* feat(skill): cross-pollinate the new tool wave into catalog routing Wire the recent tool skill wave into the two-layer routing graph so the new tool skills are reachable from the methodology skills that own their domains, and vice versa: - methodology -> tool down-routes: platform-engineering -> kubernetes, terraform, telemetry, postgres, grafana; site-reliability-engineering -> telemetry, grafana; data-engineering and backend-engineering -> postgres; frontend-engineering -> mobile-development; verification-methodology -> playwright, documents; technical-documentation -> documents - neckbeard: add mobile-development and documents routing rows plus change-surface coverage entries, and cross-link the lightweight test-hardening path to qa-methodology's bounded mutation-review material - collaboration layer: chief-of-staff-methodology -> slack/notion/email, go-to-market -> crm, conditional-customer-success -> crm; fix the dead seo-content-optimization reference in go-to-market (now seo-audit) - references/skill-triggers.md: add trigger rows for the 14 new skills - bring go-to-market's description up to the quality validator's imperative-verb + negative-boundary requirement and regenerate catalogs Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> * fix(skill): add eval manifest for technical-documentation The eval-coverage ratchet fails on modified skills without a schema-valid manifest once coverage passes 50%. technical-documentation was modified by the routing cross-pollination change and lacked one; add six output-quality cases covering README authorship, API reference generation, CLI help design, agent-facing docs, documentation-site IA, and troubleshooting sections. Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com> --------- Co-authored-by: factory-droid[bot] <138933559+factory-droid[bot]@users.noreply.github.com>
Data Engineering
Data engineering methodology — database operations (vector, relational, graph, time-series), ETL/ELT pipeline design (dbt patterns, incremental loading), SQL analytical patterns, data quality monitoring, schema migration, and storage infrastructure management. Grounded in operational patterns for production data systems.
Why Install This Skill
Your agent gets operational patterns for production data systems — real SQL, dbt models, backup commands, and migration strategies instead of textbook theory.
What You Get
| Directory | Purpose |
|---|---|
SKILL.md |
Core methodology, trigger conditions, reference index |
references/ |
Deep-dive reference files loaded on demand |
Triggers
Designing ETL/ELT pipelines, writing analytical SQL, operating vector/graph/time-series databases, planning migrations, or setting up data quality monitoring.
Requirements
Platform-agnostic. References cover PostgreSQL, DuckDB, ClickHouse, BigQuery, Snowflake, Neo4j, InfluxDB, TimescaleDB, and dbt.
Quick Start
Load SKILL.md for the methodology overview and reference table, then load specific references as needed for the task at hand.