Indexed The index holds package records for this repository. The package count is historical registry records; it does not mean those packages are available now.
Repository
Canonical name
databricks/databricks-agent-skills
GitHub repository ID
1134135741
Package records
127 (historical registry records, not current availability)
Discovery and queue
First recorded source
marketplace:anthropics/claude-plugins-official
Discovered
Queue state
ok
Recorded errors
0
Last settled processing
Next-check eligibility
Eligible from
The first recorded source is what the queue stored first, not the full discovery history. The error count increases when processing ends in the error state and resets after a successful settlement; it is not a count of all attempts. “Last settled processing” is when it finished, not when it started.
Eligibility is calculated as of . It is not a schedule: each collector run handles only a limited number of sources, and being eligible does not promise when a check happens.
Last saved scan
Repository metadata read
Scan status
complete
Scan saved
Extractor version
2
Continuation record
None
The scan record is separate from the queue state: the queue state shows processing progress, the scan record shows what was last saved. A continuation record only means unfinished state is stored; it has no remaining count.
Source-file evidence coverage
Raw counts of the pinned source files that stored package versions reference in this repository. They show what has been recorded, not how complete the repository is.
Scope and limits
Counts distinct pinned file links (repository, commit, path) across all stored versions, from any package, that name exactly this repository's canonical name. Files of packages owned by this repository are not counted unless some version links to them here. Alias names are not merged and no history is guessed from a current name.
“Known” means a regular-file identity is recorded. It does not mean a whole directory or package is covered. Links that are not valid pinned file URLs are not counted.
194 pinned source files referenced
194 known
0 absent at commit
0 file listing truncated
0 unsupported
0 not recorded
0 read failed
Deduplicated by repository, commit and path; the same path at two commits counts as two file identities. These are not component-group counts.
Failed source-commit reads 0
No source commit has a stored failed read for files that are still unrecorded.
Linked packages 127
Linked by verified repository identity. The count is historical registry records, not current availability.
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
Develop and deploy Lakeflow Jobs on Databricks via DABs, Python SDK, or the CLI. Use when creating data engineering jobs with notebooks, Python wheels, SQL, dbt, or pipelines. Invoke BEFORE starting implementation.
Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.
Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.
Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.
Databricks Lakebase Postgres: projects, scaling, connectivity, Lakebase synced tables, and Data API. Use when asked about Lakebase databases, OLTP storage, or connecting apps to Postgres on Databricks.
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; Post…
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; Post…
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; Post…
Build managed ingestion pipelines into Databricks using Lakeflow Connect. Use when ingesting from SaaS apps (Salesforce, Workday Reports, ServiceNow, Google Analytics 4, HubSpot, Confluence) or databases (SQL Server cloud and on-prem; Post…
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions acros…
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions acros…
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions acros…
Unity Catalog metric views: define, create, query, and manage governed business metrics in YAML. Use when building standardized KPIs, revenue metrics, order analytics, or any reusable business metrics that need consistent definitions acros…
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-…
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-…
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-…
Train ML models on Databricks. Use for: classification/regression/deep-learning (XGBoost, scikit-learn, LightGBM, PyTorch) with Optuna, @prod/@challenger aliases, batch scoring (spark_udf for plain models, fe.score_batch for feature-store-…
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up tra…
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up tra…
When last recorded, these names pointed to this repository. This is a recorded resolution, not a live GitHub check; packages and metadata are never merged because of it.
No aliases pointing to this repository are recorded.
Unsupported marketplace entries 0
Entries in this repository's marketplace that this registry does not support yet. They are preserved source data and are never fetched or executed.