{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"elementary","slug":"elementary","name":"Elementary","type":"repo","url":"https://github.com/elementary-data/elementary","page_url":"https://unfragile.ai/elementary","categories":["data-pipelines","testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"elementary__cap_0","uri":"capability://data.processing.analysis.dbt.native.anomaly.detection.via.statistical.test.generation","name":"dbt-native anomaly detection via statistical test generation","description":"Elementary generates dbt test macros that collect time-series metrics (row counts, freshness, schema changes) directly within dbt runs and apply statistical anomaly detection algorithms (z-score, IQR, moving average baselines) to flag deviations. Tests execute natively in dbt's DAG, storing results in Elementary's metadata schema, eliminating separate monitoring infrastructure and enabling anomalies to fail dbt runs.","intents":["Detect when table row counts drop unexpectedly without external monitoring tools","Flag schema changes (new/dropped columns) automatically during dbt runs","Identify stale data by tracking table freshness metrics over time","Set up anomaly detection without leaving the dbt ecosystem"],"best_for":["dbt-centric data teams wanting observability without external platforms","teams with strict data governance requiring anomalies to block deployments","organizations preferring configuration-as-code over UI-based rule builders"],"limitations":["Anomaly detection baselines require historical data (typically 7-30 days minimum) before meaningful detection","Statistical methods (z-score, IQR) assume normal distributions; skewed metrics may produce false positives","No built-in ML-based forecasting; relies on simple statistical models rather than ARIMA or Prophet","Requires dbt runs to complete for metric collection; cannot detect anomalies between scheduled runs"],"requires":["dbt 1.0+","Elementary dbt package installed in dbt_packages","Supported data warehouse (Snowflake, BigQuery, Redshift, Databricks, Postgres)","Python 3.8+ for Elementary CLI"],"input_types":["dbt model definitions (YAML)","dbt test results (JSON artifacts)","warehouse metadata (schema, row counts, freshness timestamps)"],"output_types":["dbt test results (pass/fail with anomaly severity)","Elementary metadata tables (anomaly_detection_results, metric_values)","Alert payloads (JSON) for downstream systems"],"categories":["data-processing-analysis","anomaly-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_1","uri":"capability://data.processing.analysis.dbt.test.result.aggregation.and.impact.lineage.tracking","name":"dbt test result aggregation and impact lineage tracking","description":"Elementary's dbt package and CLI parse dbt artifacts (manifest.json, run_results.json) to extract test metadata, execution times, and failure reasons, then correlates test failures with downstream model dependencies to surface which datasets are affected. Stores test lineage in Elementary's metadata schema, enabling root-cause analysis by tracing failures upstream through the DAG.","intents":["Understand which downstream models are impacted when a dbt test fails","Identify the root cause of data quality issues by tracing failures through the dependency graph","Aggregate test results across multiple dbt runs for historical trend analysis","Prioritize which test failures to fix based on downstream impact"],"best_for":["data teams with complex dbt DAGs (50+ models) needing impact analysis","organizations requiring root-cause analysis for data incidents","teams using dbt test-driven development wanting visibility into test coverage"],"limitations":["Lineage analysis limited to dbt DAG; cannot trace impacts into downstream BI tools or ML pipelines","Test failure reasons extracted from dbt logs; custom test logic may not produce parseable error messages","No real-time impact analysis; lineage computed post-run, not during execution","Column-level lineage only available in Elementary Cloud; open-source limited to model-level"],"requires":["dbt 1.0+ with manifest.json and run_results.json artifacts enabled","Elementary dbt package for metadata collection","Elementary CLI (edr) for artifact parsing and lineage computation"],"input_types":["dbt manifest.json (DAG structure, model definitions)","dbt run_results.json (test execution results, timing)","dbt logs (test failure messages)"],"output_types":["Elementary metadata tables (test_results, model_execution_results)","Lineage graphs (JSON/GraphQL in Cloud; HTML in reports)","Impact summaries (which models affected by test failures)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_10","uri":"capability://tool.use.integration.elementary.cloud.synchronization.and.team.collaboration","name":"elementary cloud synchronization