MCPInstall

taiwan-equity-research-coverage

by reason-machinesProductivity & Automation

Structured equity research database for 1,735 Taiwan-listed companies with wikilink knowledge graph, supply chain mapping, and financial data tools.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/trending-skills --skill taiwan-equity-research-coverage
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads taiwan-equity-research-coverage automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Taiwan Equity Research Coverage (My-TW-Coverage)

Skill by ara.so — Daily 2026 Skills collection.

A structured equity research database covering 1,735 Taiwan-listed companies (TWSE + OTC) across 99 industry sectors. Each report contains a business overview, supply chain mapping, customer/supplier relationships, and financial data — all cross-referenced through 4,900+ wikilinks forming a searchable knowledge graph.

Installation

git clone https://github.com/Timeverse/My-TW-Coverage
cd My-TW-Coverage
pip install yfinance pandas tabulate

Project Structure

My-TW-Coverage/
├── Pilot_Reports/             # 1,735 ticker reports across 99 sectors
│   ├── Semiconductors/        # 155 tickers
│   ├── Electronic Components/ # 267 tickers
│   ├── Computer Hardware/     # 114 tickers
│   └── ... (99 sector folders)
├── scripts/
│   ├── utils.py               # Shared utilities
│   ├── add_ticker.py          # Generate new ticker reports
│   ├── update_financials.py   # Refresh financial tables + valuation
│   ├── update_valuation.py    # Refresh valuation multiples only (fast)
│   ├── update_enrichment.py   # Update business descriptions from JSON
│   ├── audit_batch.py         # Quality auditing
│   ├── discover.py            # Buzzword → related companies search
│   ├── build_wikilink_index.py# Rebuild WIKILINKS.md index
│   ├── build_themes.py        # Generate thematic investment screens
│   └── build_network.py       # Generate D3.js network graph
├── WIKILINKS.md               # Auto-generated browsable wikilink index
├── network/index.html         # Interactive D3.js wikilink network
├── themes/                    # Thematic investment screens (auto-generated)
└── task.md                    # Batch definitions and progress tracking

Report Format

Each ticker report is a markdown file at Pilot_Reports/<Sector>/<TICKER>_<Name>.md:

# 2330 - [[台積電]]

## 業務簡介
**板塊:** Technology
**產業:** Semiconductors
**市值:** 47,326,857 百萬台幣
**企業價值:** 44,978,990 百萬台幣

台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程...

## 供應鏈位置
**上游:** [[ASML]], [[Applied Materials]], [[SUMCO]]
**中游:** **台積電** (晶圓代工)
**下游:** [[Apple]], [[NVIDIA]], [[AMD]], [[Broadcom]]

## 主要客戶及供應商
### 主要客戶
- [[Apple]], [[NVIDIA]], [[AMD]], [[Qualcomm]]

### 主要供應商
- [[ASML]], [[Tokyo Electron]], [[Shin-Etsu]]

## 財務概況
### 估值指標
| P/E (TTM) | Forward P/E | P/S (TTM) | P/B | EV/EBITDA |
|-----------|-------------|-----------|-----|-----------|
| 28.5      | 22.1        | 9.3       | 7.2 | 16.4      |

### 年度財務數據
[Annual 3-year financial table with 14 metrics]

### 季度財務數據
[Quarterly 4-quarter financial table]

Key Commands

Add a New Ticker

# Basic (auto-detect sector)
python scripts/add_ticker.py 2330 台積電

# With explicit sector
python scripts/add_ticker.py 2330 台積電 --sector Semiconductors

Update Financial Data

# Single ticker
python scripts/update_financials.py 2330

# Multiple tickers
python scripts/update_financials.py 2330 2454 3034

# By batch number (see task.md for batch definitions)
python scripts/update_financials.py --batch 101

# By sector
python scripts/update_financials.py --sector Semiconductors

# All 1,735 tickers (slow)
python scripts/update_financials.py

Update Valuation Only (~3x Faster)

