AI-Trader Market Intel
HKUDS/AI-Trader
Reads AI-Trader's read-only market snapshots, grouped financial news and events board through its market-intel endpoints, for context before trading or posting.
Runs a four-role parallel research team on a listed company, each role applying one investor's lens to business, financials, industry and risk before a lead agent combines the results.
SKILL.md written in Chinese; this summary is our English description.
Install Investment Research Team
$ npx skills add xbtlin/ai-berkshire --skill investment-team -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire investment-team --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/investment-team .claude/skills/investment-team && 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 "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .claude/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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/investment-teamType 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 investment-team -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire investment-team --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/investment-team .agents/skills/investment-team && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .agents/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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 investment-team -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire investment-team --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/investment-team .cursor/skills/investment-team && 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 "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .cursor/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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/investment-team--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 investment-team -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire investment-team --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/investment-team .gemini/skills/investment-team && 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 "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .gemini/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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 investment-teamInstalls 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 investment-team -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/investment-team .github/skills/investment-team && 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 "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .github/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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 investment-team -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 investment-team --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/investment-team .opencode/skills/investment-team && 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 "investment-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-team into .opencode/skills/investment-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-team", 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
investment-team- GitHub stars
- 17k
- Token cost
- ~1.8k tokens
- SKILL.md length
- 520 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
Runs a four-role parallel research team on a listed company, each role applying one investor's lens to business, financials, industry and risk before a lead agent combines the results.
- Works in 8 steps: 一句话结论 → 四维评分总表 → 核心数据速览 → …
- Running a structured multi-agent research pass on a listed company
- SKILL.md covers Codex adapter note, 执行流程 and 重要注意事项
- Calls python3
What it does
The skill sets up a team of agents that research a listed company in parallel. It first shows you the team layout and waits for confirmation, then runs four specialist roles under a lead agent that coordinates and writes the final report: a business analyst on business model and moat, a financial analyst on statements and valuation, an industry researcher on competitive landscape, and a risk assessor on risks and management quality. Each role is tied to one investor's viewpoint: Duan Yongping, Buffett, Munger and Li Lu.
Before the team starts, the skill grades how much public information exists on the company, from A for well covered to C for scarce, and changes the research approach to match: contrarian checks for A, confidence labels for B and a first-principles mode for C. It also requires web search permission to be confirmed first, because background agents cannot ask for it and would otherwise answer from training knowledge alone. An adapter note asks for today's date to be checked and a data cutoff stated in the report header.
When your agent uses it
- Running a structured multi-agent research pass on a listed company
- Comparing business quality, valuation, industry position and risk in one report
- Pressure-testing a well-covered stock with a deliberately contrarian view
- Researching a thinly covered company from first principles
Example prompts
- “Run the investment research team on Meituan.”
- “Do a four-role analysis of Tencent and give me one combined report.”
- “Research Nvidia with the multi-agent team and state the data cutoff date in the report header.”
Requirements
- Web search permission for background agents
- Python 3 and the repository's tools/ scripts
Workflow steps
8 steps, taken from the step headings in SKILL.md.
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
Investment Research Team loads about 1.8k tokens when it runs. Until then it costs about 22 tokens; SKILL.md has 520 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). 520 words, ~1,781 tokens.
Download SKILL.mdSave it as
.claude/skills/investment-team/SKILL.md (or your agent's skills folder).- name
- investment-team
- description
- AI Berkshire skill: 投研团队：四角色并行分析框架. Source: skills/investment-team.md.
Codex adapter note
This skill is generated from skills/investment-team.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.