and team collaboration","description":"Elementary Cloud provides a managed SaaS platform that syncs monitoring data from open-source Elementary instances, enabling team collaboration, centralized dashboards, and advanced features (column-level lineage, AI-powered tests, team management). Cloud instances pull data from warehouse via Elementary CLI's `send-report` command or push via API, maintaining data residency while providing collaborative UI.","intents":["Share data quality dashboards across teams without self-hosting","Enable non-technical stakeholders to explore data quality without CLI access","Centralize monitoring across multiple dbt projects or warehouses","Access advanced features (column-level lineage, AI tests) without self-hosting infrastructure"],"best_for":["organizations wanting managed observability without self-hosting","teams with multiple dbt projects needing centralized visibility","enterprises requiring team-based access control and audit logs"],"limitations":["Elementary Cloud requires SaaS subscription; no self-hosted Cloud option","Data sync is asynchronous (via CLI push); no real-time dashboard updates","Advanced features (AI tests, column-level lineage) only in Cloud; not available in open-source","Cloud instance requires network access to warehouse for metadata queries"],"requires":["Elementary Cloud subscription","Elementary CLI (edr) for data synchronization","dbt artifacts (manifest.json, run_results.json)","Warehouse credentials for Cloud instance to query metadata"],"input_types":["Elementary metadata tables (test_results, anomaly_detection_results)","dbt artifacts (manifest.json, run_results.json)","Warehouse metadata (schema, row counts)"],"output_types":["Cloud dashboard (web UI with test results, anomalies, lineage)","Team collaboration features (comments, alerts, ownership)","API access to monitoring data (for custom integrations)"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_11","uri":"capability://safety.moderation.anonymous.usage.tracking.and.telemetry.collection","name":"anonymous usage tracking and telemetry collection","description":"Elementary CLI collects anonymous telemetry (command usage, feature adoption, error rates) via optional tracking module (elementary/tracking/tracking_interface.py) to inform product development. Tracking is opt-out and does not collect sensitive data (SQL, credentials, table names), enabling Elementary team to understand adoption patterns without compromising user privacy.","intents":["Help Elementary team understand which features are most used","Identify common error patterns to prioritize bug fixes","Measure adoption of new features across the user base","Inform product roadmap based on actual usage data"],"best_for":["open-source projects wanting to understand user behavior","teams contributing to Elementary wanting to see impact"],"limitations":["Tracking is opt-out, not opt-in; requires explicit disabling for privacy-conscious users","Telemetry data sent to Elementary's servers; no on-premises alternative","No granular control over which events are tracked; all-or-nothing opt-out","Tracking adds network latency to CLI execution (typically <100ms)"],"requires":["Network access to Elementary's telemetry endpoint","No explicit configuration; tracking enabled by default"],"input_types":["CLI command execution (command name, arguments, execution time)","Error logs (error type, stack trace, context)"],"output_types":["Telemetry events (JSON) sent to Elementary's analytics backend"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_12","uri":"capability://automation.workflow.configuration.as.code.monitoring.setup.via.dbt.yaml","name":"configuration-as-code monitoring setup via dbt yaml","description":"Elementary enables teams to define monitoring configuration (anomaly detection thresholds, freshness SLAs, alert routing) directly in dbt YAML files using the 'meta' field on models and columns. This approach treats monitoring configuration as code, enabling version control, code review, and reproducible monitoring setups. Configuration includes owner tags (meta.owner), anomaly detection parameters (meta.anomaly_detection), and custom metric definitions. The dbt package reads this configuration during runs to apply monitoring logic without separate configuration files.","intents":["Define monitoring configuration alongside model definitions in dbt YAML","Version control monitoring configuration with dbt code","Enable code review workflows for monitoring changes","Avoid separate configuration management systems (ConfigMaps, environment variables)"],"best_for":["Teams practicing infrastructure-as-code and wanting monitoring-as-code","Organizations with strict code review and change management processes","dbt users comfortable with YAML configuration and version