Refreshes only P/E, Forward P/E, P/S, P/B, EV/EBITDA, and stock price — skips full financial statement re-fetch.

python scripts/update_valuation.py 2330
python scripts/update_valuation.py --batch 101
python scripts/update_valuation.py --sector Semiconductors
python scripts/update_valuation.py                          # All tickers

Discover Companies by Buzzword

Find every Taiwan-listed company related to a theme or technology:

# Basic search
python scripts/discover.py "液冷散熱"

# Auto-detect relevant sectors (skips banks/insurance/real estate for tech terms)
python scripts/discover.py "液冷散熱" --smart

# Tag matching companies with [[wikilinks]] in their reports
python scripts/discover.py "液冷散熱" --apply

# Apply + rebuild themes + rebuild network graph
python scripts/discover.py "液冷散熱" --apply --rebuild

# Limit to a specific sector
python scripts/discover.py "液冷散熱" --sector Semiconductors

Common buzzword examples:

  • "CoWoS" — TSMC advanced packaging supply chain
  • "HBM" — High Bandwidth Memory ecosystem
  • "電動車" — EV component suppliers
  • "AI 伺服器" — AI server supply chain (148 companies)
  • "光阻液" — Photoresist suppliers and consumers
  • "碳化矽" — Silicon carbide (SiC) companies

Update Enrichment Content (Bulk AI Research)

Prepare a JSON file, then apply to specific tickers, batches, or sectors:

python scripts/update_enrichment.py --data enrichment.json 2330
python scripts/update_enrichment.py --data enrichment.json --batch 101
python scripts/update_enrichment.py --data enrichment.json --sector Semiconductors

Enrichment JSON format:

{
  "2330": {
    "desc": "台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程，為 [[Apple]]、[[NVIDIA]] 等科技巨頭提供晶片製造服務。",
    "supply_chain": "**上游:**\n- [[ASML]] (EUV 微影設備)\n- [[Applied Materials]] (薄膜沉積)\n**中游:**\n- **台積電** (晶圓代工)\n**下游:**\n- [[Apple]]\n- [[NVIDIA]]",
    "cust": "### 主要客戶\n- [[Apple]] (約25%營收)\n- [[NVIDIA]]\n- [[AMD]]\n\n### 主要供應商\n- [[ASML]]\n- [[Tokyo Electron]]"
  },
  "2454": {
    "desc": "...",
    "supply_chain": "...",
    "cust": "..."
  }
}

Audit Report Quality

# Single batch
python scripts/audit_batch.py 101 -v

# All batches
python scripts/audit_batch.py --all -v

Audit checks:

  • Minimum 8 wikilinks per report
  • No generic terms in brackets (e.g. [[公司]], [[產品]])
  • No placeholder text remaining
  • No English text in Chinese-language sections
  • Metadata completeness (板塊, 產業, 市值, 企業價值)
  • Section depth (業務簡介, 供應鏈位置, 主要客戶及供應商, 財務概況 all present)

Rebuild Wikilink Index

python scripts/build_wikilink_index.py

Regenerates WIKILINKS.md — a browsable index of all 4,900+ wikilinks categorized as Technologies, Materials, Applications, and Companies. Run after any enrichment update.

Generate Thematic Investment Screens

# Build all 20 themes
python scripts/build_themes.py

# Single theme
python scripts/build_themes.py "CoWoS"

# List available themes
python scripts/build_themes.py --list

Output in themes/ — each page shows companies grouped by upstream/midstream/downstream role.

Generate Interactive Network Graph

# Default: min 5 co-occurrences
python scripts/build_network.py

# Fewer edges for cleaner view
python scripts/build_network.py --min-weight 10

# Only top 200 nodes
python scripts/build_network.py --top 200

Opens network/index.html in browser — D3.js force-directed graph. Node colors:

  • 🔴 Red = Taiwan company
  • 🔵 Blue = International company
  • 🟢 Green = Technology
  • 🟠 Orange = Material
  • 🟣 Purple = Application

Wikilink Graph — Core Feature

The wikilink graph is what makes this database powerful. Every [[entity]] in every report creates edges in a knowledge graph.

