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connector-skill

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UpdatedFebruary 11, 2026 at 12:28

Connect to data sources, explore schemas, write queries, and profile data quality. Use when the user wants to query a database, explore a data source, profile tables, or understand their data schema. Handles SQL dialects, API-based data sources, and file-based data. Includes query optimization and result validation patterns.

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jeremyserna-lgtm

jeremyserna-lgtm/truth_forge

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Related occupationsSOC

Based on SOC occupation classification

Software DevelopersComputer and Mathematical Occupations·SOC 15-1252

File Explorer

2 files

SKILL.md

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name	connector-skill
description	Connect to data sources, explore schemas, write queries, and profile data quality. Use when the user wants to query a database, explore a data source, profile tables, or understand their data schema. Handles SQL dialects, API-based data sources, and file-based data. Includes query optimization and result validation patterns.

Data Connector Skill

Query Patterns

SQL-based Sources

Write dialect-appropriate SQL. Check schema first, validate results after.

API-based Sources

Use MCP tools to query. Handle pagination and rate limits.

File-based Sources

Read CSV/Excel/JSON files and analyze with pandas or similar.

Validation Checklist

Row count sanity
Null check
Value range check
Aggregation logic

Trace Awareness

When previous TRACE.md or FILTER.md files exist for this data source:

Read the Attention Log — know what schema areas were already explored
Read the Confidence Map — know where schema understanding is weak
Read the Surplus Value — know what data patterns emerged last time
Read the Filter — skip known-noise areas, prioritize signal areas

References

See references/schema-guide.md for schema exploration patterns

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__SKILL_DESCRIPTION__

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name	connector-skill
description	Connect to data sources, explore schemas, write queries, and profile data quality. Use when the user wants to query a database, explore a data source, profile tables, or understand their data schema. Handles SQL dialects, API-based data sources, and file-based data. Includes query optimization and result validation patterns.

Data Connector Skill

Query Patterns

SQL-based Sources

Write dialect-appropriate SQL. Check schema first, validate results after.

API-based Sources

Use MCP tools to query. Handle pagination and rate limits.

File-based Sources

Read CSV/Excel/JSON files and analyze with pandas or similar.

Validation Checklist

Row count sanity
Null check
Value range check
Aggregation logic

Trace Awareness

When previous TRACE.md or FILTER.md files exist for this data source:

Read the Attention Log — know what schema areas were already explored
Read the Confidence Map — know where schema understanding is weak
Read the Surplus Value — know what data patterns emerged last time
Read the Filter — skip known-noise areas, prioritize signal areas

References

See references/schema-guide.md for schema exploration patterns