Longbridge Earnings
helsome/folio
Earnings analysis — pre- and post-earnings. An agent skill from helsome/folio.
Researches a company's leadership from public sources: statements versus results, capital allocation decisions, governance and pay, and feedback from outside the firm.
SKILL.md written in Chinese; this summary is our English description.
Install Management Deep Dive
$ npx skills add xbtlin/ai-berkshire --skill management-deep-dive -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire management-deep-dive --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/management-deep-dive .claude/skills/management-deep-dive && 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 "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .claude/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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/management-deep-diveType 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 management-deep-dive -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire management-deep-dive --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/management-deep-dive .agents/skills/management-deep-dive && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .agents/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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 management-deep-dive -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire management-deep-dive --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/management-deep-dive .cursor/skills/management-deep-dive && 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 "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .cursor/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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/management-deep-dive--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 management-deep-dive -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire management-deep-dive --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/management-deep-dive .gemini/skills/management-deep-dive && 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 "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .gemini/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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 management-deep-diveInstalls 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 management-deep-dive -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/management-deep-dive .github/skills/management-deep-dive && 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 "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .github/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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 management-deep-dive -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 management-deep-dive --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/management-deep-dive .opencode/skills/management-deep-dive && 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 "management-deep-dive" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/management-deep-dive into .opencode/skills/management-deep-dive/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "management-deep-dive", 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
management-deep-dive- GitHub stars
- 17k
- Token cost
- ~1.7k tokens
- SKILL.md length
- 478 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
Researches a company's leadership from public sources: statements versus results, capital allocation decisions, governance and pay, and feedback from outside the firm.
- Works in 4 steps: Agent 1：CEO公开发言与预测记录（股东信、电话会、采访、社交媒体） → Agent 2：资本配置决策记录（并购、回购、分红、新业务投资） → Agent 3：治理结构与薪酬（股权结构、关联交易、高管薪酬） → …
- Judging whether a company's leadership does what it says
- SKILL.md covers Codex adapter note, 设计理念, 执行流程 and 关键原则
- Calls python3
What it does
This skill deepens the management step of a standard investment research workflow. It is meant for cases where the management rating comes out uncertain, or where leadership is the core of the investment case. The input is a company name, or a person's name followed by the company, for example Wang Xing at Meituan. Its premise is that buying a stock means buying the people running it, and that an AI can test whether words match actions using public information.
It first identifies the key people, such as the CEO or chair, CFO, a departed founder and the controlling shareholder. Four background agents then run in parallel: one gathers the CEO's public statements and forecasts, one records capital allocation decisions, one reviews ownership, related-party dealings and pay, and one collects outside feedback from employees, customers and the industry. The CEO assessment then sets past judgments against actual outcomes over five years.
When your agent uses it
- Judging whether a company's leadership does what it says
- Following up a research report where the management rating is uncertain
- Reviewing a capital allocation record of acquisitions, buybacks and dividends
- Checking governance, related-party deals and executive pay
Example prompts
- “Do a management deep dive on Meituan.”
- “Assess Jensen Huang's record as CEO of Nvidia from public statements and capital allocation decisions.”
- “Compare what the CEO promised in recent shareholder letters with what the company delivered.”
Requirements
- Web search access for the data-gathering agents
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- Agent 1：CEO公开发言与预测记录（股东信、电话会、采访、社交媒体）
- Agent 2：资本配置决策记录（并购、回购、分红、新业务投资）
- Agent 3：治理结构与薪酬（股权结构、关联交易、高管薪酬）
- Agent 4：侧面验证信息（员工评价、客户反馈、行业口碑）
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
Management Deep Dive loads about 1.7k tokens when it runs. Until then it costs about 24 tokens; SKILL.md has 478 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). 478 words, ~1,651 tokens.
Download SKILL.mdSave it as
.claude/skills/management-deep-dive/SKILL.md (or your agent's skills folder).- name
- management-deep-dive
- description
- AI Berkshire skill: 管理层纵深研究：买股票就是买人. Source: skills/management-deep-dive.md.
Codex adapter note
This skill is generated from skills/management-deep-dive.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.
