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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 Buffett-style value investing checklist on one or more listed companies, with parallel data collection, a six-gate review and star scoring.
SKILL.md written in Chinese; this summary is our English description.
Install Value Investing Pre-Buy Checklist
$ npx skills add xbtlin/ai-berkshire --skill investment-checklist -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire investment-checklist --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-checklist .claude/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .claude/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklistType 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-checklist -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire investment-checklist --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-checklist .agents/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .agents/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklist -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire investment-checklist --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-checklist .cursor/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .cursor/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklist--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-checklist -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire investment-checklist --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-checklist .gemini/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .gemini/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklistInstalls 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-checklist -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-checklist .github/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .github/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklist -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-checklist --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-checklist .opencode/skills/investment-checklist && 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-checklist" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/investment-checklist into .opencode/skills/investment-checklist/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "investment-checklist", 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-checklist- GitHub stars
- 17k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 411 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
Runs a Buffett-style value investing checklist on one or more listed companies, with parallel data collection, a six-gate review and star scoring.
- Works in 8 steps: 盈利能力：ROE（5-10年趋势）、毛利率、净利率、自由现金流 → 估值数据：当前股价、市值、PE(TTM)、前瞻PE、PB、股息率 → 增长趋势：近3年收入/利润增速 → …
- Screening a company against a structured value investing checklist before buying
- SKILL.md covers Codex adapter note, 执行流程, 输出格式要求 and 关键原则
- Calls python3
What it does
Written mostly in Chinese, this skill runs a Buffett-style value investing checklist on one or more companies, given as names or tickers. It first resolves each company's full name, ticker and exchange and sets aside unlisted companies with a short note. It then rates how much information is available for each as A, B or C, so that thin data is reported as a gray zone needing first-hand information rather than treated as a failed check.
Data collection starts one background agent per company in parallel, covering profitability, valuation, growth, financial health, competition, moat evidence, management record and recent events. Each listed company then goes through six gates, beginning with whether the business sits within your circle of competence, with items scored from one to five stars. The adapter note says to confirm today's date first and state the data cutoff in the report, cross-check financial figures and use exact arithmetic tools for valuation math.
When your agent uses it
- Screening a company against a structured value investing checklist before buying
- Comparing several listed companies on the same six-gate review
- Checking whether a company sits inside your circle of competence
Example prompts
- “Run the value investing checklist on NVDA, AAPL and MSFT.”
- “Check Tencent and Moutai against the pre-buy checklist.”
- “Evaluate this company with the checklist and tell me where the data is too thin to judge.”
Requirements
- Web search and financial data access for the data collection step
- Python 3 to run the repository's tools/financial_rigor.py
Workflow steps
8 steps, taken from the first numbered list in SKILL.md.
- 盈利能力：ROE（5-10年趋势）、毛利率、净利率、自由现金流
- 估值数据：当前股价、市值、PE(TTM)、前瞻PE、PB、股息率
- 增长趋势：近3年收入/利润增速
- 财务健康：负债水平、资本开支需求、现金储备、净现金/净负债
- 竞争格局：市场份额、主要竞争对手、份额变化趋势
- 护城河证据：品牌/转换成本/网络效应/规模效应/技术壁垒的具体证据
- 管理层记录：CEO履历、关键决策、持股、资本配置记录
- 最新动态：近6个月重大事件（业绩、并购、监管、管理层变动等）
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
Value Investing Pre-Buy Checklist loads about 1.5k tokens when it runs. Until then it costs about 26 tokens; SKILL.md has 411 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). 411 words, ~1,478 tokens.
Download SKILL.mdSave it as
.claude/skills/investment-checklist/SKILL.md (or your agent's skills folder).- name
- investment-checklist
- description
- AI Berkshire skill: 巴菲特价值投资买入前 Checklist. Source: skills/investment-checklist.md.
Codex adapter note
This skill is generated from skills/investment-checklist.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.