Search by entity to find related companies:

SearchResultsInsight
[[Apple]]207 companiesApple's full Taiwan supplier network
[[NVIDIA]]277 companiesNVIDIA's Taiwan supply chain
[[台積電]]469 companiesTaiwan semiconductor ecosystem
[[CoWoS]]39 companiesTSMC advanced packaging players
[[AI 伺服器]]148 companiesAI server supply chain
[[PCB]]263 companiesPrinted circuit board ecosystem
[[電動車]]223 companiesEV component suppliers

Browse: Open WIKILINKS.md for the full categorized index.

Code Examples

Read and Parse a Report

import re
from pathlib import Path

def get_report(ticker: str, reports_dir: str = "Pilot_Reports") -> dict:
    """Find and parse a ticker report."""
    base = Path(reports_dir)
    # Find the file across all sector subdirectories
    matches = list(base.rglob(f"{ticker}_*.md"))
    if not matches:
        return {}
    
    content = matches[0].read_text(encoding="utf-8")
    
    # Extract all wikilinks
    wikilinks = re.findall(r'\[\[([^\]]+)\]\]', content)
    
    # Extract sector metadata
    sector_match = re.search(r'\*\*產業:\*\*\s*(.+)', content)
    board_match = re.search(r'\*\*板塊:\*\*\s*(.+)', content)
    
    return {
        "ticker": ticker,
        "file": str(matches[0]),
        "sector": sector_match.group(1).strip() if sector_match else None,
        "board": board_match.group(1).strip() if board_match else None,
        "wikilinks": list(set(wikilinks)),
        "wikilink_count": len(set(wikilinks)),
        "content": content
    }

# Usage
report = get_report("2330")
print(f"Sector: {report['sector']}")
print(f"Wikilinks ({report['wikilink_count']}): {report['wikilinks'][:10]}")

Build a Custom Wikilink Index

import re
from pathlib import Path
from collections import defaultdict

def build_wikilink_index(reports_dir: str = "Pilot_Reports") -> dict:
    """
    Returns: {entity: [list of tickers that mention it]}
    """
    index = defaultdict(list)
    
    for md_file in Path(reports_dir).rglob("*.md"):
        # Extract ticker from filename (e.g. "2330_台積電.md" -> "2330")
        ticker = md_file.stem.split("_")[0]
        content = md_file.read_text(encoding="utf-8")
        wikilinks = set(re.findall(r'\[\[([^\]]+)\]\]', content))
        
        for link in wikilinks:
            index[link].append(ticker)
    
    # Sort by mention count
    return dict(sorted(index.items(), key=lambda x: len(x[1]), reverse=True))

# Find all companies in Apple's supply chain
index = build_wikilink_index()
apple_suppliers = index.get("Apple", [])
print(f"Apple supply chain: {len(apple_suppliers)} companies")
print(apple_suppliers[:20])

# Find companies involved in CoWoS
cowos_companies = index.get("CoWoS", [])
print(f"\nCoWoS ecosystem: {len(cowos_companies)} companies: {cowos_companies}")

Find Supply Chain Overlaps Between Two Entities

def supply_chain_overlap(entity_a: str, entity_b: str, reports_dir: str = "Pilot_Reports"):
    """Find tickers that appear in both entities' supply chains."""
    index = build_wikilink_index(reports_dir)
    
    set_a = set(index.get(entity_a, []))
    set_b = set(index.get(entity_b, []))
    overlap = set_a & set_b
    
    print(f"{entity_a}: {len(set_a)} companies")
    print(f"{entity_b}: {len(set_b)} companies")
    print(f"Overlap: {len(overlap)} companies — {sorted(overlap)}")
    return overlap

