an underlying trend to beneficiaries, questions each link and looks for real signed or landed business events rather than forecasts. Step two splits the industry into upstream, midstream and downstream, notes each link's business model, margin range, competition, barriers and cyclicality, and marks bottleneck links. Later steps scan listed companies in A-shares, Hong Kong, the US and elsewhere, apply four investors' frameworks to segment leaders and suggest an industry-level allocation. A section on AI bias covers mature-industry, emerging-industry and large-company effects. The excerpt is cut off there.
When your agent uses it
- Researching an industry from an investment theme to listed companies
- Mapping an industry's upstream, midstream and downstream links
- Testing each link of an investment thesis against real evidence
- Screening segment leaders across several stock markets
Example prompts
- “Research the power equipment industry chain and identify its bottleneck links.”
- “Build and test the investment logic chain for data center cooling demand.”
- “Map the lithium battery supply chain and list leading listed companies per segment.”
- “Apply the four-investor framework to the top company in each segment of this chain.”
Requirements
- Python 3 to run the repository's financial tools
- Web search for current industry data
Workflow steps
5 steps, taken from the first numbered list in SKILL.md.
- 验证投资逻辑链的每一个环节
- 绘制完整产业链全景图
- 扫描全球所有上市公司（A股/港股/美股/国际）
- 对每个细分环节的头部公司执行四大师框架分析
- 输出行业级投资组合配置建议
What it can do on your machine
Read from SKILL.md and the folder at commit a221a20. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Shell commands in SKILL.md call:
python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Industry Investment Research Framework loads about 1.4k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 441 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from xbtlin/ai-berkshire at commit a221a20, republished under its MIT licence (© xbtlin). 441 words, ~1,435 tokens.
Download SKILL.mdSave it as
.claude/skills/industry-research/SKILL.md (or your agent's skills folder).- name
- industry-research
- description
- AI Berkshire skill: 行业投资研究：产业链全景扫描 + 四大师个股分析框架. Source: skills/industry-research.md.
Codex adapter note
This skill is generated from skills/industry-research.md so Claude Code and Codex users share one canonical workflow.
- Treat
$ARGUMENTSas the user's request in the current Codex thread. - When the source mentions Claude-only surfaces such as Task, Agent, WebSearch, Bash, Read, or Write, use the closest Codex capability available in this session: subagents when available, web search when needed, shell commands for local tools, and normal file edits for workspace files.
- Use shared project tools from
tools/in this repository. Prefer running commands from the repository root with paths likepython3 tools/financial_rigor.py ...; if the current thread starts outside the repo, locate the actual checkout path first instead of assuming a fixed home-directory path. - Before starting research, run the
datecommand to confirm today's date; treat it as the baseline for "latest" data and state the data cutoff date in the report header. Never assume the current date from training data. - Preserve the research quality rules from
AGENTS.md: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.
