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//_.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: Search Results Insight [[Apple]] 207 companies Apple's full Taiwan supplier network [[NVIDIA]] 277 companies NVIDIA's Taiwan supply chain [[台積電]] 469 companies Taiwan semiconductor ecosystem [[CoWoS]] 39 companies TSMC advanced packaging players [[AI 伺服器]] 148 companies AI server supply chain [[PCB]] 263 companies Printed circuit board ecosystem [[電動車]] 223 companies EV 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 Operation Tokens Used Command Update financials Free (yfinance) python scripts/update_financials.py Update valuation Free (yfinance) python scripts/update_valuation.py Discover (with results) Free python scripts/discover.py "term" Audit Free python scripts/audit_batch.py Build themes/network/index Free python scripts/build_*.py /add-ticker (Claude Code) Medium AI research per ticker /update-enrichment (Claude Code) Medium 3–5 web searches per ticker /discover (no results found) Low–High AI 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