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.
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.
SKILL.md written in Chinese; this summary is our English description.
Install Earnings Report Deep Reading
$ npx skills add xbtlin/ai-berkshire --skill earnings-review -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-review --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-review .claude/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .claude/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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-reviewType 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-review -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-review --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-review .agents/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .agents/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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-review -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-review --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-review .cursor/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .cursor/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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-review--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-review -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire earnings-review --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-review .gemini/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .gemini/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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-reviewInstalls 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-review -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-review .github/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .github/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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-review -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-review --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-review .opencode/skills/earnings-review && 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-review" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/earnings-review into .opencode/skills/earnings-review/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earnings-review", 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
earnings-review- GitHub stars
- 17k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 395 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
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.
- Works in 4 steps: 财报原文：从公司IR页面、SEC… → 业绩电话会纪要/录音：从 Seeking Alpha、公司IR页面、雪球等获取 → 管理层致股东信（如有年报）：完整阅读 → …
- Reading a company's latest quarterly or annual report in depth
- SKILL.md covers Codex adapter note, 设计理念, 执行流程 and 关键原则
- Calls python3
What it does
Written in Chinese and adapted from a Claude Code workflow for Codex, this skill analyzes one company's results from primary sources instead of news or broker research. You give a company and period, such as a quarter or an annual report, and it reads the latest one by default. The report states a data cutoff date taken from the `date` command and labels uncertainty and source gaps.
It first grades source availability as A, B or C, depending on whether full original filings and call transcripts were found, and cuts back footnote analysis when only summaries exist. Background agents then gather filings from investor relations pages, SEC EDGAR and exchange disclosure sites, along with call transcripts and shareholder letters. Tables follow for revenue and profit, margins, GAAP against non-GAAP, EPS and cash flow, and figures from two sources that differ by more than 1% are flagged.
When your agent uses it
- Reading a company's latest quarterly or annual report in depth
- Checking management guidance against reported results
- Building a financial review from filings rather than news summaries
Example prompts
- “Do a deep reading of Tencent's latest quarterly results from the filing and call transcript.”
- “Review PDD's annual report and flag any figures where sources disagree.”