投研团队：四角色并行分析框架
对 $ARGUMENTS 进行团队化投资研究分析。使用 Team 工具创建真正的多Agent并行研究团队。
执行流程
第一步：展示团队框架
向用户展示以下团队结构，确认后启动：
| 角色 | 职责 | 分析框架 |
|---|---|---|
| team-lead（你自己） | 统筹协调、汇总研判、输出最终报告 | 四大师综合框架 |
| business-analyst | 商业模式 & 护城河分析 | 段永平视角 |
| financial-analyst | 财务报表 & 估值分析 | 巴菲特视角 |
| industry-researcher | 行业格局 & 竞争态势 | 芒格视角 |
| risk-assessor | 风险评估 & 管理层研判 | 李录视角 |
第一步半：AI研究偏见评估
在创建团队前，先向用户展示该公司的"AI可研究性"评估：
信息丰富度评级（决定研究策略）：
| 等级 | 特征 | 研究策略调整 |
|---|---|---|
| A级（信息充裕） | 上市多年、券商覆盖广 | 团队重点放在反面检验和非共识视角，避免输出与市场一致的"正确的废话" |
| B级（信息适中） | 上市不久、覆盖有限 | 每个Agent的推算数据必须标注置信度，team-lead汇总时标注"数据充分度" |
| C级（信息稀缺） | 冷门/新上市/新兴市场 | 团队转为"第一性原理模式"：不追求报告完整性，聚焦商业本质的几个核心问题 |
关键提醒：资料多≠确定性高，资料少≠确定性低。AI能输出的置信度 ≠ 投资的真实确定性。确定性来自商业模式本身，不来自资料数量。
将评级结果告知每个Agent，影响其研究方式。
第一步¾：WebSearch 权限预检（关键 · 避免 Agent 静默退化）
在创建团队、启动任何后台 Agent 之前，必须先确认 WebSearch 权限已放行。
为什么必须预检：本 skill 用 run_in_background: true 启动 4 个后台子 Agent，而后台 Agent 无法向用户弹出交互式权限确认。若 WebSearch 未在 .claude/settings.local.json 的 permissions.allow 白名单中，子 Agent 的联网搜索会被静默拦截，导致其退化为仅凭训练知识（有知识截止日期）作答，却仍按框架输出一份"看起来完整、实则未联网"的伪研究——这是本 skill 最危险的失败模式（见 issue #58）。
预检步骤：
- 用 Bash 检查白名单是否含 WebSearch：
bash grep -l '"WebSearch"' .claude/settings.local.json ~/.claude/settings.local.json 2>/dev/null - 若两处都未命中（即未放行）→ 停下来，不要启动 Agent，提示用户：
⚠️ 检测到 WebSearch 未在权限白名单中。后台研究 Agent 无法联网，会退化成仅凭训练知识作答。请先在
.claude/settings.local.json的permissions.allow加入"WebSearch"（或运行/permissions勾选），再重跑本命令。 - 命中 → 正常继续。
第二步：创建团队
使用 TeamCreate 创建团队：
- team_name:
{公司名}-research（英文小写，如meituan-research） - agent_type:
team-lead
第三步：创建4个任务
使用 TaskCreate 创建以下4个任务（每个都要有 subject、description、activeForm）：
任务1：商业模式分析
- subject:
分析{公司名}商业模式、护城河与用户价值 - description 包含：
- 商业模式本质：核心生意定义、收入结构拆解
- 平台/产品飞轮效应如何运转
- 护城河分析：品牌/转换成本/网络效应/规模效应/技术壁垒，逐一验证
- 用户/客户价值：为各方创造了什么独特价值
- 业务矩阵与协同效应
- 段永平"好生意"标准评估：差异化、定价权、可持续竞争优势
- 要求搜索最新财报、行业报告等公开信息
Show full SKILL.md (205 more words)Show less
任务2：财务与估值分析
- subject:
分析{公司名}财务数据、盈利能力与估值 - description 包含：
- 近3-5年营收、净利润、经营利润趋势
- 盈利能力指标：ROE、ROA、毛利率、经营利润率
- 现金流分析：经营性现金流、自由现金流、资本开支
- 资产负债表健康度：现金储备、负债率、流动性
- 估值分析：PE/PS/PB/EV等，与历史及同业对比
- 安全边际评估：内在价值 vs 当前股价
- 金融严谨性验证（必须使用Bash调用工具，禁止心算）：
- 市值验算：
python3 tools/financial_rigor.py verify-market-cap --price {价格} --shares {股本} --reported {报告市值} --currency {币种} - 估值验算：
python3 tools/financial_rigor.py verify-valuation --price {价格} --eps {EPS} --bvps {每股净资产} - 关键数据交叉验证：
python3 tools/financial_rigor.py cross-validate --field {字段} --values '{JSON}' --unit {单位} - 三情景估值：
python3 tools/financial_rigor.py three-scenario --price {价格} --eps {EPS} --shares {股本亿} --growth {乐观} {中性} {悲观} --pe {乐观PE} {中性PE} {悲观PE} - 将工具输出结果直接嵌入报告中作为验证记录
- 市值验算：
任务3：行业与竞争分析
- subject:
分析{行业}行业格局与{公司名}竞争态势 - description 包含：
- 行业规模与增长：市场规模、增速、渗透率
- 竞争格局：主要对手市场份额、竞争策略对比
- 核心竞争者威胁评估：逐个分析主要竞争对手
- 各细分赛道格局
- 行业趋势：技术变革、政策影响、新进入者
- 产业链分析：上中下游价值分配
- 要求搜索最新行业数据和竞争动态
任务4：风险与管理层评估
- subject:
评估{公司名}投资风险与管理层质量 - description 包含：
- 管理层评估：CEO能力圈、诚信度、战略眼光、资本配置能力、历史决策质量
- 监管风险：当前及潜在监管影响
- 竞争风险：各竞争对手威胁程度评估
- 业务风险：新业务亏损、扩张不确定性
- 宏观风险：经济周期、行业周期影响
- 治理结构：股权结构、关联交易、股东回报政策
- 长期确定性：10年后公司会怎样？什么可能颠覆其商业模式？
