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 four parallel analyst personas over one earnings report, then an editor and reader-review pass turn the findings into a publishable article.
SKILL.md written in Chinese; this summary is our English description.
$ npx skills add xbtlin/ai-berkshire --skill earnings-team -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-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/earnings-team .claude/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .claude/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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/earnings-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 earnings-team -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-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/earnings-team .agents/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .agents/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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 earnings-team -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-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/earnings-team .cursor/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .cursor/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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/earnings-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 earnings-team -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-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/earnings-team .gemini/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .gemini/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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 earnings-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 earnings-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/earnings-team .github/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .github/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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 earnings-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 earnings-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/earnings-team .opencode/skills/earnings-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 "earnings-team" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-team into .opencode/skills/earnings-team/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-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.
earnings-teamRuns four parallel analyst personas over one earnings report, then an editor and reader-review pass turn the findings into a publishable article.
This skill spawns background agents to gather primary materials first, the earnings release, the call transcript, and the prior period's figures for tracking commitments, and rates how complete that material is, which controls how deep each analyst can go into footnotes versus just core numbers.
Four personas each take one lens, business quality, financial-statement integrity, competitive shifts, and management red flags, and work in parallel; a team-lead role then synthesizes their findings into a draft, an editor rewrites it for general readers, and a reader-review persona checks whether it's actually clear before the team lead finalizes it. It also checks the current date first so a report's data cutoff is stated rather than assumed from training data.
2 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit a221a20. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Earnings Report Deep-Read Team loads about 2.3k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 561 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.
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.
The full file from xbtlin/ai-berkshire at commit a221a20, republished under its MIT licence (© xbtlin). 561 words, ~2,321 tokens.
.claude/skills/earnings-team/SKILL.md (or your agent's skills folder).This skill is generated from skills/earnings-team.md so Claude Code and Codex users share one canonical workflow.
$ARGUMENTS as the user's request in the current Codex thread.tools/ in this repository. Prefer running commands from the repository root with paths like python3 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.date command 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.AGENTS.md: cross-check financial data, use exact arithmetic tools for valuation/math, and clearly label uncertainty and source gaps.对 $ARGUMENTS 进行团队化财报精读分析。四位大师并行解读财报,编辑润色成文,读者评审把关质量,最终产出可直接发布的公众号文章。
支持输入格式:公司名 季度,例如:腾讯 2025Q4、PDD 2025年报、美团 最新
一份好的财报分析要解决两个问题:
本 Skill 的流程分三阶段:
使用 Agent 工具启动后台 Agent 并行获取以下原始材料:
| 资料类型 | 获取来源 | 优先级 |
|---|---|---|
| 财报原文 | 公司IR页面、SEC EDGAR(美股)、港交所披露易(港股)、巨潮资讯网(A股) | 最高 |
| 业绩电话会纪要 | Seeking Alpha、公司IR页面、雪球 | 最高 |
| 管理层致股东信 | 年报中提取 | 高(仅年报时) |
| 上一期财报/电话会 | 同上 | 高(用于承诺追踪) |
资料可得性评级:
| 等级 | 特征 | 影响 |
|---|---|---|
| A级 | 获取到完整原文 | 正常执行全部步骤 |
| B级 | 仅获取到部分原文或第三方汇总 | 标注"非原始来源",降低附注分析权重 |
| C级 | 仅有新闻报道和数据网站摘要 | 聚焦核心数据变化,跳过附注挖掘,标注"一手资料不足" |
将资料可得性评级告知每个 Agent,影响其分析深度。
| 阶段 | 角色 | 大师/定位 | 核心任务 |
|---|---|---|---|
| 研究 | Team Lead(你自己) | 总协调 | 统筹、合成、定稿 |
| 研究 | 生意本质解读者 | 段永平 | 这门生意变好了还是变差了? |
| 研究 | 财务质量审计师 | 巴菲特 | 赚的是真钱还是假钱? |
| 研究 | 竞争变化解读者 | 芒格 | 竞争格局在怎么变? |
| 研究 | 风险信号猎手 | 李录 | 管理层在隐瞒什么? |
| 发布 | 编辑 | 公众号写作 | 把研究报告改写成好文章 |
| 发布 | 读者评审 | 普通投资者 | 读者能看懂吗?有收获吗? |
使用 Agent 工具在同一条消息中启动4个后台 Agent。
核心问题:这份财报反映的生意本质,变好了还是变差了?