- “Analyze Meituan's most recent earnings and grade how complete the primary sources are.”
Requirements
- Web access to filings and call transcripts
- The repository's `tools/` helpers, such as `financial_rigor.py`
Workflow steps
4 steps, taken from the first numbered list in SKILL.md.
- 财报原文：从公司IR页面、SEC EDGAR（美股10-K/10-Q）、港交所披露易（港股）、巨潮资讯网（A股）获取
- 业绩电话会纪要/录音：从 Seeking Alpha、公司IR页面、雪球等获取
- 管理层致股东信（如有年报）：完整阅读
- 投资者日/分析师日材料（如近期有）
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
Earnings Report Deep Reading loads about 1.4k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 395 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). 395 words, ~1,445 tokens.
Download SKILL.mdSave it as
.claude/skills/earnings-review/SKILL.md (or your agent's skills folder).- name
- earnings-review
- description
- AI Berkshire skill: 财报精读：一手资料深度解读. Source: skills/earnings-review.md.
Codex adapter note
This skill is generated from skills/earnings-review.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.
财报精读：一手资料深度解读
对 $ARGUMENTS 进行财报精读分析。
支持输入格式：公司名 季度，例如：腾讯 2025Q4、PDD 2025年报、美团 最新（默认读取最近一期）
"我从不看卖方研报，只读原始财报。" —— 李录
"我每天读500页。知识就是这样积累的，像复利一样。" —— 巴菲特
设计理念
大多数AI投研工具依赖二手信息（新闻、研报摘要、数据网站）。但巴菲特和李录的核心能力是读一手资料——年报、季报、电话会纪要。
二手信息的问题：
- 被筛选过——分析师选择性呈现对其观点有利的数据
- 有时滞——等别人消化完，alpha已经没了
- 缺乏语境——"收入增长15%"脱离了管理层对增长质量的讨论
本Skill直接解读一手资料，关注巴菲特和李录真正会看的内容。
执行流程
前置步骤：资料可得性评级
| 等级 | 特征 | 影响 |
|---|---|---|
| A级 | 获取到完整原文（10-K/年报/电话会纪要） | 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 | 聚焦核心财务数据变化，跳过附注挖掘，标注"一手资料不足" |
第一步：获取一手资料
使用 Task 工具启动多个后台 Agent 并行获取以下原始材料：
- 财报原文：从公司IR页面、SEC EDGAR（美股10-K/10-Q）、港交所披露易（港股）、巨潮资讯网（A股）获取
- 业绩电话会纪要/录音：从 Seeking Alpha、公司IR页面、雪球等获取
- 管理层致股东信（如有年报）：完整阅读
- 投资者日/分析师日材料（如近期有）
如果无法获取完整原文，按 skills/financial-data.md 规范使用标准数据源拼凑（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），但必须标注"非原始财报，来自第三方汇总"，且关键数据两源误差>1%须标记。
第二步：核心财务数据提取与验证
Show full SKILL.md (164 more words)Show less
2.1 收入与利润表
| 指标 | 本期 | 上期 | YoY变化 | 管理层指引 | 是否达标 |
|---|
必须覆盖：
- 总收入及分业务/分地区收入拆解
- 毛利润、毛利率变化
- 经营利润、经营利润率变化（区分GAAP和Non-GAAP）
- 净利润（注意非经常性损益的影响）
- EPS（基本 vs 稀释）
2.2 现金流表（巴菲特最看重）
| 指标 | 本期 | 上期 | 变化 | 关注点 |
|---|
必须覆盖：
- 经营性现金流 vs 净利润的比率（>100%为佳，<80%需警惕）
- 资本开支及其构成（维护性 vs 扩张性）
- 自由现金流 = 经营现金流 - 资本开支
- 回购金额、分红金额
- 现金及等价物期末余额
2.3 资产负债表健康度
必须覆盖：
- 现金+短期投资 vs 有息负债
- 净现金/净负债变化趋势
- 应收账款周转天数变化（是否在放松信用条件冲收入？）
- 存货周转天数变化（是否在积压？）
- 商誉及无形资产占比（是否有减值风险？）
数据验证：使用 tools/financial_rigor.py 对关键数据进行校验：
# 收入和净利润交叉验证（至少2个来源）
python3 tools/financial_rigor.py cross-validate \
--metric "revenue" --values 108.3e9 107.9e9 --sources "公司财报" "Yahoo Finance"
# 市值校验
python3 tools/financial_rigor.py verify-market-cap \
--price 101 --shares 1.488e9 --reported 1.44e11 --currency USD
# 估值指标验算
python3 tools/financial_rigor.py verify-valuation \
--price 101 --eps 9.6 --bvps 26.5 --fcf-per-share 10.2第三步：管理层讨论精读（MD&A）
这是巴菲特和李录花最多时间的部分。不是看数字，是听管理层怎么说。
3.1 管理层语气分析
逐段阅读管理层讨论/电话会发言，标注以下信号：
| 信号类型 | 具体表现 | 示例 |
|---|---|---|
| 🟢 坦诚信号 | 主动承认问题、给出具体原因 | "本季度利润率下降主要因为我们在X领域的投入超出预期" |
| 🟢 清晰信号 | 战略表述具体、有量化目标 | "我们计划在未来12个月将X业务的市场份额从15%提升到20%" |
| 🔴 模糊信号 | 大量使用"我们相信"、"长期来看"等没有实质内容的话 | "我们对未来充满信心" |
| 🔴 转移信号 | 回避直接问题、用其他话题带过 | 被问利润率时转谈收入增速 |
| 🔴 归因外部化 | 把问题全归咎于宏观/行业/竞争对手 | "由于宏观环境影响..." |
3.2 承诺追踪
从上一期财报/电话会中提取管理层的具体承诺，与本期实际情况对比：
| 上期承诺 | 本期兑现情况 | 评价 |
|---|---|---|
| "下半年利润率将恢复到X%" | 实际Y% | ✅达标 / ❌未达标 / ⚠️部分达标 |
段永平："看一个管理层靠不靠谱，最简单的方法就是看他以前说的话做到了没有。"
3.3 关键问题识别
从电话会Q&A环节提取分析师最尖锐的问题，以及管理层的回答质量：
| 分析师问题 | 管理层回答 | 回答质量(1-5) | 是否回避 |
|---|
第四步：附注与隐藏信息挖掘
财报附注里藏着管理层不想让你轻易看到的信息：
4.1 必查附注项
- 关联交易：与大股东/关联方的交易条款是否公允？
- 股权激励：期权/RSU的稀释效应有多大？行权价是多少？
- 或有负债：诉讼、担保、承诺等表外风险
- 会计政策变更：是否改变了收入确认方式、折旧年限等？
- 分部信息：不同业务的利润率差异，是否有"好业务补贴坏业务"
- 客户/供应商集中度：前五大客户/供应商占比
4.2 异常信号检测
- 应收账款增速 > 收入增速（可能在塞渠道）
- 存货增速 > 收入增速（可能在积压）
- 经营现金流 < 净利润且差距扩大（利润质量存疑）
- 资本化开支突然增加（可能在美化利润）
- 非经常性收益占比突然上升
第五步：与历史数据对比
5.1 趋势分析
将本期关键指标放入至少4个季度（或3年年报）的时间序列中：
| 指标 | Q-4 | Q-3 | Q-2 | Q-1 | 本期 | 趋势判断 |
|---|
重点关注：
- 利润率是在改善还是恶化？
- 收入增速是在加速还是减速？
- 现金流质量是在提升还是下降？
- 资本开支强度是在增加还是减少？
5.2 与管理层指引对比
| 指标 | 管理层此前指引 | 实际结果 | 偏差 | 解读 |
|---|
第六步：输出精读报告
报告结构
一、核心数据速览（一页表格）
二、本期最重要的3个变化（不超过500字）
三、管理层语气与承诺追踪
四、附注中的隐藏信息
五、关键问题（电话会Q&A精选）
六、与投资论文的关系（如有持仓）
七、结论：这份财报改变了什么？结论必须明确回答
- 这份财报是超预期、符合预期、还是低于预期？（不能说"基本符合"然后列一堆两面话）
- 对投资论文的影响：强化 / 无影响 / 削弱 / 破裂
- 需要关注的下一个催化剂是什么？
- 如果你已持有，该加仓/持有/减仓？