巴菲特价值投资买入前 Checklist
对 $ARGUMENTS 执行巴菲特价值投资买入前 Checklist 分析。
支持输入格式：单个或多个公司，用逗号/顿号/空格分隔。例如：腾讯, 茅台, 英伟达 或 NVDA AAPL MSFT
执行流程
第一步：解析输入，识别所有待分析公司
从 $ARGUMENTS 中解析出所有公司名称/代码。对每家公司确定：
- 公司全称、股票代码、上市交易所
- 如果公司未上市，标记为"未上市"并给出简要说明（是否有间接投资途径），跳过完整Checklist
第一步半：AI研究偏见预警
对每家公司进行"信息丰富度"快速评级（A/B/C），并在报告中标注：
| 等级 | 判断标准 | 对Checklist的影响 |
|---|---|---|
| A级 | 上市多年、数据充裕 | 正常执行，但警惕"共识陷阱"——所有指标看起来都清晰不代表真的确定 |
| B级 | 数据有限需推算 | 每个推算指标标注置信度，"好生意"判断加权考虑数据可靠性 |
| C级 | 信息极度稀缺 | 不勉强填满六关表格，诚实标注"数据不足无法判断"，聚焦可验证的核心问题 |
核心原则：Checklist的目标是排除坏选择。对于C级公司，"数据不足"不等于"不通过"，也不等于"通过"——应诚实标注为"灰色地带，需补充一手信息"，而不是因为AI无法填满表格就判为否决。
段永平说过："看不懂"有两种——一种是生意太复杂真的看不懂，一种是你还没花时间去看。AI研究的局限是容易把"资料少"和"看不懂"混为一谈。
第二步：并行数据收集
使用 Task 工具为每家公司启动独立的后台 Agent 进行数据收集（所有公司同时并行启动），每个Agent负责收集：
- 盈利能力：ROE（5-10年趋势）、毛利率、净利率、自由现金流
- 估值数据：当前股价、市值、PE(TTM)、前瞻PE、PB、股息率
- 增长趋势：近3年收入/利润增速
- 财务健康：负债水平、资本开支需求、现金储备、净现金/净负债
- 竞争格局：市场份额、主要竞争对手、份额变化趋势
- 护城河证据：品牌/转换成本/网络效应/规模效应/技术壁垒的具体证据
- 管理层记录：CEO履历、关键决策、持股、资本配置记录
- 最新动态：近6个月重大事件（业绩、并购、监管、管理层变动等）
第三步：逐公司执行六关 Checklist
对每家已上市公司，依次过六关：
第一关：我能理解这门生意吗（能力圈）
必须回答：
- 能用一句话说清楚这家公司靠什么赚钱吗？
- 10年后大概率还在做什么生意？
- 哪些关键变量决定成败？
- 对这个行业的认知是来自深度研究还是道听途说？
评分标准（★1-5）：
- ★★★★★：商业模式极其简单清晰，10年确定性高（如茅台：酿酒卖酒）
- ★★★★☆：模式清晰但有技术门槛，需要一定专业知识理解
- ★★★☆☆：模式可理解但10年确定性不高，行业变化快
- ★★☆☆☆：业务线复杂或行业剧变中，难以预判未来
- ★☆☆☆☆：完全不在能力圈内
硬性否决：如果连赚钱方式都说不清，直接标记为"不在能力圈，不做分析"。
Show full SKILL.md (161 more words)Show less
第二关：这是一门好生意吗（经济特征）
用数据说话，关键指标必须通过工具精确计算：
python3 tools/financial_rigor.py verify-valuation \
--price {股价} --eps {EPS} --bvps {每股净资产} --fcf-per-share {每股FCF} --dividend {每股股息}| 指标 | 该公司数值 | 参考标准 | 判断 |
|---|---|---|---|
| ROE（5年均值） | >15%优秀, >20%卓越 | ||
| 毛利率 | >40%暗示定价权 | ||
| 自由现金流 | 持续为正、≈净利润 | ||
| 资本开支强度 | 轻资产优于重资产 | ||
| 负债水平 | 有息负债/净利润<3年 |
评分标准（★1-5）：
- ★★★★★：ROE>25%、高毛利、强FCF、轻资产、低负债（全部达标）
- ★★★★☆：4项达标
- ★★★☆☆：3项达标
- ★★☆☆☆：2项达标或趋势恶化
- ★☆☆☆☆：多数不达标或FCF持续为负
第三关：护城河够不够深（竞争优势）
逐项检查：
| 护城河类型 | 是否具备 | 具体证据 | 变宽还是变窄？ |
|---|---|---|---|
| 品牌/定价权 | |||
| 转换成本 | |||
| 网络效应 | |||
| 成本/规模优势 | |||
| 技术/专利壁垒 |
追加检验：如果给竞争对手100亿，能否复制这门生意？
评分标准（★1-5）：
- ★★★★★：多重护城河叠加且在变宽
- ★★★★☆：至少一条强护城河且稳定
- ★★★☆☆：有护城河但不够深，或趋势不明
- ★★☆☆☆：护城河正在被侵蚀
- ★☆☆☆☆：无明显护城河
第四关：管理层是否值得信任（人的因素）
| 检查项 | 评估 |
|---|---|
| 诚实度（承诺vs交付） | |
| 资本配置能力（回购/分红/并购记录） | |
| 股东利益导向（持股、薪酬） | |
| 所有者心态（创始人 vs 职业经理人） | |
| 公司治理（关联交易、商誉、审计） | |
| CEO离开后能否照常运转？ |
评分标准（★1-5）：
- ★★★★★：创始人掌舵、资本配置卓越、利益完全一致
- ★★★★☆：管理层优秀但有小瑕疵
- ★★★☆☆：管理层合格但有治理隐患
- ★★☆☆☆：有诚信或治理问题
- ★☆☆☆☆：严重诚信问题（→硬性否决）
第五关：价格是否足够便宜（安全边际）
| 指标 | 数值 | 历史分位 | 判断 |