# Companies in both NVIDIA and Apple supply chains
supply_chain_overlap("NVIDIA", "Apple")

# Companies in both AI server and EV supply chains
supply_chain_overlap("AI 伺服器", "電動車")

Batch Financial Update with Error Handling

import subprocess
import sys

def update_sector_financials(sector: str, valuation_only: bool = False):
    """Update financials for all tickers in a sector."""
    script = "update_valuation.py" if valuation_only else "update_financials.py"
    cmd = [sys.executable, f"scripts/{script}", "--sector", sector]
    
    result = subprocess.run(cmd, capture_output=True, text=True)
    
    if result.returncode != 0:
        print(f"Error: {result.stderr}")
    else:
        print(result.stdout)
    
    return result.returncode

# Update valuation multiples for semiconductors (fast)
update_sector_financials("Semiconductors", valuation_only=True)

# Full financial update for a sector
update_sector_financials("Electronic Components", valuation_only=False)

Prepare Enrichment JSON

import json

def build_enrichment_entry(ticker: str, company_name: str,
                           description: str, upstream: list[str],
                           midstream: str, downstream: list[str],
                           customers: list[str], suppliers: list[str]) -> dict:
    """
    Build a properly formatted enrichment entry.
    All entity names in lists will be wrapped in [[wikilinks]].
    """
    def wikify(items):
        return "\n".join(f"- [[{item}]]" for item in items)
    
    supply_chain = (
        f"**上游:**\n{wikify(upstream)}\n"
        f"**中游:**\n- **{company_name}** ({midstream})\n"
        f"**下游:**\n{wikify(downstream)}"
    )
    
    cust_section = (
        f"### 主要客戶\n{wikify(customers)}\n\n"
        f"### 主要供應商\n{wikify(suppliers)}"
    )
    
    return {
        "desc": description,
        "supply_chain": supply_chain,
        "cust": cust_section
    }

# Build enrichment for multiple tickers
enrichment = {
    "2330": build_enrichment_entry(
        ticker="2330",
        company_name="台積電",
        description="台積電為全球最大晶圓代工廠，專注於 [[CoWoS]]、[[3奈米]] 先進製程，為全球領先科技公司提供晶片製造服務。",
        upstream=["ASML", "Applied Materials", "SUMCO", "Tokyo Electron"],
        midstream="晶圓代工",
        downstream=["Apple", "NVIDIA", "AMD", "Broadcom", "Qualcomm"],
        customers=["Apple", "NVIDIA", "AMD", "Qualcomm", "MediaTek"],
        suppliers=["ASML", "Tokyo Electron", "Shin-Etsu", "Applied Materials"]
    )
}

# Save to file
with open("enrichment.json", "w", encoding="utf-8") as f:
    json.dump(enrichment, f, ensure_ascii=False, indent=2)

print("enrichment.json ready. Apply with:")
print("python scripts/update_enrichment.py --data enrichment.json 2330")

Audit a Batch Programmatically

import subprocess
import json

def audit_and_report(batch_id: int) -> dict:
    """Run audit and parse results."""
    result = subprocess.run(
        ["python", "scripts/audit_batch.py", str(batch_id), "-v"],
        capture_output=True, text=True
    )
    
    output = result.stdout
    
    # Parse pass/fail counts from output
    passed = output.count("✓") 
    failed = output.count("✗")
    
    return {
        "batch": batch_id,
        "passed": passed,
        "failed": failed,
        "pass_rate": passed / (passed + failed) if (passed + failed) > 0 else 0,
        "output": output
    }

results = audit_and_report(101)
print(f"Batch 101: {results['passed']} passed, {results['failed']} failed")
print(f"Pass rate: {results['pass_rate']:.1%}")

