AI-Trader Market Intel
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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.
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.
SKILL.md written in Chinese; this summary is our English description.
Install Era Alpha Investment Research Framework
$ npx skills add xbtlin/ai-berkshire --skill era-alpha -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install xbtlin/ai-berkshire era-alpha --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/era-alpha .claude/skills/era-alpha && 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 "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .claude/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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/era-alphaType 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 era-alpha -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install xbtlin/ai-berkshire era-alpha --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/era-alpha .agents/skills/era-alpha && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .agents/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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 era-alpha -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install xbtlin/ai-berkshire era-alpha --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/era-alpha .cursor/skills/era-alpha && 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 "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .cursor/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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/era-alpha--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 era-alpha -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install xbtlin/ai-berkshire era-alpha --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/era-alpha .gemini/skills/era-alpha && 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 "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .gemini/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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 era-alphaInstalls 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 era-alpha -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/era-alpha .github/skills/era-alpha && 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 "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .github/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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 era-alpha -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 era-alpha --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/era-alpha .opencode/skills/era-alpha && 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 "era-alpha" agent skill from https://github.com/xbtlin/ai-berkshire/tree/main/codex-skills/era-alpha into .opencode/skills/era-alpha/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "era-alpha", 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
era-alpha- GitHub stars
- 17k
- Token cost
- ~974 tokens
- SKILL.md length
- 312 words
- Files
- 1
- Skills in repo
- 22
- Repo updated
- First seen
- Licence
- MIT
At a glance
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.
- Works in 3 steps: 修正一（精简范围）：不覆盖所有行业，聚焦指定赛道的 2-3… → 修正二（高频数据交叉验证）：财报是三个月前的体检报告，必须用行业高频数据（周度/月… → 修正三（估值锚点）：长期看"高了还能更高"，但市盈率超历史均值 3…
- Researching which companies lead a high-growth industry segment
- SKILL.md covers Codex adapter note, 方法论来源与本质, 第一步：行业认知地图（原版"读365份财报"的聚焦版） and 第二步：自问核心问题（不听别人的）, plus 5 more sections
- Calls python3
What it does
The SKILL.md is mostly in Chinese and is a Codex adapter of a Claude Code skill. Given an industry or direction in the request, it runs a four-step method: build a map of the industry chain, ask core questions about growth and the leading company, verify with several kinds of evidence, and hold until a fundamental turning point. The goal is to identify companies with pricing power and barriers that keep outgrowing their peers, together with entry and exit discipline.
It adds three adjustments to the original method: narrow the scope to two or three core segments, cross-check slow financial reports against high-frequency data such as weekly or monthly shipments, prices and orders, and use a valuation anchor, since buying when the price-to-earnings ratio sits more than three standard deviations above its historical average can cost a lot of time. Step one produces a table of chain segments with stage, competitive structure, candidate companies and a one-line reason, drawing on prospectuses, annual and quarterly reports and call minutes rather than broker views. Step two answers the core questions from that data without leaning on market consensus.
Before researching, the agent runs the date command and states the data cutoff in the report header, cross-checks financial data, uses exact arithmetic tools for valuation and math, and labels uncertainty and source gaps. It points to shared repository tools such as `tools/financial_rigor.py`, and positions itself as narrower and deeper than the industry-research and industry-funnel skills.
When your agent uses it
- Researching which companies lead a high-growth industry segment
- Cross-checking a company's earnings reports against high-frequency industry data
- Defining the turning points that would end a long-term holding thesis
Example prompts
- “Run the era alpha method on the solid-state battery industry.”
- “Map the AI server supply chain and pick the two or three segments worth studying in depth.”
- “Check whether this leading company's growth is backed by cash flow and monthly shipment data.”
Requirements
- The repository's shared tools folder, including `financial_rigor.py`
- Python 3 to run the shared tools
- Web search access for current industry data
Workflow steps
3 steps, taken from the first numbered list in SKILL.md.
- 修正一（精简范围）：不覆盖所有行业，聚焦指定赛道的 2-3 个核心环节做深做透。三五家真正看透，胜过认识一千家。
- 修正二（高频数据交叉验证）：财报是三个月前的体检报告，必须用行业高频数据（周度/月度出货量、价格、订单、装机、渗透率）做实时体温计校正。
- 修正三（估值锚点）：长期看"高了还能更高"，但市盈率超历史均值 3 个标准差时介入可能长期输时间。合理或低估时重仓、明显泡沫时减仓、拐点确认时清仓，不闭眼买。
What it can do on your machine
Read from SKILL.md and the folder at commit efa220f. 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
Era Alpha Investment Research Framework loads about 974 tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 312 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 efa220f, republished under its MIT licence (© xbtlin). 312 words, ~974 tokens.
