connector-skill

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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Repositorio: jeremyserna-lgtm/truth_forge
Última actividad en el origen: 11 de febrero de 2026 a las 12:28
Idioma detectado de SKILL.md: inglés
Estrellas: 0
Forks: 0

Opciones de instalación

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npx skills add https://github.com/jeremyserna-lgtm/truth_forge --skill connector-skill

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SKILL.md

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ecosystem-analyzer

Perceive the plugin ecosystem through the six-phase knowledge aggregation methodology — structural reconnaissance, hub identification, sampling, content extraction, pattern synthesis, and feedback. Use when the user says "what plugins do I need", "audit my plugins", "what's missing", "analyze my setup", "plugin gaps", "what should I build next", or wants to understand the current state of their Cowork plugin ecosystem. Also triggers when any plugin produces output that could inform what plugin to build next. This skill is the TRUTH phase of THE LOOP — it perceives what IS before meaning can be extracted. It reads not just structure but scars, grammar, traces, and the living relationships between plugins that structural analysis alone misses. This is a trace-aware analysis that comprehends how past agents thought about the ecosystem.

2026-02-11

plugin-architect

Create complete Cowork plugins through HOLD:AGENT:HOLD architecture and THE FORGE PROCESS. Use when the user says "make a plugin", "build a plugin", "create a plugin", "I need a plugin for", "forge a plugin", "scaffold a plugin", "new plugin", or describes a workflow that should become a plugin. Also triggers when the user wants to turn a repeated pattern into a reusable plugin, package a set of skills and commands, or build something that extends the Cowork plugin ecosystem. Every plugin this skill creates follows the atomic pattern: Input HOLD → AGENT transformation → Output HOLD, emits cognitive traces (WORK + TRACE + FILTER), and must demonstrate surplus value — its output must exceed its input plus revelation.

2026-02-11

recursive-engine

The recursive loop (ALPHA:OMEGA) that drives plugin evolution through THE FURNACE metabolic cycle. Each plugin this engine creates feeds its outputs AND TRACES back through TRUTH:MEANING:CARE to determine what plugin to build next. Use when the user says "evolve", "what's next", "keep going", "build the next one", "recursive loop", "plugin chain", "what does the ecosystem need now", "molt", or when any plugin's output reveals a gap that a new plugin could fill. This is THE LOOP — it doesn't just create plugins, it creates the conditions for the next plugin to emerge from the outputs of the last one. Every cycle must produce surplus value (output exceeds input) and emit cognitive traces (WORK + TRACE + FILTER). The loop never ends; it molts.

2026-02-11

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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

connector-skill

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Data Connector Skill

Query Patterns

SQL-based Sources

API-based Sources

File-based Sources

Validation Checklist

Trace Awareness

References

Data Connector Skill

Query Patterns

SQL-based Sources

API-based Sources

File-based Sources

Validation Checklist

Trace Awareness

References