refactor: rename asset to entity across scripts, docs, and comments

Replace all user-facing and internal references to "asset" with
"entity" to align with the new Entity+Edge data model. This includes
Tengo script variables (asset → entity, new_asset → new_entity),
processor/sink code, test fixtures, READMEs, and documentation.

Author: ravisuhag

agent/agent_test.go

@@ -930,7 +930,7 @@ func TestAgentRun(t *testing.T) {
 						"engine": "tengo",
 						"script": heredoc.Doc(`
 							text := import("text")
-							asset.description = text.replace(asset.description, "incredible", "amazing", -1)
+							entity.description = text.replace(entity.description, "incredible", "amazing", -1)
 						`),
 					},
 				},

docs/concepts/context_graph.mdx

@@ -12,9 +12,9 @@ Meteor closes this gap. By continuously extracting metadata — schemas, lineage
 
 ## What is a Context Graph?
 
-A context graph is a connected, queryable representation of your data ecosystem. It captures not just what assets exist, but how they relate to each other:
+A context graph is a connected, queryable representation of your data ecosystem. It captures not just what entities exist, but how they relate to each other:
 
-- **Nodes** represent assets: tables, dashboards, jobs, topics, models, buckets, users, repositories, teams, groups.
+- **Nodes** represent entities: tables, dashboards, jobs, topics, models, buckets, users, repositories, teams, groups.
 - **Edges** represent relationships: lineage (which table feeds which dashboard), ownership (who is responsible), membership (who belongs to which team), and dependency (which job produces which dataset).
 
 Unlike a flat catalog or a search index, a context graph preserves **structure**. It knows that a revenue dashboard depends on a sales table, which is produced by an ETL job, which reads from a Kafka topic. This structure is what makes AI useful over enterprise data.
@@ -34,10 +34,10 @@ Meteor's extractors connect to 30+ data sources — databases (BigQuery, Postgre
 
 ### Process
 
-Processors enrich and transform assets in-flight before they reach a sink. Use them to:
+Processors enrich and transform entities in-flight before they reach a sink. Use them to:
 
 - Append **labels** for classification (environment, domain, sensitivity, PII)
-- **Enrich** assets with custom fields from external systems
+- **Enrich** entities with custom fields from external systems
 - Run **scripts** (Tengo) for arbitrary transformation logic, including HTTP calls to external APIs
 
 ### Deliver
@@ -77,7 +77,7 @@ With Meteor's lineage graph, AI can:
 - **Root-cause analysis** — "This metric dropped. What changed upstream?"
 - **Dependency awareness** — "Before deprecating this dataset, show me everything downstream."
 
-### Asset Discovery for AI Agents
+### Entity Discovery for AI Agents
 
 Function-calling AI agents need to know what tools and data are available. The context graph serves as the agent's **world model**:
 
@@ -96,10 +96,10 @@ Meteor's plugin architecture makes it straightforward to extend the context grap
 
 | Capability | Approach |
 |---|---|
-| **Semantic search** | Use a script processor to generate vector embeddings from asset descriptions, enabling similarity-based retrieval |
-| **Business glossary** | Extract metric definitions and business terms as first-class assets, linking them to underlying tables |
-| **Usage signals** | Build extractors that capture query frequency and dashboard views, helping AI rank assets by relevance |
-| **Data quality** | Enrich assets with freshness, completeness, and anomaly scores so AI can assess data trustworthiness |
+| **Semantic search** | Use a script processor to generate vector embeddings from entity descriptions, enabling similarity-based retrieval |
+| **Business glossary** | Extract metric definitions and business terms as first-class entities, linking them to underlying tables |
+| **Usage signals** | Build extractors that capture query frequency and dashboard views, helping AI rank entities by relevance |
+| **Data quality** | Enrich entities with freshness, completeness, and anomaly scores so AI can assess data trustworthiness |
 | **LLM-optimized exports** | Create sinks that format metadata as structured context windows sized for LLM consumption |
 
 ## The Flywheel
@@ -109,7 +109,7 @@ The context graph is not a one-time build. It is a continuously improving loop:
 1. **Meteor extracts** metadata from across the data ecosystem
 2. **The context graph** grows richer with each extraction cycle
 3. **AI systems** use the graph for grounding, reasoning, and discovery
-4. **AI interactions** reveal gaps — missing descriptions, unknown lineage, unlabeled assets
+4. **AI interactions** reveal gaps — missing descriptions, unknown lineage, unlabeled entities
 5. **Teams fill gaps**, improving metadata quality
 6. **Meteor captures** the improvements, and the cycle continues
 

docs/concepts/processor.mdx

@@ -62,7 +62,7 @@ processors:
     config:
       engine: tengo
       script: |
-        asset.name = asset.name + " (processed)"
+        entity.name = entity.name + " (processed)"
 ```
 
 ## Writing a Recipe with Processors
@@ -88,7 +88,7 @@ processors:
     config:
       engine: tengo
       script: |
-        asset.name = asset.name + " [" + asset.source + "]"
+        entity.name = entity.name + " [" + entity.source + "]"
 ```
 
 In this example, each entity first gets enriched with a `domain` attribute, then gets labeled with `source: meteor`, and finally has its name modified by the script processor.