Download SKILL.mdSave it as
.claude/skills/era-alpha/SKILL.md (or your agent's skills folder).- name
- era-alpha
- description
- AI Berkshire skill: 时代α捕手：高增长核心资产的识别-验证-持有框架. Source: skills/era-alpha.md.
Codex adapter note
This skill is generated from skills/era-alpha.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.
时代α捕手：高增长核心资产的识别-验证-持有框架
对 $ARGUMENTS 行业/方向执行"时代α四步法"：建立行业认知地图 → 自问核心问题 → 全方位验证 → 持有到拐点。目标是找出当下最核心高增长行业中真正有定价权、有壁垒、能持续跑赢同行的 α 企业，并给出介入与退出纪律。
方法论来源与本质
源自一套职业投资人的四步操作手册，本质一句话：把财富建立在认知之上，而不是运气或情绪之上。原版四步（读一年财报建地图 → 自问核心高增长行业与核心α → 财报+调研+行业+宏观全验证后介入 → 基本面拐点前死拿）是职业选手的修炼路径，本技能内置了三项修正，使其成为可执行路径：
- 修正一（精简范围）：不覆盖所有行业，聚焦指定赛道的 2-3 个核心环节做深做透。三五家真正看透，胜过认识一千家。
- 修正二（高频数据交叉验证）：财报是三个月前的体检报告，必须用行业高频数据（周度/月度出货量、价格、订单、装机、渗透率）做实时体温计校正。
- 修正三（估值锚点）：长期看"高了还能更高"，但市盈率超历史均值 3 个标准差时介入可能长期输时间。合理或低估时重仓、明显泡沫时减仓、拐点确认时清仓，不闭眼买。
与现有技能的分工：
industry-research偏产业链全景切片；industry-funnel偏全市场漏斗筛选era-alpha偏"时代级高增长主线"的 α 识别 + 增长可持续性验证 + 持有/退出纪律，聚焦更窄、验证更深、给出明确的拐点清单
第一步：行业认知地图（原版"读365份财报"的聚焦版）
对目标赛道建立产业链认知地图，每个环节回答：
- 这个环节处于什么阶段？（导入期/成长期/成熟期/衰退期，用渗透率和增速定位）
- 商业模式与赚钱方式？（毛利率、费用率、现金流与利润的匹配度）
- 竞争格局？（CR3、定价权在谁手里、壁垒是技术/规模/生态/牌照）
- 每个环节的 α 候选是谁？（营收增速、ROE 趋势、市占率变化三个维度筛）
信息源要求：以招股说明书、年报/季报、业绩说明会纪要等一手资料为主，券商观点只做线索不做结论。A股/港股/美股/未上市候选都要覆盖，不因资料难找而漏掉。
产出：产业链环节表（环节 | 阶段 | 格局 | α候选 | 一句话理由），并从中选出 2-3 个最值得做深的核心环节（修正一）。
第二步：自问核心问题（不听别人的）
用第一步的数据回答，禁止引用"市场共识""机构观点"作为论据：
- 当下这条赛道最核心、增长最快的环节是什么？ 用数据说话：增速、渗透率、订单能见度。
- 该环节的核心 α 是谁？ 标准：定价权（毛利率高于同行且稳定或提升）、壁垒（对手三年内追不上的东西是什么）、增长质量（收入增长伴随现金流增长，不是赊出来的）。
- 为什么是它而不是老二？ 必须能一句话说清 α 与 β 的差别，说不清就是没看懂。
产出：1-3 家核心 α + 明确的"为什么是它"论证。
第三步：全方位验证（财报 + 高频数据交叉）
对每家核心 α 做增长可持续性五问，所有维度必须一致指向"高增长可持续"才算看懂：
| 维度 | 验证内容 |
|---|---|
| 财报 | 最近 4-8 个季度营收/利润增速趋势、毛利率方向、合同负债/存货/在建工程等前瞻科目 |
| 高频数据（修正二） | 该环节的周度/月度实时指标（出货量、价格、招标、流量、token 调用量等），是否与财报趋势一致 |
| 行业跟踪 | 政策方向、技术路线有无被颠覆风险、供给端扩产节奏（供过于求是成长股最大杀手） |
| 竞争格局 | 市占率变化方向、新进入者威胁、客户集中度与议价权 |
| 宏观 | 利率环境、资本开支周期位置、地缘/监管变量 |
任何一个维度出现矛盾信号，必须明确写出，不许含糊带过。
第四步：估值锚点与介入（修正三，替代"不看股价"）
- 当前 PE/PS 处于自身历史分位数的什么位置？是否超过历史均值 +3 个标准差？
- 估值与增速匹配吗？（PEG 视角：高增速可以消化高估值，但要算清楚需要几年）
- 结论三选一：低估/合理可重仓介入、偏贵但增长可消化可持有或分批、明显泡沫只看不买。
- 给出介入方式建议：一次性/分批/等回调到什么水位。
第五步：持有纪律与拐点清单
持有纪律：只要基本面一切正常（增速未放缓、格局未恶化、渗透率仍在提升、宏观未逆转），股价波动是噪音，说什么都不能随便离场。
拐点清单（每家 α 必须列出，逐条可观察、可证伪）：
| 层级 | 拐点信号示例 |
|---|---|
| 宏观拐点 | 货币政策转向、资本开支周期见顶信号 |
| 行业拐点 | 供给过剩价格崩盘、技术路线被颠覆、渗透率见顶、政策逆转 |
| 公司拐点 | 核心管理层离职、毛利率连续两季下滑、市占率被侵蚀、合同负债转负增长 |
每条信号写明观察哪个数据、多久看一次，让"拐点"从感觉变成清单。
执行方式
- 数据规模大时，按产业链环节并行派出研究 Agent（每环节一个），要求联网获取最新财报与高频数据，返回结构化事实（数据+出处），不返回观点。
- 主线程按第一到第五步综合成报告。
- 遵循
financial-data技能的交叉验证规范；关键数字（增速、毛利率、估值分位）必须标注数据截至日期。
报告结构
一、行业认知地图（环节表 + 核心环节选择理由）
二、核心问题的回答（最核心高增长环节 + 核心α + 为什么是它）
三、增长可持续性验证（五维度逐项 + 矛盾信号明示）
四、估值锚点与介入建议
五、持有纪律与拐点清单（可观察、可证伪）
六、本报告可能错在哪（至少 3 条自我证伪）风格要求：数据密度优先，保留全部硬数据，砍掉脚手架语言；纯中文表达；不挂任何投资人名字，用分析维度命名章节。
© 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/era-alpha of xbtlin/ai-berkshire.
Open the folder on GitHubat commit efa220f
Compare with similar skills
Era Alpha Investment Research Framework 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 |
|---|---|---|---|---|---|---|
| Era Alpha Investment Research Framework this skillxbtlin/ai-berkshire | 17k | — | ~974 | 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 | 1.7k | — | ~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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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.
Deep Company Article Series
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.
Earnings Report Deep Reading
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.
Earnings Report Deep-Read Team