Common Workflows

Workflow 1: Research a New Investment Theme

# 1. Search for related companies
python scripts/discover.py "液冷散熱" --smart

# 2. Apply wikilinks to matching reports
python scripts/discover.py "液冷散熱" --apply

# 3. Rebuild themes to include new theme
python scripts/build_themes.py "液冷散熱"

# 4. Rebuild wikilink index and network
python scripts/build_wikilink_index.py
python scripts/build_network.py

# 5. Browse results
open themes/液冷散熱.md
open network/index.html

Workflow 2: Onboard a New Ticker

# 1. Add the report (Python script, free)
python scripts/add_ticker.py 6669 緯穎 --sector Computer Hardware

# 2. Update financial data
python scripts/update_financials.py 6669

# 3. Prepare enrichment JSON (use AI research or manual)
# Edit enrichment.json with business description, supply chain, customers

# 4. Apply enrichment
python scripts/update_enrichment.py --data enrichment.json 6669

# 5. Audit quality
python scripts/audit_batch.py --all -v

# 6. Rebuild index
python scripts/build_wikilink_index.py

Workflow 3: Refresh Valuation for Earnings Season

# Fast valuation refresh only (no full financial re-fetch)
python scripts/update_valuation.py --sector Semiconductors
python scripts/update_valuation.py --sector Electronic Components
python scripts/update_valuation.py --sector Computer Hardware

# Or refresh everything (slow, run overnight)
python scripts/update_valuation.py

Workflow 4: Map a Supply Chain

# Find all Taiwan companies connected to a specific technology
from collections import defaultdict
import re
from pathlib import Path

def map_supply_chain(technology: str, reports_dir: str = "Pilot_Reports"):
    results = {"upstream": [], "midstream": [], "downstream": []}
    
    for md_file in Path(reports_dir).rglob("*.md"):
        content = md_file.read_text(encoding="utf-8")
        
        if f"[[{technology}]]" not in content:
            continue
        
        ticker = md_file.stem.split("_")[0]
        company = md_file.stem.split("_", 1)[1] if "_" in md_file.stem else ""
        
        # Detect position in supply chain
        if f"**上游:**" in content and f"[[{technology}]]" in content.split("**上游:**")[1].split("**中游:**")[0]:
            results["upstream"].append(f"{ticker} {company}")
        elif f"[[{technology}]]" in content and "**中游:**" in content:
            mid_section = content.split("**中游:**")[1].split("**下游:**")[0] if "**下游:**" in content else ""
            if f"[[{technology}]]" in mid_section:
                results["midstream"].append(f"{ticker} {company}")
        else:
            results["downstream"].append(f"{ticker} {company}")
    
    return results

chain = map_supply_chain("CoWoS")
print(f"Upstream: {chain['upstream']}")
print(f"Midstream: {chain['midstream']}")
print(f"Downstream: {chain['downstream']}")

Token Cost Reference

OperationTokens UsedCommand
Update financialsFree (yfinance)python scripts/update_financials.py
Update valuationFree (yfinance)python scripts/update_valuation.py
Discover (with results)Freepython scripts/discover.py "term"
AuditFreepython scripts/audit_batch.py
Build themes/network/indexFreepython scripts/build_*.py
/add-ticker (Claude Code)MediumAI research per ticker
/update-enrichment (Claude Code)Medium3–5 web searches per ticker
/discover (no results found)Low–HighAI researches online

Rule of thumb: Use Python scripts for bulk data operations. Use Claude Code slash commands only when AI research is needed for a specific ticker.