第七步：保存报告
将报告写入 reports/{公司名}-earnings-{期间}.md，例如 reports/腾讯-earnings-2025Q4.md
第八步：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
python3 tools/report_audit.py extract \
--report reports/{公司名}-earnings-{期间}.md
# 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 → 发布；【打回】 有不通过 → 修正后重审。
关键原则
- 读原文，不读摘要：尽一切可能获取一手资料
- 看变化，不看绝对值：趋势比数字本身重要
- 听语气，不只听内容：管理层怎么说和说了什么一样重要
- 查附注，不只看正文：魔鬼藏在细节里
- 给结论，不做汇总：精读的目的是形成判断，不是复述财报
© 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/earnings-review of xbtlin/ai-berkshire.
Open the folder on GitHubat commit a221a20
Compare with similar skills
Earnings Report Deep Reading 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 Reading this skillxbtlin/ai-berkshire | 17k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Eastmoney Market DataHKUDS/Vibe-Trading | 35k | — | ~1k | Automated safety check: Pass | MIT | |
| SEC EDGAR Filings FetcherHKUDS/Vibe-Trading | 35k | — | ~1.4k | Automated safety check: Pass | MIT | |
| Financial Researchfirecrawl/web-agent | 1.2k | — | ~1.1k | Automated safety check: Pass | MIT | |
| US Market Data ToolkitGeeksfino/finskills | 282 | — | ~1.2k | Automated safety check: Pass | Apache-2.0 | |
| Financial Statement Deep DiveGeeksfino/finskills | 282 | — | ~1.8k | Automated safety check: Pass | Apache-2.0 |
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Questions about Earnings Report Deep Reading
What does Earnings Report Deep Reading do?
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. Written in Chinese and adapted from a Claude Code workflow for Codex, this skill analyzes one company's results from primary sources instead of news or broker research. You give a company and period, such as a quarter or an annual report, and it reads the latest one by default.
When should I use Earnings Report Deep Reading?
Earnings Report Deep Reading fits situations like: reading a company's latest quarterly or annual report in depth; checking management guidance against reported results; building a financial review from filings rather than news summaries.
How do I install Earnings Report Deep Reading in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill earnings-review -a claude-code`. Or copy the skill folder (codex-skills/earnings-review in xbtlin/ai-berkshire) into .claude/skills/earnings-review in your project. Claude Code loads it when a task matches its description.
How do I install Earnings Report Deep Reading in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill earnings-review -a codex`. Or copy the skill folder (codex-skills/earnings-review in xbtlin/ai-berkshire) into .agents/skills/earnings-review in your project. Codex loads it when a task matches its description.
Can I use Earnings Report Deep Reading 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 earnings-review -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-review, .gemini/skills/earnings-review, .github/skills/earnings-review and .opencode/skills/earnings-review in your project.
What does Earnings Report Deep Reading need to run?
Going by SKILL.md and its folder, Earnings Report Deep Reading needs the command-line tools its instructions call (python3). Our summary lists: Web access to filings and call transcripts; The repository's `tools/` helpers, such as `financial_rigor.py`.
Does Earnings Report Deep Reading 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 Earnings Report Deep Reading 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 Earnings Report Deep Reading use?
Earnings Report Deep Reading 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 Earnings Report Deep Reading use?
About 1.4k tokens (SKILL.md is roughly 5.8k 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 Earnings Report Deep Reading?
Skills that share tags, products or a category with Earnings Report Deep Reading: Eastmoney Market Data (HKUDS/Vibe-Trading, 35k stars), SEC EDGAR Filings Fetcher (HKUDS/Vibe-Trading, 35k stars), Financial Research (firecrawl/web-agent, 1.2k stars) and US Market Data Toolkit (Geeksfino/finskills, 282 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Earnings Report Deep Reading?
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.