docs/examples/index.mdx

@@ -24,7 +24,7 @@ export METEOR_BIGQUERY_PROJECT_ID=my-gcp-project
 meteor run ./bigquery-kafka-recipe.yaml
 ```
 
-Extracts BigQuery metadata with column profiling enabled and streams it to a Kafka topic. The `key_path` config keys each message by asset URN for consistent partitioning.
+Extracts BigQuery metadata with column profiling enabled and streams it to a Kafka topic. The `key_path` config keys each message by entity URN for consistent partitioning.
 
 ## Postgres to Compass
 
@@ -46,7 +46,7 @@ export METEOR_KAFKA_BROKERS=broker1:9092,broker2:9092
 meteor run ./bigquery-multi-sink.yaml
 ```
 
-Demonstrates multiple sinks and chained processors. Extracts BigQuery metadata with column profiling and view lineage, enriches and labels assets, then sends to both Compass and Kafka simultaneously.
+Demonstrates multiple sinks and chained processors. Extracts BigQuery metadata with column profiling and view lineage, enriches and labels entities, then sends to both Compass and Kafka simultaneously.
 
 ## Tableau to File
 

docs/guides/troubleshooting.mdx

@@ -69,7 +69,7 @@ Common issues and solutions when working with Meteor.
 
 ### Empty results
 
-**Symptom:** Recipe runs successfully but produces no assets.
+**Symptom:** Recipe runs successfully but produces no entities.
 
 **Solutions:**
 - Check that the source has data accessible with the provided credentials.
@@ -79,12 +79,12 @@ Common issues and solutions when working with Meteor.
 
 ### Partial extraction
 
-**Symptom:** Some assets are extracted but others are missing.
+**Symptom:** Some entities are extracted but others are missing.
 
 **Solutions:**
 - Check the `exclude` configuration in your recipe.
 - For database extractors, ensure the user has permissions on all target schemas.
-- Check Meteor logs for per-asset errors that may indicate permission issues on specific resources.
+- Check Meteor logs for per-entity errors that may indicate permission issues on specific resources.
 
 ## Sink Issues
 

docs/reference/configuration.mdx

@@ -115,8 +115,8 @@ For complete configuration details, refer to the [sinks reference](./sinks).
 
 | Processor | Key Config Fields |
 | :-------- | :---------------- |
-| `enrich` | `attributes` — key-value map to merge into asset attributes |
-| `labels` | `labels` — key-value map to append to asset labels |
+| `enrich` | `attributes` — key-value map to merge into entity properties |
+| `labels` | `labels` — key-value map to append to entity properties labels |
 | `script` | `engine`, `script` — Tengo script for custom transformation |
 
 For complete configuration details, refer to the [processors reference](./processors).

docs/reference/processors.mdx

@@ -70,15 +70,15 @@ processors:
 
 ## script
 
-Transforms the entity using a user-defined script. The entity is exposed to the script as an `asset` variable (a map). Currently [Tengo][tengo] is the only supported engine. The script has full control over entity fields, including the ability to make HTTP calls to external services.
+Transforms the entity using a user-defined script. The entity is exposed to the script as an `entity` variable (a map). Currently [Tengo][tengo] is the only supported engine. The script has full control over entity fields, including the ability to make HTTP calls to external services.
 
 ```yaml
 processors:
   - name: script
     config:
       engine: tengo
       script: |
-        asset.name = asset.name + " (processed)"
+        entity.name = entity.name + " (processed)"
 ```
 
 | Key | Type | Description | Required |

plugins/extractors/bigquery/bigquery.go

@@ -47,7 +47,7 @@ type Config struct {
 	TablePattern         string  `mapstructure:"table_pattern"`
 	Exclude              Exclude `mapstructure:"exclude"`
 	IncludeColumnProfile bool    `mapstructure:"include_column_profile"`
-	// MaxPreviewRows can also be set to -1 to restrict adding preview_rows key in asset data
+	// MaxPreviewRows can also be set to -1 to restrict adding preview_rows key in entity properties
 	MaxPreviewRows      int      `mapstructure:"max_preview_rows" default:"30"`
 	MixValues           bool     `mapstructure:"mix_values" default:"false"`
 	IsCollectTableUsage bool     `mapstructure:"collect_table_usage" default:"false"`
@@ -336,7 +336,7 @@ func (e *Extractor) extractTable(ctx context.Context, ds *bigquery.Dataset, emit
 				}
 				record, err := e.buildRecord(ctx, table, tmd)
 				if err != nil {
-					e.logger.Error("failed to build asset", "err", err, "table", tableFQN)
+					e.logger.Error("failed to build record", "err", err, "table", tableFQN)
 					return nil
 				}
 				emit(record)

plugins/extractors/bigtable/bigtable.go

@@ -120,7 +120,7 @@ func (e *Extractor) Init(ctx context.Context, config plugins.Config) error {
 
 // Extract checks if the extractor is configured and
 // if so, then extracts the metadata and
-// returns the assets.
+// returns the records.
 func (e *Extractor) Extract(ctx context.Context, emit plugins.Emit) error {
 	return e.getTablesInfo(ctx, emit)
 }

plugins/extractors/grafana/grafana.go

@@ -98,7 +98,7 @@ func (e *Extractor) Init(ctx context.Context, config plugins.Config) error {
 }
 
 // Extract checks if the extractor is configured and
-// if so, then it extracts the assets from the extractor.
+// if so, then it extracts the metadata from the extractor.
 func (e *Extractor) Extract(ctx context.Context, emit plugins.Emit) error {
 	if e.extract["datasources"] {
 		if err := e.extractDatasources(ctx, emit); err != nil {