- 要求搜索最新监管动态、管理层言论等
第四步：启动4个并行Agent
使用 Task 工具同时启动4个Agent（必须在同一条消息中并行调用）：
每个Agent的配置：
subagent_type:general-purposerun_in_background:trueteam_name: 对应团队名name: 对应角色名（business-analyst / financial-analyst / industry-researcher / risk-assessor）
每个Agent的prompt模板：
你是{公司名}投研团队中的"{角色中文名}"，负责从{大师名}投资视角分析{公司名}。
请完成任务 #{任务编号}：{任务subject}
具体要求：
{任务description的内容}
**研究方法**：
- 使用 WebSearch 搜索最新公开信息（财报、行业报告、新闻）
- **财务数据必须来自两个独立来源**，按 `skills/financial-data.md` 规范执行（美股：macrotrends+stockanalysis；港股：aastocks+macrotrends；A股：东方财富+巨潮资讯；台股：FinMind `tools/twstock_data.py`+Goodinfo），两源误差>1%须标记
- 确保数据准确，关键数据标注来源
- 分析要深入，不流于表面
- **联网失败禁止伪装**：若 WebSearch 被拦截/不可用，禁止用训练知识冒充联网结果。必须在报告顶部醒目标注「⚠️ 本报告未能联网，基于训练知识（截止日期 X），置信度降级」，并如实告知 team-lead，由其决定是否中止研究
**输出要求**：
- 报告要详尽，使用Markdown表格呈现关键数据
- 每个分析维度要有明确结论和评分
- 报告末尾要有该维度的总体结论
**完成后**：
1. 使用 TaskUpdate 将任务 #{任务编号} 标记为 completed
2. 通过 SendMessage 把完整分析报告发送给 team-lead（type: "message", recipient: "team-lead"）第五步：接收报告并跟踪进度
- 向用户实时展示进度表（哪些Agent已完成、哪些仍在研究中）
- 每收到一份报告，更新进度并展示该报告的核心要点（3-5条）
- 等待全部4份报告到齐
第六步：关闭团队成员
全部报告收到后，向4个Agent发送 shutdown_request（使用 SendMessage，type: "shutdown_request"）。
第七步：汇总最终报告
综合4份分析报告，输出以下结构的最终报告：
1. 一句话结论
用一段话（50-100字）概括是否值得投资及核心逻辑
2. 四维评分总表
| 维度 | 框架 | 评分(1-5星) | 核心判断 |
|---|
综合评分：X / 5
3. 核心数据速览
关键财务和经营指标表格（近2年对比）
4. 各维度分析摘要
每个维度摘取3-5条最重要的发现
5. 投资论点（Bull vs Bear）
- 🟢 看多逻辑（5-7条）
- 🔴 看空逻辑（5-7条）
6. 巴菲特买入前Checklist
| # | 检查项 | 通过? | 说明 | 10个核心检查项，逐一评估
7. 最终投资建议
- 定性判断表（生意质量/管理层/估值/时机）
- 分层操作建议表（激进型/稳健型/保守型 → 建议+价格区间）
- 关键催化剂（加仓信号/减仓信号各3-5条）
8. 总结段落
100-200字的最终总结
第八步：保存报告
将完整最终报告写入 ~/{公司名}投资研究报告_{日期}.md（日期格式 YYYYMMDD）。
第九步：数据抽检（准出流程）
# 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。
第十步：清理团队
使用 TeamDelete 清理团队资源。
重要注意事项
- 4个Agent必须并行启动——在同一条消息中调用4次Task工具
- Agent通过SendMessage汇报——不是文件协作，是消息通信
- 数据准确性——要求Agent使用WebSearch搜索最新数据，关键数据交叉验证
- 结论要明确——不回避给出买入/观望/回避建议和具体价格区间
- 所有分析必须有数据支撑——附数据来源
- 耐心等待——4个Agent研究需要几分钟，实时向用户更新进度
- 反偏见意识——team-lead在汇总时必须评估：各Agent的分析是否受限于资料充裕度？是否与市场共识过度趋同？最终报告需包含"信息丰富度评级"和"AI研究局限性声明"
- 信息稀缺时的诚实原则——宁可在报告中留白标注"数据不足"，也不要用推测填满框架伪装确定性
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Just SKILL.md in codex-skills/investment-team of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Compare with similar skills
Investment Research Team 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 |
|---|---|---|---|---|---|---|
| Investment Research Team this skillxbtlin/ai-berkshire | 17k | — | ~1.8k | Automated safety check: Pass | MIT | |
| AI-Trader Market IntelHKUDS/AI-Trader | 23k | — | ~1.1k | Automated safety check: Pass | None | |
| 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 | |
| Zhengxi Fund Manager Views Librarylyra81604/zhengxi-views | 1.8k | — | ~1.6k | Automated safety check: Pass | MIT | |
| SEC EDGAR Filings FetcherHKUDS/Vibe-Trading | 35k | — | ~1.4k | Automated safety check: Pass | MIT |
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Questions about Investment Research Team
What does Investment Research Team do?
