China Electronic Components Sourcing Skill

Description

This skill helps international electronics buyers navigate China's electronic components manufacturing landscape, which is projected to exceed ¥5.2 trillion in revenue by 2026. It provides data-backed intelligence on regional clusters, supply chain structure, and industry trends based on the latest government policies and industry reports.

Key Capabilities

• Industry Overview: Get a summary of China's electronic components industry scale and development targets.
• Supply Chain Structure: Understand the complete industry chain from raw materials to downstream applications.
• Regional Clusters: Identify specialized manufacturing hubs for different component types (semiconductors, passives, PCBs, connectors, sensors).
• Subsector Insights: Access detailed information on key subsectors (semiconductors, passive components, PCBs, connectors, sensors, etc.).
• Sourcing Recommendations: Get practical guidance on evaluating and selecting suppliers, including verification methods and communication best practices.

How to Use

You can interact with this skill using natural language. For example:

• "What's the overall status of China's electronic components industry in 2026?"
• "Show me the supply chain structure for electronic components"
• "Which regions are best for sourcing automotive-grade semiconductors?"
• "Tell me about MLCC manufacturing clusters"
• "How do I evaluate PCB suppliers in China?"
• "What certifications should I look for in sensor suppliers?"

Data Sources

This skill aggregates data from:

• Ministry of Industry and Information Technology (MIIT) official policies
• National Bureau of Statistics of China
• China Electronic Components Association (CECA) annual reports
• Industry research publications (updated Q1 2026)

Implementation

The skill logic is implemented in do.py, which reads structured data from data.json. All data is cluster-level intelligence without individual factory contacts.

API Reference

The following Python functions are available in do.py for programmatic access:

get_industry_overview() -> Dict

Returns overview of China's electronic components industry scale, targets, and key policy initiatives.

Example:

from do import get_industry_overview
result = get_industry_overview()
# Returns: industry scale, 2026 targets, automation rates, key drivers, etc.

get_supply_chain_structure() -> Dict

Returns the complete electronic components supply chain structure (upstream, midstream, downstream).

Example:

from do import get_supply_chain_structure
result = get_supply_chain_structure()
# Returns: raw materials, manufacturing, application industries

get_regional_clusters(region: Optional[str] = None) -> Union[List[Dict], Dict]

Returns all regional clusters or a specific cluster by name.

• If region is None: returns list of all clusters
• If region is specified: returns that cluster's details

Example:

from do import get_regional_clusters
all_clusters = get_regional_clusters()
yangtze = get_regional_clusters("Yangtze River Delta")

find_clusters_by_specialization(specialization: str) -> List[Dict]

Find clusters that specialize in a given component type.

Example:

from do import find_clusters_by_specialization
results = find_clusters_by_specialization("automotive semiconductors")

get_subsector_info(subsector: str) -> Dict

Return detailed information about a specific electronic components subsector.

Example:

from do import get_subsector_info
mlcc_info = get_subsector_info("MLCC")
semiconductor_info = get_subsector_info("semiconductors")

get_sourcing_guide() -> Dict

Return supplier evaluation and sourcing best practices.

Example:

from do import get_sourcing_guide
guide = get_sourcing_guide()
# Returns: evaluation criteria, verification methods, communication tips

get_faq(question: Optional[str] = None) -> Union[List[Dict], Dict]

Return FAQ list or answer to a specific question.

Example:

from do import get_faq
all_faqs = get_faq()
moq_faq = get_faq("MOQ")

get_glossary(term: Optional[str] = None) -> Union[Dict, str]

Return glossary of terms or definition of a specific term.

Example:

from do import get_glossary
all_terms = get_glossary()
mlcc_def = get_glossary("MLCC")

search_data(query: str) -> List[Dict]

Simple search across all data for a query string.

Example:

from do import search_data
results = search_data("automotive")

get_metadata() -> Dict

Return metadata about the data source and last update.

Example:

from do import get_metadata
meta = get_metadata()