|---|---|---|---|
| PE (TTM) | |||
| 前瞻PE | |||
| PB | |||
| 股息率 | |||
| FCF Yield |
追加检验（必须通过工具精确计算，禁止心算）：
python3 tools/financial_rigor.py three-scenario \
--price {股价} --eps {EPS} --shares {股本亿} \
--growth {乐观} {中性} {悲观} --pe {乐观PE} {中性PE} {悲观PE} --currency {币种}- 三情景下的估值区间（取工具输出结果）
- 如果判断有误，在当前价格买入最多亏多少？
- 股价腰斩你敢加仓吗？
评分标准（★1-5）：
- ★★★★★：相对内在价值打5折以下，极端安全边际
- ★★★★☆：打7折，有良好安全边际
- ★★★☆☆：合理估值，安全边际一般
- ★★☆☆☆：偏贵，安全边际不足
- ★☆☆☆☆：严重高估
第六关：仓位与决策纪律（防止情绪失控）
检查以下情绪信号：
- 是否因为FOMO想买？
- 是否因为别人推荐才想买？
- 如果停牌5年你能接受吗？
- 买入论述能否用200字以内写清楚？
第四步：镜子测试
对每家公司写出镜子测试语句：
"我以 ___元 买入 ___公司，因为：
- 这门生意的本质是___，我理解它；
- 它的护城河是___，而且在变宽/变窄；
- 管理层___，值得/不值得信赖；
- 当前价格相当于内在价值的___折，有/无足够安全边际；
- 即使我错了，下行风险可控/不可控，因为___。"
5句话说不完整 = 不买。 明确标注"通过"或"未通过"。
第五步：快速否决清单
对每家公司逐条检查，触发任何一条直接标注为"否决"：
- 说不清楚这家公司怎么赚钱
- 连续3年自由现金流为负且看不到改善
- 管理层有诚信污点
- 竞争优势正在被不可逆侵蚀
- 需要靠"下一个接盘者出更高价"来赚钱（博傻）
- 无法承受这笔投资归零的后果
- 买入理由主要是"别人都在买"或"最近涨得好"
- 无法用200字以内写清楚买入理由
第六步：输出总览对比表（多公司时必须输出）
当分析多家公司时，必须生成对比总览表：
| 公司 | Checklist通过？ | 能力圈 | 好生意 | 护城河 | 管理层 | 安全边际 | 核心结论 |
|---|---|---|---|---|---|---|---|
| ★☆☆☆☆ | ★☆☆☆☆ | ★☆☆☆☆ | ★☆☆☆☆ | ★☆☆☆☆ |
第七步：最终结论与写入文件
对每家公司给出明确结论（不回避）：
- ✅ 通过 Checklist（X/6关）— 可以进入深度研究阶段
- ❌ 未通过 Checklist — 说明哪条红线被触发
- ❓ 灰色地带 — 说明关键争议点是什么，投资者需要自行判断什么
- N/A — 未上市/无法买入
将完整报告写入 ~/巴菲特Checklist-[公司名或"多公司对比"].md
输出格式要求
- 每家公司独立成章，包含：六关评分表 + 核心数据表 + 关键风险（3-5条）+ 镜子测试 + 明确结论
- 多公司时在最后附总览对比表
- 所有评分必须使用★符号（★1-5），不含半星
- 数据必须标注来源时间，估计值必须注明"估计"
- 文末附结语，呼应巴菲特名言："投资的第一条规则是不要亏损"
- 语言风格：直接、犀利、不说废话。用巴菲特/芒格/段永平的语录穿插点评
关键原则
- 宁可错过，不可做错：Checklist的目标是排除坏选择，不是找到最好的
- 诚实面对能力圈：看不懂就说看不懂，不要勉强分析
- 安全边际是生命线：好公司买贵了也会亏钱
- 镜子测试不可跳过：说不清楚理由就不买，没有例外
© 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/investment-checklist of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Compare with similar skills
Value Investing Pre-Buy Checklist 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 |
|---|---|---|---|---|---|---|
| Value Investing Pre-Buy Checklist this skillxbtlin/ai-berkshire | 17k | — | ~1.5k | 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 Value Investing Pre-Buy Checklist
What does Value Investing Pre-Buy Checklist do?
Runs a Buffett-style value investing checklist on one or more listed companies, with parallel data collection, a six-gate review and star scoring. Written mostly in Chinese, this skill runs a Buffett-style value investing checklist on one or more companies, given as names or tickers. It first resolves each company's full name, ticker and exchange and sets aside unlisted companies with a short note.