管理层纵深研究：买股票就是买人
对 $ARGUMENTS 进行管理层深度研究。
支持输入格式：公司名 或 人名 公司名，例如：美团、王兴 美团、黄仁勋 英伟达
"买股票就是买人。找到你信任的人，然后长期持有。" —— 段永平
"评估管理层，要看他们在没人看着的时候做什么。" —— 巴菲特
设计理念
大多数投资分析对管理层的评估停留在表面：履历、持股比例、薪酬。但巴菲特花大量时间和管理层吃饭聊天，李录说他投资的本质是投人，段永平说买股票就是买人。
本Skill是 /investment-research 第五步管理层评估的深化版。当标准投资研究中管理层评分不确定（★★★或以下）、或管理层是核心投资逻辑时，使用本Skill做纵深研究。
AI无法和管理层吃饭，但可以通过公开信息做到：
- 追踪管理层的话与做是否一致（承诺vs兑现）
- 分析每一笔重大资本配置决策的回报
- 从困难时期的决策中推断品格
- 通过员工/商家/客户的反馈侧面验证
执行流程
第一步：识别关键管理层并启动并行数据收集
使用 WebSearch 确认以下关键人物：
| 角色 | 姓名 | 任期 | 背景 | 持股/期权 |
|---|---|---|---|---|
| CEO/董事长 | ||||
| CFO | ||||
| 创始人（如不在位） | ||||
| 实际控制人（如不同于CEO） | ||||
| 其他关键高管 |
注意：区分"谁在做决策"和"谁的名字在头衔上"。有些公司创始人虽然卸任但仍是灵魂人物（如黄峥之于拼多多）。
确认关键人物后，使用 Task 工具启动多个后台 Agent 并行收集以下数据：
- Agent 1：CEO公开发言与预测记录（股东信、电话会、采访、社交媒体）
- Agent 2：资本配置决策记录（并购、回购、分红、新业务投资）
- Agent 3：治理结构与薪酬（股权结构、关联交易、高管薪酬）
- Agent 4：侧面验证信息（员工评价、客户反馈、行业口碑）
第二步：CEO能力圈评估
2.1 战略眼光
搜索CEO过去5年的公开发言（股东信、电话会、采访、社交媒体），提取其对以下问题的判断：
| 时间 | CEO的判断/预测 | 实际结果 | 准确度 |
|---|---|---|---|
| "我们认为X市场会..." | X市场实际... | ✅/❌ | |
| "未来3年我们的重点是..." | 实际执行... | ✅/❌ |
关键问题：
- CEO有没有做过超前于市场的正确判断？
- CEO有没有在大家都看好的时候保持冷静？
- CEO对行业趋势的理解是跟随市场还是独立思考？
2.2 执行能力
| 维度 | 评估 | 证据 |
|---|---|---|
| 战略到落地 | 说了的事做到了吗？ | |
| 组织能力 | 能不能吸引和留住人才？ | |
| 危机处理 | 遇到困难时怎么应对？ | |
| 迭代速度 | 犯错后纠正的速度快吗？ |
第三步：诚信度评估（最重要）
巴菲特："我们寻找三种品质：正直、智慧和精力。如果没有第一种，后两种会害死你。"
3.1 承诺vs兑现追踪
从过去3年的财报电话会、股东信、公开采访中，提取管理层做过的具体承诺：
| # | 时间 | 承诺内容 | 承诺场合 | 兑现情况 | 评价 |
|---|---|---|---|---|---|
| 1 | "我们将在2025年实现X业务盈利" | 2024年报电话会 | ✅/⚠️/❌ | ||
| 2 | "我们计划回购$X亿" | 2024年股东信 | ✅/⚠️/❌ |
兑现率统计：
| 承诺兑现率 | 评价 |
|---|---|
| >80% | 优秀——说到做到 |
| 60-80% | 合格——大方向对但执行有偏差 |
| 40-60% | 令人担忧——承诺过多交付不足 |
| <40% | 严重问题——不可信赖 |
Show full SKILL.md (190 more words)Show less
3.2 困难时期的表现
搜索公司历史上遭遇的重大危机/困难（股价暴跌、业绩miss、监管冲击、竞争加剧），分析管理层的应对：
| 危机事件 | 时间 | 管理层反应 | 事后回看的评价 |
|---|
关注：
- 是主动沟通还是躲避？
- 是归因内部还是甩锅外部？
- 是趁机做困难但正确的事，还是选择短期讨好市场？
3.3 对利益相关方的态度
| 利益相关方 | 管理层态度 | 证据 | 评价 |
|---|---|---|---|
| 股东 | 尊重/忽视/利用 | ||
| 员工 | 善待/压榨/漠视 | ||
| 客户/用户 | 以客户为中心/短期榨取 | ||
| 商家/供应商 | 公平合作/极端压价 | ||
| 监管/社会 | 合规配合/打擦边球 |
李录："对利益相关方的态度决定了企业的长期生命力。短期压榨能提升效率，但长期会损害生态。"
第四步：资本配置能力
这是巴菲特最看重的管理层能力——每赚一块钱，管理层能把它变成多少钱？
4.1 资本配置决策记录
搜索公司过去5年的重大资本配置决策，逐笔评估：
并购记录：
| 时间 | 收购标的 | 金额 | 战略逻辑 | 事后回报 | 评分(1-5) |
|---|
回购记录：
使用 tools/financial_rigor.py verify-valuation 校验回购时和当前的PE等估值指标。
| 时间 | 回购金额 | 平均回购价 | 当时PE | 事后回看 | 评分(1-5) |
|---|
分红记录：
| 年份 | 分红金额 | 分红率 | 同期FCF | 是否可持续 |
|---|
新业务投资：
| 时间 | 投资领域 | 累计投入 | 当前状态 | 回报评估 | 评分(1-5) |
|---|
4.2 资本配置评分
| 维度 | 评分(1-5) | 说明 |
|---|---|---|
| 并购纪律 | 是否在合理价格收购？收购后整合如何？ | |
| 回购时机 | 是否在低估时回购、高估时停止？ | |
| 分红合理性 | 分红率是否与FCF匹配？ | |
| 新业务投资 | 成功率如何？止损纪律如何？ | |
| 现金管理 | 现金储备是否合理？是否囤积过多？ | |
| 综合评分 |