行业投资研究：产业链全景扫描 + 四大师个股分析框架
对 $ARGUMENTS 行业进行系统化产业链投资研究。
研究目标
从一个投资主题/逻辑链出发，完成：
- 验证投资逻辑链的每一个环节
- 绘制完整产业链全景图
- 扫描全球所有上市公司（A股/港股/美股/国际）
- 对每个细分环节的头部公司执行四大师框架分析
- 输出行业级投资组合配置建议
第一步：投资逻辑链构建与验证
1.1 画出逻辑链
用箭头链路表达从"底层趋势"到"受益标的"的因果关系，例如：
底层趋势 A
→ 导致需求 B
→ 创造瓶颈/刚需 C
→ 受益产业链 D1.2 逐环节验证
对逻辑链的每个箭头提出质疑并寻找证据：
| 环节 | 核心假设 | 验证方式 | 数据来源 |
|---|---|---|---|
| A→B | 搜索行业数据/预测 | ||
| B→C | 搜索供需分析 | ||
| C→D | 搜索实际案例/签约 |
1.3 寻找"已发生的验证事件"
列出支撑该逻辑链的已签约/已落地的真实商业事件（而非预测），例如大公司的采购协议、政策文件、行业报告等。
第二步：产业链全景图绘制
2.1 绘制产业链结构
将行业拆解为上游→中游→下游→辅助环节，例如：
上游：原材料/资源开采 → 材料加工/提纯
中游：核心设备制造 → 系统集成/工程建设 → 新技术研发
下游：运营/服务 → 终端客户
辅助：检测/认证 → 维护服务 → 金融工具（ETF/信托）2.2 识别每个环节的"生意特征"
对每个环节标注：
| 环节 | 商业模式 | 毛利率区间 | 竞争格局 | 壁垒类型 | 周期性 |
|---|---|---|---|---|---|
| 卖资源/卖设备/卖服务/收租 | 垄断/寡头/充分竞争 | 资源/牌照/技术/规模 | 强/中/弱 |
2.3 标记"卡脖子环节"
识别产业链中供给最紧张、替代最难、利润率最高的环节——这些往往是最佳投资标的所在。
AI研究偏见自觉：行业研究的特殊陷阱
行业研究中，AI数据偏见会以独特方式放大：
行业级偏见：
| 偏见类型 | 表现 | 应对 |
|---|---|---|
| 成熟行业偏好 | 传统行业（银行/能源/消费）资料极多，AI分析看起来"更确定" | 确定性来自商业模式，不来自研报数量 |
| 新兴行业低估 | 新行业（AI应用/合成生物等）资料少，AI分析偏保守 | 用"终局思维"而非"当前数据"判断行业价值 |
| 龙头偏好 | 大公司资料远多于小公司，AI天然倾向推荐龙头 | 小公司可能有更好的风险回报比，不要因为AI分析篇幅短就忽略 |
| 上市偏好 | 只扫描上市公司会遗漏产业链中的关键未上市玩家 | 必须搜索未上市公司，标注"未来IPO候选" |
| 英文偏好 | AI对英文资料的处理能力更强，可能低估中国/亚洲市场玩家 | 必须同时搜索中英文信息源 |
产业链扫描中的反偏见措施：
- 对每个环节，不仅列出"AI容易找到的公司"，还要主动搜索"冷门但可能优质的标的"
- 对信息稀缺的小市值公司，不因分析篇幅短就降低推荐度——用核心问题（生意本质、护城河、管理层）而非报告长度来评判
- 在最终报告中标注每家公司的"信息充分度"（A/B/C级），让读者知道AI分析的可靠程度
第三步：全球上市公司扫描
使用 Task 工具启动后台 Agent，全面搜索该行业所有上市公司。
Show full SKILL.md (176 more words)Show less
搜索清单
- 美股（NYSE/NASDAQ/NYSE American）相关公司
- A股（上海/深圳）相关公司
- 港股相关公司
- 其他国际市场（日本/韩国/欧洲/澳大利亚等）
- 行业ETF
- 关键未上市公司（可能未来IPO）
对每家公司收集
- 公司名称（中英文）
- 股票代码和交易所
- 市值（近似）
- 一句话描述（在产业链中的位置和作用）
- 是否纯正标的（纯核电 vs 多元化中有核电业务）
- 产业链所属环节
输出格式
按产业链环节分类，每个环节一张表，包含所有扫描到的公司。 再按投资确定性分层：
- Tier 1：大市值、纯正标的、行业龙头
- Tier 2：中市值、纯正或高占比、细分龙头
- Tier 3：小市值、开发阶段、高风险高弹性
- Tier 4：多元化公司中有相关业务的大型企业
第四步：各环节头部公司四大师分析
对每个产业链环节的Tier 1和Tier 2公司，执行以下分析（Tier 3/4公司简要点评即可）：
4.1 生意本质（段永平）
- 一句话定义这家公司在产业链中做什么
- 收入结构与增速
- 毛利率/净利率水平及趋势
- 现金流特征
- 追问：这是一门好生意吗？为什么？
4.2 护城河（巴菲特）
用五类护城河评分（★1-5）：
| 护城河 | 强度 | 证据 |
|---|---|---|
| 品牌/定价权 | ||
| 转换成本 | ||
| 网络效应 | ||
| 规模效应 | ||
| 技术/牌照壁垒 |
追问：10年后护城河还在吗？
4.3 风险（芒格）
- 这家公司最可能怎么失败？
- 最坏情景下值多少钱？
- 聪明人为什么不买？
4.4 管理层（段永平+巴菲特）
- CEO/创始人是谁？关键决策记录
- 持股比例与利益对齐
- 简评（A/B/C级）
4.5 估值快照
- 当前PE/PS/EV/EBITDA
- 与同环节竞争对手对比
- 简评：贵了/合理/便宜
4.6 推荐度
用★1-5标注：
- ★★★★★ = 核心仓位候选
- ★★★★☆ = 卫星仓位候选
- ★★★☆☆ = 观察名单
- ★★☆☆☆ = 高风险期权
- ★☆☆☆☆ = 不推荐
第五步：行业级风险评估（芒格"检查清单"）
5.1 系统性风险清单
| 风险 | 概率 | 影响 | 应对策略 |
|---|---|---|---|
| 投资逻辑链的某个环节被证伪 | |||
| 替代技术出现 | |||
| 政策/监管黑天鹅 | |||
| 需求周期性回调 | |||
| 估值泡沫破裂 |
5.2 历史类比
找到历史上类似的产业链投资主题，分析其最终结局：
- 类比行业是什么？
- 最终赢家是谁？（上游/中游/下游？）
- 多数投资者赚钱了还是亏钱了？
- 对当前行业的启示是什么？
5.3 偏误自查
- 叙事偏差：故事是否太完美？
- 锚定效应：是否被近期涨幅锚定？
- 从众效应：是否因为"所有人都在买"？
第六步：文明趋势判断（李录框架）