Troubleshooting

yfinance returns no data for a Taiwan ticker:

import yfinance as yf
# Taiwan tickers need .TW suffix for TWSE, .TWO for OTC
tsmc = yf.Ticker("2330.TW")
print(tsmc.info.get("marketCap"))

Report not found by scripts:

  • Filename must match pattern: {TICKER}_{CompanyName}.md
  • Must be inside a subfolder of Pilot_Reports/
  • Use python scripts/utils.py to test file discovery

Audit fails "too few wikilinks":

  • Minimum 8 unique [[wikilinks]] required per report
  • Use update_enrichment.py to add richer content
  • Run discover.py --apply to auto-tag relevant wikilinks

build_network.py produces empty graph:

# Ensure reports exist and have wikilinks first
python scripts/build_wikilink_index.py  # Check WIKILINKS.md has entries
python scripts/build_network.py --min-weight 2  # Lower threshold

Enrichment JSON rejected:

  • Ensure file is valid UTF-8 with ensure_ascii=False
  • Keys must be ticker strings ("2330", not 2330)
  • Required keys per entry: desc, supply_chain, cust
  • Content must be in Traditional Chinese; English only for proper nouns

Finding batch numbers:

  • See task.md for batch definitions and which tickers are in each batch
  • Batches are used for incremental processing of the 1,735 tickers

FAQ

What does the taiwan-equity-research-coverage skill do?

Structured equity research database for 1,735 Taiwan-listed companies with wikilink knowledge graph, supply chain mapping, and financial data tools.

How do I install taiwan-equity-research-coverage?

Run npx skills add https://github.com/reason-machines/trending-skills --skill taiwan-equity-research-coverage in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/taiwan-equity-research-coverage/ (personal) or .claude/skills/taiwan-equity-research-coverage/ (project).

Which AI agents can use taiwan-equity-research-coverage?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is taiwan-equity-research-coverage safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains taiwan-equity-research-coverage?

It is published by reason-machines in reason-machines/trending-skills. It has 802 recorded installs.

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飞书多维表格（Base）操作：建表、字段、记录、视图、统计、公式/lookup、表单、仪表盘、应用模式（BaseApp/AppMode 页面与组件）、Workspace 目录、workflow、角色权限、模板中心（多维表格模板分类/列表/搜索）；遇到 Base/多维表格/bitable、BaseApp/AppMode、/base/ 或 /app/ 链接时使用。BaseApp 不走 lark-apps；文件导入/导出转 lark-drive，认证/授权转 lark-shared。

Productivity & Automation815K installs

lark-drive

open.feishu.cn

飞书云空间（云盘/云存储）：管理 Drive 文件和文件夹，包含上传/下载、创建文件夹、复制/移动/删除、查看元数据、查询权限设置、评论/权限/订阅、标题、版本、飞书文档密级标签（secure labels）和本地文件导入。用户需要整理云盘目录、处理云空间资源 URL/token、判断链接类型/真实 token/标题，或导入 Word/Markdown/Excel/CSV/PPTX/.base 为 docx/sheet/bitable/slides 时使用；doubao.com 云空间 URL/token 也按资源路径和 token 路由，不回退 WebFetch。不负责：文档内容编辑（走 lark-doc）、表格/Base 表内数据操作（走 lark-sheets/lark-base）、知识空间节点/成员管理（走 lark-wiki）、原生 Markdown 文件读写/patch/diff（走 lark-markdown）。

Productivity & Automation815K installs

lark-shared

open.feishu.cn

Use for lark-cli setup/auth tasks: auth login/status/logout, user vs bot identity, business-domain permissions (--domain, including all/docs/drive), missing scopes, revoking authorization, or handling notice JSON.

Productivity & Automation814.8K installs

lark-im

open.feishu.cn

飞书即时通讯：收发消息和管理群聊。发送和回复消息、搜索聊天记录、管理群聊成员、上传下载图片和文件、管理表情回复、发送应用内/短信/电话加急、发送和处理交互卡片（Interactive Card）、监听卡片按钮回调（card.action.trigger）。当用户需要发消息、查看或搜索聊天记录、下载聊天中的文件、查看群成员、搜索群、创建群聊或话题群、管理标记数据、管理 Feed 置顶（添加/移除/查询置顶会话）、管理标签数据、处理卡片回调时使用。

Productivity & Automation814.1K installs