巴菲特标准：理想的管理层在有好机会时果断投资，没有好机会时积极回购/分红，永远不做高价并购。
第五步：治理结构评估
5.1 股权结构
| 项目 | 详情 | 风险评估 |
|---|---|---|
| 是否有AB股/超级投票权？ | ||
| 创始人/实控人持股比例？ | ||
| 是否有VIE结构？ | ||
| 独立董事是否真正独立？ | ||
| 大股东近期增减持记录？ |
5.2 薪酬合理性
| 高管 | 年度总薪酬 | 占公司净利润比 | 与同行对比 | 是否合理 |
|---|
关注：激励结构是否与长期股东利益一致？还是鼓励短期行为？
5.3 关联交易
| 关联方 | 交易内容 | 金额 | 是否公允 | 风险评估 |
|---|
第六步：侧面验证
AI无法和管理层面对面交流，但可以通过公开渠道的侧面信息验证。注意：以下信息取决于公开可搜索的内容，可能不完整，标注信息来源和可得性。
6.1 员工视角
搜索 Glassdoor评分摘要、知乎讨论等可公开搜索的员工评价（脉脉等需登录的平台标注"用户可自行补充"）：
| 维度 | 评分趋势 | 关键反馈 |
|---|---|---|
| 企业文化 | ||
| 管理层评价 | ||
| 工作强度 | ||
| 薪酬满意度 | ||
| 发展前景 |
6.2 客户/商家视角
搜索App Store评分、消费者投诉、商家论坛：
| 维度 | 评分/趋势 | 关键反馈 |
|---|---|---|
| 产品满意度 | ||
| 客户服务 | ||
| 商家/供应商关系 |
6.3 行业口碑
搜索行业论坛、社交媒体，了解同行和业内人士对该管理层的评价。
第七步：CEO离开后的情景分析
巴菲特："好公司应该是傻瓜都能经营的——因为迟早会有傻瓜来经营。"
| 问题 | 回答 |
|---|---|
| 如果CEO明天离开，公司能正常运转吗？ | |
| 现有管理团队的深度如何？有没有明确的继任者？ | |
| 公司的竞争优势是依赖CEO个人，还是依赖组织/系统？ | |
| 历史上的管理层交接是否顺利？ |
第八步：输出管理层评估报告
报告结构
一、关键人物速览（表格）
二、诚信度评估
- 承诺兑现率
- 困难时期表现
- 对利益相关方态度
三、能力评估
- 战略眼光（预判准确度）
- 执行能力
- 资本配置记录
四、治理结构
- 股权结构风险
- 薪酬合理性
- 关联交易
五、侧面验证
- 员工视角
- 客户/商家视角
六、综合评分与结论综合评分
| 维度 | 权重 | 评分(1-5) | 加权 |
|---|---|---|---|
| 诚信度 | 35% | ||
| 战略与执行能力 | 25% | ||
| 资本配置能力 | 25% | ||
| 治理结构 | 15% | ||
| 综合评分 | 100% |
段永平的"买人"标准
回答以下三个问题：
- 这个人是否正直？（诚实、不占股东便宜）
- 这个人是否有能力？（战略眼光+执行力+资本配置）
- 你愿意把钱交给这个人管10年吗？
三个都是"是" = ★★★★★（5分） 前两个是"是" = ★★★★（4分） 只有第一个是"是" = ★★★（3分） 第一个不是"是" = ★（1分，不投）
第九步：保存报告
将报告写入 reports/{公司名}-management-{YYYYMMDD}.md，例如 reports/美团-management-20260409.md
关键原则
- 诚信是一票否决项 — 能力不足可以学习，品格有问题无法修复
- 看行为不看言辞 — 管理层说什么不重要，做了什么才重要
- 在困难中看真相 — 顺风时谁都是好CEO，逆风时才见真功夫
- 资本配置是终极考试 — 赚钱容易，把赚到的钱配置好难
- 不要爱上管理层 — 保持客观，即使是你欣赏的人也可能犯大错
© 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/management-deep-dive of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Compare with similar skills
Management Deep Dive 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 |
|---|---|---|---|---|---|---|
| Management Deep Dive this skillxbtlin/ai-berkshire | 17k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Longbridge Earningshelsome/folio | 270 | 1 repos | ~2.5k | Automated safety check: Pass | None | |
| Neodata Financial Searchinfometa/workbuddyskills | 346 | — | ~2.1k | Automated safety check: Notes | None | |
| Serenity Supply-Chain Researchmuxuuu/serenity-skill | 4.1k | — | ~1.9k | 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 |
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Questions about Management Deep Dive
What does Management Deep Dive do?
Researches a company's leadership from public sources: statements versus results, capital allocation decisions, governance and pay, and feedback from outside the firm. This skill deepens the management step of a standard investment research workflow. It is meant for cases where the management rating comes out uncertain, or where leadership is the core of the investment case.