段永平:"投资就是买一门生意。看财报不是看数字,是看这门生意有没有变。"
分析内容:
收入结构拆解与解读
用户/客户价值变化
护城河检测
"好生意"标准评估
管理层产品直觉
输出要求:每个子项标注 🟢改善 / 🟡持平 / 🔴恶化,给出段永平式总结点评。
核心问题:这家公司赚的是真钱还是假钱?安全边际变了吗?
巴菲特:"我看每一份财报,第一件事就是翻到现金流量表。"
分析内容:
核心财务数据提取与验证
python3 tools/financial_rigor.py cross-validate \
--metric "revenue" --values {值1} {值2} --sources "来源1" "来源2"现金流分析(最重要)
利润质量检验
资产负债表健康度
估值与安全边际更新
python3 tools/financial_rigor.py verify-market-cap \
--price {价格} --shares {股本} --reported {报告市值} --currency {币种}
python3 tools/financial_rigor.py verify-valuation \
--price {价格} --eps {EPS} --bvps {每股净资产}
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}输出要求:所有计算附工具输出记录,利润质量信号灯 🟢/🟡/🔴,巴菲特式总结点评。
核心问题:这份财报揭示了竞争格局的什么变化?
芒格:"我想知道我会死在哪里,这样我就不去那儿了。"
分析内容:
从财报数据推断竞争变化
同期竞争对手对比
管理层对竞争的讨论
行业趋势信号
芒格式逆向思考
输出要求:竞争格局判断(加强/持平/恶化),竞争对手对比表,芒格式逆向点评。
核心问题:管理层在这份财报里隐瞒了什么?哪些信号在闪烁?
李录:"投资最重要的是避免永久性资本损失。"
分析内容:
管理层语气分析
承诺追踪
附注与隐藏信息
电话会Q&A精选
永久性资本损失风险
输出要求:管理层可信度评分★1-5,承诺兑现率,风险信号清单,李录式总结点评。
向用户实时展示:
📊 {公司名} {期间} 财报精读进度
━━━━━━━━━━━━━━━━━━━━━━━
阶段一·研究
☐ 段永平·生意本质 ⏳ 分析中...
☐ 巴菲特·财务质量 ⏳ 分析中...
☐ 芒格·竞争格局 ⏳ 分析中...
☐ 李录·风险信号 ⏳ 分析中...
阶段二·合成 ⏸ 等待中
阶段三·发布 ⏸ 等待中每收到一份报告,更新进度并展示核心发现(3-5条)。
全部4份研究报告到齐后,Team Lead 综合产出研究报告初稿。
合成要点——不是拼报告,是找交叉和矛盾:
# {公司名} {期间} 财报精读报告
**四大师并行解读 | {日期}**
## 一、一句话结论
> 50-100字:超/符/低预期,核心变化,对投资论文的影响。
## 二、本期最重要的3个变化
聚焦真正重要的变化,不罗列数据,每个变化100字以内。
## 三、四大师评分表
| 视角 | 大师 | 核心问题 | 结论 | 评分 | vs上期 |
|------|------|---------|------|------|--------|
## 四、核心数据速览
关键财务和运营指标表格(本期 vs 上期 vs 同比)
## 五、各视角深度分析
每个视角3-5条最重要发现
## 六、管理层语气与承诺追踪
承诺兑现表 + 语气变化分析
## 七、四大师会怎么做?
| 大师 | 如果持有 | 如果没持有 | 理由 |
## 八、结论
1. 超/符/低预期?
2. 投资论文影响:强化/无影响/削弱/破裂
3. 下一个催化剂
4. 操作建议研究报告完成后,并行启动两个 Agent:
定位:把硬核研究报告改写成公众号读者爱看、能看懂的文章。
核心原则:
具体任务:
标题与开头
结构优化
表达润色
读者价值检测
格式适配
输出:改写后的完整公众号文章。
定位:以一个"关注价值投资、有基础财务知识、持有/关注该公司"的普通投资者身份审读文章。
评审维度:
可读性(权重30%)
信息价值(权重30%)
可信度(权重20%)
行动指导性(权重20%)
输出格式:
## 读者评审报告
### 总体评分:X/10
### 优点(2-3条)
读者视角下文章做得好的地方
### 必须修改(硬伤)
- 问题1:具体描述 → 建议修改方式
- 问题2:...
### 建议优化(锦上添花)
- 建议1:...
- 建议2:...