- 这个行业所依托的底层趋势，是"文明级范式转移"还是"阶段性热潮"？
- 历史上最接近的技术革命类比是什么？
- 10-20年后，这个行业的终局是什么？
- 产业链中，哪个环节最可能出现"赢家通吃"？
- 哪个环节最可能被颠覆？
第七步：投资组合配置建议
7.1 推荐组合
按以下结构输出：
| 层级 | 仓位占比 | 标的 | 所属环节 | 核心逻辑 |
|---|---|---|---|---|
| 核心仓位 | 占主题仓位50-60% | 最确定、护城河最宽 | ||
| 卫星仓位 | 占主题仓位25-35% | 弹性较大、确定性稍低 | ||
| 期权仓位 | 占主题仓位5-15% | 高风险高回报，可以归零 | ||
| ETF替代 | 可替代以上全部 | 不想选股的"懒人方案" |
7.2 买入/卖出信号
| 信号类型 | 具体条件 |
|---|---|
| 加仓信号 | |
| 减仓信号 | |
| 清仓信号 |
7.3 主题仓位上限建议
根据投资逻辑链的确定性和风险程度，建议该主题占总仓位的上限百分比。
第八步：综合决策备忘录
行业总评表
| 维度 | 结论 | 信心度 |
|---|---|---|
| 投资逻辑链（验证程度） | ||
| 最佳环节（段永平"对的生意"） | ||
| 最宽护城河（巴菲特） | ||
| 最大风险（芒格） | ||
| 文明趋势定位（李录） | ||
| 整体估值水平 |
四位大师模拟点评
用引用格式，模拟四位大师对该行业投资机会的点评。
输出要求
- 所有分析必须有数据支撑，附数据来源
- 使用 Markdown 表格呈现关键数据
- 产业链全景图用代码块的文本图表示
- 每个环节至少分析2-3家头部公司
- 全球公司扫描要尽可能完整（A股/港股/美股/国际）
- 最终将完整报告写入
~/[行业名]产业链投资研究报告.md - 结论要明确，给出具体的标的、仓位和价格区间建议
- 每个分析模块末尾有对应大师的"追问"
数据抽检（准出流程）
报告写入后，执行数据抽检，通过方可发布：
# Step 1 — 提取抽检清单（15%随机抽样）
python3 tools/report_audit.py extract \
--report <报告文件路径>
# Step 2 — 对清单每项从可靠信源取数（参见 skills/financial-data.md）
# Step 3 — 输出准出/打回判决
python3 tools/report_audit.py verdict \
--results '<填好的JSON>' \
--report <报告文件名>【准出】 全部通过 → 报告可发布；【打回】 有不通过 → 修正后重审。
© xbtlin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in codex-skills/industry-research of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Compare with similar skills
Industry Investment Research Framework next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Industry Investment Research Framework this skillxbtlin/ai-berkshire | 17k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Eastmoney Market DataHKUDS/Vibe-Trading | 35k | — | ~1k | Automated safety check: Pass | MIT | |
| Stock Deep Analysis Workflowwbh604/UZI-Skill | 7.1k | — | ~9.1k | Automated safety check: Notes | MIT | |
| SEC EDGAR Filings FetcherHKUDS/Vibe-Trading | 35k | — | ~1.4k | Automated safety check: Pass | MIT | |
| A-Share Daily Reviewqusong0627/QuantMind | 1.7k | — | ~1.9k | Automated safety check: Pass | AGPL-3.0 | |
| Futu OpenAPI Market and Trading Assistantqusong0627/QuantMind | 1.7k | — | ~3.3k | Automated safety check: Notes | AGPL-3.0 |
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Questions about Industry Investment Research Framework
What does Industry Investment Research Framework do?
Runs a structured industry research workflow: tests an investment logic chain, maps the supply chain, scans listed companies and applies four investors' frameworks to segment leaders. py from the repository's tools folder, cross-check financial data, use exact arithmetic for valuation, and label uncertainty and source gaps. The task then follows a set sequence for a named industry.
When should I use Industry Investment Research Framework?