Runs a four-role parallel research team on a listed company, each role applying one investor's lens to business, financials, industry and risk before a lead agent combines the results. The skill sets up a team of agents that research a listed company in parallel. It first shows you the team layout and waits for confirmation, then runs four specialist roles under a lead agent that coordinates and writes the final report: a business analyst on business model and moat, a financial analyst on statements and valuation, an industry researcher on competitive landscape, and a risk assessor on risks and management quality.
When should I use Investment Research Team?
Investment Research Team fits situations like: running a structured multi-agent research pass on a listed company; comparing business quality, valuation, industry position and risk in one report; pressure-testing a well-covered stock with a deliberately contrarian view; researching a thinly covered company from first principles.
How do I install Investment Research Team in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill investment-team -a claude-code`. Or copy the skill folder (codex-skills/investment-team in xbtlin/ai-berkshire) into .claude/skills/investment-team in your project. Claude Code loads it when a task matches its description.
How do I install Investment Research Team in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill investment-team -a codex`. Or copy the skill folder (codex-skills/investment-team in xbtlin/ai-berkshire) into .agents/skills/investment-team in your project. Codex loads it when a task matches its description.
Can I use Investment Research Team 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 investment-team -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/investment-team, .gemini/skills/investment-team, .github/skills/investment-team and .opencode/skills/investment-team in your project.
What does Investment Research Team need to run?
Going by SKILL.md and its folder, Investment Research Team needs the command-line tools its instructions call (python3). Our summary lists: Web search permission for background agents; Python 3 and the repository's tools/ scripts.
Does Investment Research Team 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 Investment Research Team 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 Investment Research Team use?
Investment Research Team 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 Investment Research Team use?
About 1.8k tokens (SKILL.md is roughly 7.1k 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 Investment Research Team?
Skills that share tags, products or a category with Investment Research Team: AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars), Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), Stock Deep Analysis Workflow (wbh604/UZI-Skill, 7.1k stars) and Zhengxi Fund Manager Views Library (lyra81604/zhengxi-views, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Investment Research Team?
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.