When should I use Management Deep Dive?
Management Deep Dive fits situations like: judging whether a company's leadership does what it says; following up a research report where the management rating is uncertain; reviewing a capital allocation record of acquisitions, buybacks and dividends; checking governance, related-party deals and executive pay.
How do I install Management Deep Dive in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill management-deep-dive -a claude-code`. Or copy the skill folder (codex-skills/management-deep-dive in xbtlin/ai-berkshire) into .claude/skills/management-deep-dive in your project. Claude Code loads it when a task matches its description.
How do I install Management Deep Dive in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill management-deep-dive -a codex`. Or copy the skill folder (codex-skills/management-deep-dive in xbtlin/ai-berkshire) into .agents/skills/management-deep-dive in your project. Codex loads it when a task matches its description.
Can I use Management Deep Dive 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 management-deep-dive -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/management-deep-dive, .gemini/skills/management-deep-dive, .github/skills/management-deep-dive and .opencode/skills/management-deep-dive in your project.
What does Management Deep Dive need to run?
Going by SKILL.md and its folder, Management Deep Dive needs the command-line tools its instructions call (python3). Our summary lists: Web search access for the data-gathering agents.
Does Management Deep Dive 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 Management Deep Dive 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 Management Deep Dive use?
Management Deep Dive 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 Management Deep Dive use?
About 1.7k tokens (SKILL.md is roughly 6.6k 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 Management Deep Dive?
Skills that share tags, products or a category with Management Deep Dive: Longbridge Earnings (helsome/folio, 270 stars), Neodata Financial Search (infometa/workbuddyskills, 346 stars), Serenity Supply-Chain Research (muxuuu/serenity-skill, 4.1k stars) and AI-Trader Market Intel (HKUDS/AI-Trader, 23k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Management Deep Dive?
xbtlin (a GitHub user) maintains it in xbtlin/ai-berkshire, which has 16,664 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.