xbtlin/ai-berkshire
Runs four parallel analyst personas over one earnings report, then an editor and reader-review pass turn the findings into a publishable article.
Financial Data Cross-Validation
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.
Income Investment Analysis
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.
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Questions about Era Alpha Investment Research Framework
What does Era Alpha Investment Research Framework do?
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. md is mostly in Chinese and is a Codex adapter of a Claude Code skill. Given an industry or direction in the request, it runs a four-step method: build a map of the industry chain, ask core questions about growth and the leading company, verify with several kinds of evidence, and hold until a fundamental turning point.
When should I use Era Alpha Investment Research Framework?
Era Alpha Investment Research Framework fits situations like: researching which companies lead a high-growth industry segment; cross-checking a company's earnings reports against high-frequency industry data; defining the turning points that would end a long-term holding thesis.
How do I install Era Alpha Investment Research Framework in Claude Code?
Run `npx skills add xbtlin/ai-berkshire --skill era-alpha -a claude-code`. Or copy the skill folder (codex-skills/era-alpha in xbtlin/ai-berkshire) into .claude/skills/era-alpha in your project. Claude Code loads it when a task matches its description.
How do I install Era Alpha Investment Research Framework in Codex?
Run `npx skills add xbtlin/ai-berkshire --skill era-alpha -a codex`. Or copy the skill folder (codex-skills/era-alpha in xbtlin/ai-berkshire) into .agents/skills/era-alpha in your project. Codex loads it when a task matches its description.
Can I use Era Alpha Investment Research Framework 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 era-alpha -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/era-alpha, .gemini/skills/era-alpha, .github/skills/era-alpha and .opencode/skills/era-alpha in your project.
What does Era Alpha Investment Research Framework need to run?
Going by SKILL.md and its folder, Era Alpha Investment Research Framework needs the command-line tools its instructions call (python3). Our summary lists: The repository's shared tools folder, including `financial_rigor.py`; Python 3 to run the shared tools; Web search access for current industry data.
Does Era Alpha Investment Research Framework 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 Era Alpha Investment Research Framework 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 Era Alpha Investment Research Framework use?
Era Alpha Investment Research Framework 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 Era Alpha Investment Research Framework use?
About 974 tokens (SKILL.md is roughly 3.9k 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 Era Alpha Investment Research Framework?
Skills that share tags, products or a category with Era Alpha Investment Research Framework: 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, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Era Alpha Investment Research Framework?
xbtlin (a GitHub user) maintains it in xbtlin/ai-berkshire, which has 16,652 GitHub stars. The repository holds 22 skills in this directory. The repository was last updated on October 6, 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.