### 读者最想知道但文章没回答的问题
- 问题1
- 问题2
### 一句话总评收到编辑改写稿和读者评审报告后:
reports/{公司名}/
├── {公司名}-earnings-{期间}.md ← 最终公众号文章(定稿)
├── {公司名}-earnings-{期间}-研究底稿.md ← 四大师合成研究报告(自用)
├── {公司名}-earnings-{期间}-段永平.md ← 生意本质解读
├── {公司名}-earnings-{期间}-巴菲特.md ← 财务质量审计
├── {公司名}-earnings-{期间}-芒格.md ← 竞争格局解读
├── {公司名}-earnings-{期间}-李录.md ← 风险信号分析
└── {公司名}-earnings-{期间}-读者评审.md ← 读者评审报告对最终文章执行抽检:
python3 tools/report_audit.py extract \
--report reports/{公司名}/{公司名}-earnings-{期间}.md
python3 tools/report_audit.py verdict \
--results '<填好的JSON>' \
--report {报告文件名}【准出】 全部通过 → 可发布;【打回】 有不通过 → 修正后重审。
| Skill | 定位 | 何时用 |
|---|---|---|
/earnings-review | 单Agent财报精读 | 快速过一遍,只需一个视角 |
/earnings-team(本Skill) | 六Agent团队精读 + 公众号发布 | 重要公司的关键财报,需要深度+发布 |
/investment-team | 四Agent全面公司研究 | 首次研究一家公司 |
© xbtlin, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in codex-skills/earnings-team of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Earnings Report Deep-Read 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 |
|---|---|---|---|---|---|---|
| Earnings Report Deep-Read Team this skillxbtlin/ai-berkshire | 17k | — | ~2.3k | 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 |
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.
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.
wbh604/UZI-Skill
Runs a staged deep analysis of a single stock on China A-share, Hong Kong and US markets, ending in an HTML report with valuation models and investor-panel scores.
lyra81604/zhengxi-views
Answers questions with sourced quotes from one Chinese fund manager's public writings, applies his stated investment method and compares his words with real fund holdings.
HKUDS/Vibe-Trading
Fetches U.S. SEC EDGAR data: resolves tickers to CIK numbers, lists recent 10-K, 10-Q and 8-K filings with document URLs, and pulls XBRL financial series.
helsome/folio
Earnings analysis — pre- and post-earnings. An agent skill from helsome/folio.
xbtlin/ai-berkshire
Scans a long-running industry trend for supply chain chokepoints, aiming to find second- and third-layer suppliers that the market has not yet priced in.
xbtlin/ai-berkshire
Plans and writes a three-to-eight-part long-form article series that breaks down one company, built on fact-checked financials, valuation and management analysis.
xbtlin/ai-berkshire
Reads a company's filings and earnings call material in depth, rates how complete the sources are and extracts the key figures into a structured review.
xbtlin/ai-berkshire
A four-step research framework for finding and tracking high-growth core companies in one industry: map the sector, ask core questions, verify, then hold to the turning point.
xbtlin/ai-berkshire
A research rule set for pulling company financials from prioritized sources by market and cross-checking every key figure against two independent sources.
xbtlin/ai-berkshire
Assesses whether a company's distributions are durable enough to earn a place in an income portfolio, starting from a ticker or company name.
Categories
Runs four parallel analyst personas over one earnings report, then an editor and reader-review pass turn the findings into a publishable article. This skill spawns background agents to gather primary materials first, the earnings release, the call transcript, and the prior period's figures for tracking commitments, and rates how complete that material is, which controls how deep each analyst can go into footnotes versus just core numbers.
Earnings Report Deep-Read Team fits situations like: analyzing a company's quarterly or annual earnings report; turning a dense earnings release into a readable article; tracking whether management kept its prior commitments.
Run `npx skills add xbtlin/ai-berkshire --skill earnings-team -a claude-code`. Or copy the skill folder (codex-skills/earnings-team in xbtlin/ai-berkshire) into .claude/skills/earnings-team in your project. Claude Code loads it when a task matches its description.
Run `npx skills add xbtlin/ai-berkshire --skill earnings-team -a codex`. Or copy the skill folder (codex-skills/earnings-team in xbtlin/ai-berkshire) into .agents/skills/earnings-team in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add xbtlin/ai-berkshire --skill earnings-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/earnings-team, .gemini/skills/earnings-team, .github/skills/earnings-team and .opencode/skills/earnings-team in your project.
Going by SKILL.md and its folder, Earnings Report Deep-Read Team needs the command-line tools its instructions call (python3).
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.
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.
Earnings Report Deep-Read Team is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.3k tokens (SKILL.md is roughly 9.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Earnings Report Deep-Read 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.
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.