When should I use Value Investing Pre-Buy Checklist?
Value Investing Pre-Buy Checklist fits situations like: screening a company against a structured value investing checklist before buying; comparing several listed companies on the same six-gate review; checking whether a company sits inside your circle of competence.
How do I install Value Investing Pre-Buy Checklist in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill investment-checklist -a claude-code`. Or copy the skill folder (codex-skills/investment-checklist in xbtlin/ai-berkshire) into .claude/skills/investment-checklist in your project. Claude Code loads it when a task matches its description.
How do I install Value Investing Pre-Buy Checklist in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill investment-checklist -a codex`. Or copy the skill folder (codex-skills/investment-checklist in xbtlin/ai-berkshire) into .agents/skills/investment-checklist in your project. Codex loads it when a task matches its description.
Can I use Value Investing Pre-Buy Checklist 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-checklist -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-checklist, .gemini/skills/investment-checklist, .github/skills/investment-checklist and .opencode/skills/investment-checklist in your project.
What does Value Investing Pre-Buy Checklist need to run?
Going by SKILL.md and its folder, Value Investing Pre-Buy Checklist needs the command-line tools its instructions call (python3). Our summary lists: Web search and financial data access for the data collection step; Python 3 to run the repository's tools/financial_rigor.py.
Does Value Investing Pre-Buy Checklist 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 Value Investing Pre-Buy Checklist 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 Value Investing Pre-Buy Checklist use?
Value Investing Pre-Buy Checklist 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 Value Investing Pre-Buy Checklist use?
About 1.5k tokens (SKILL.md is roughly 5.9k 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 Value Investing Pre-Buy Checklist?
Skills that share tags, products or a category with Value Investing Pre-Buy Checklist: 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 Value Investing Pre-Buy Checklist?
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.