fix(skills): remove phantom resource references and fix CoC links (#447)

Remove references to non-existent resource files (references/, assets/,
scripts/, examples/) from 115 skill SKILL.md files. These sections
pointed to directories and files that were never created, causing
confusion when users install skills.

Also fix broken Code of Conduct links in issue templates to use
absolute GitHub URLs instead of relative paths that 404.
This commit is contained in:
Seth Hobson
2026-03-07 10:53:17 -05:00
parent ade0c7a211
commit 47a5dbc3f9
118 changed files with 3 additions and 1492 deletions

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@@ -517,9 +517,3 @@ airflow/
- **Don't use global state** - Tasks should be stateless
- **Don't skip catchup blindly** - Understand implications
- **Don't put heavy logic in DAG file** - Import from modules
## Resources
- [Airflow Documentation](https://airflow.apache.org/docs/)
- [Astronomer Guides](https://docs.astronomer.io/learn)
- [TaskFlow API](https://airflow.apache.org/docs/apache-airflow/stable/tutorial/taskflow.html)

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@@ -581,10 +581,3 @@ if not all(r.passed for r in results.values()):
- **Don't skip freshness** - Stale data is bad data
- **Don't hardcode thresholds** - Use dynamic baselines
- **Don't test in isolation** - Test relationships too
## Resources
- [Great Expectations Documentation](https://docs.greatexpectations.io/)
- [dbt Testing Documentation](https://docs.getdbt.com/docs/build/tests)
- [Data Contract Specification](https://datacontract.com/)
- [Soda Core](https://docs.soda.io/soda-core/overview.html)

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@@ -554,10 +554,3 @@ dbt ls --select tag:critical # List models by tag
- **Don't repeat logic** - Extract to macros
- **Don't test in prod** - Use dev target
- **Don't ignore freshness** - Monitor source data
## Resources
- [dbt Documentation](https://docs.getdbt.com/)
- [dbt Best Practices](https://docs.getdbt.com/guides/best-practices)
- [dbt-utils Package](https://hub.getdbt.com/dbt-labs/dbt_utils/latest/)
- [dbt Discourse](https://discourse.getdbt.com/)

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@@ -409,9 +409,3 @@ spark_configs = {
- **Don't over-cache** - Memory is limited
- **Don't ignore data skew** - It dominates job time
- **Don't use `.count()` for existence** - Use `.take(1)` or `.isEmpty()`
## Resources
- [Spark Performance Tuning](https://spark.apache.org/docs/latest/sql-performance-tuning.html)
- [Spark Configuration](https://spark.apache.org/docs/latest/configuration.html)
- [Databricks Optimization Guide](https://docs.databricks.com/en/optimizations/index.html)