Industry Investment Research Framework fits situations like: researching an industry from an investment theme to listed companies; mapping an industry's upstream, midstream and downstream links; testing each link of an investment thesis against real evidence; screening segment leaders across several stock markets.
How do I install Industry Investment Research Framework in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill industry-research -a claude-code`. Or copy the skill folder (codex-skills/industry-research in xbtlin/ai-berkshire) into .claude/skills/industry-research in your project. Claude Code loads it when a task matches its description.
How do I install Industry Investment Research Framework in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill industry-research -a codex`. Or copy the skill folder (codex-skills/industry-research in xbtlin/ai-berkshire) into .agents/skills/industry-research in your project. Codex loads it when a task matches its description.
Can I use Industry Investment Research Framework in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xbtlin/ai-berkshire --skill industry-research -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/industry-research, .gemini/skills/industry-research, .github/skills/industry-research and .opencode/skills/industry-research in your project.
What does Industry Investment Research Framework need to run?
Going by SKILL.md and its folder, Industry Investment Research Framework needs the command-line tools its instructions call (python3). Our summary lists: Python 3 to run the repository's financial tools; Web search for current industry data.
Does Industry Investment Research Framework access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Industry Investment Research Framework safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Industry Investment Research Framework use?
Industry Investment Research Framework is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Industry Investment Research Framework use?
About 1.4k tokens (SKILL.md is roughly 5.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Industry Investment Research Framework?
Skills that share tags, products or a category with Industry Investment Research Framework: Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars), SEC EDGAR Filings Fetcher (HKUDS/Vibe-Trading, 35k stars) and A-Share Daily Review (qusong0627/QuantMind, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Industry Investment Research Framework?
xbtlin (a GitHub user) maintains it in xbtlin/ai-berkshire, which has 16,676 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 8, 2026.
Source: xbtlin/ai-berkshire on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.