control workflows"],"limitations":["Configuration is limited to dbt YAML syntax — complex monitoring logic cannot be expressed in YAML","Changes to monitoring configuration require dbt run to take effect — no real-time configuration updates","YAML configuration is not validated until dbt run — syntax errors are caught late in the pipeline","Configuration is stored in dbt project repository — requires dbt project access to modify monitoring settings"],"requires":["dbt 1.0+ with YAML model definitions","Elementary dbt package installed","dbt project repository with version control (Git)"],"input_types":["dbt model YAML files (dbt_project.yml, models/*.yml)","Meta field configuration (owner, anomaly_detection, metrics)"],"output_types":["Applied monitoring configuration (read by Elementary dbt package)","Monitoring metadata stored in warehouse tables"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_2","uri":"capability://automation.workflow.automated.data.quality.report.generation.and.distribution","name":"automated data quality report generation and distribution","description":"Elementary CLI's `report` command generates a self-contained HTML dashboard aggregating test results, anomaly detections, model performance metrics, and data lineage into a single interactive report. The `send-report` command distributes reports via Slack, Teams, email, or uploads to S3/GCS, enabling async sharing of data quality status without requiring dashboard access.","intents":["Share data quality status with non-technical stakeholders via email or Slack","Generate executive summaries of data health without requiring access to dashboards","Archive historical reports for compliance and audit trails","Distribute reports to teams across time zones without real-time dashboard access"],"best_for":["organizations with distributed teams needing async data quality updates","regulated industries requiring audit trails of data quality checks","teams without centralized dashboard infrastructure (self-hosted Elementary)"],"limitations":["HTML reports are static snapshots; no real-time updates or drill-down interactivity","Report generation requires full dbt artifact parsing; large projects (1000+ models) may take 5-10 minutes","Slack/Teams integration limited to message attachments; no interactive buttons or drill-through links","S3/GCS upload requires pre-configured credentials; no built-in access control for shared reports"],"requires":["Elementary CLI (edr) installed","dbt artifacts (manifest.json, run_results.json) from latest dbt run","For Slack/Teams: webhook URL or bot token","For S3/GCS: IAM credentials and bucket access"],"input_types":["dbt artifacts (manifest.json, run_results.json)","Elementary metadata tables (test_results, anomaly_detection_results)","Configuration YAML (report recipients, S3/GCS paths)"],"output_types":["HTML report (self-contained, ~2-5MB for typical projects)","Slack/Teams messages with report link and summary","S3/GCS objects (report HTML + metadata JSON)"],"categories":["automation-workflow","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_3","uri":"capability://data.processing.analysis.multi.warehouse.metadata.extraction.and.normalization","name":"multi-warehouse metadata extraction and normalization","description":"Elementary's warehouse client layer abstracts SQL dialects across Snowflake, BigQuery, Redshift, Databricks, and Postgres, providing a unified interface for querying metadata (table schemas, row counts, freshness timestamps, column statistics). Clients handle dialect-specific syntax for information_schema queries, enabling anomaly detection and lineage analysis to work identically across warehouses without custom logic per platform.","intents":["Run the same dbt package and CLI across multiple warehouse platforms without code changes","Extract schema metadata and freshness information from any supported warehouse","Normalize metadata queries across warehouses with different information_schema implementations","Support multi-warehouse organizations without maintaining separate monitoring per platform"],"best_for":["enterprises with heterogeneous data stacks (Snowflake + BigQuery + Redshift)","organizations migrating between warehouses needing consistent observability","managed service providers supporting multiple customer warehouses"],"limitations":["Supported warehouses limited to 5 platforms; custom warehouses require forking Elementary","Some warehouse-specific features (Snowflake's query history, BigQuery's BI Engine stats) not exposed","Metadata extraction performance varies by warehouse; BigQuery metadata queries slower than Snowflake","No support for data lakes (Delta Lake, Iceberg) without warehouse abstraction layer"],"requires":["Supported warehouse: Snowflake, BigQuery, Redshift, Databricks, or Postgres","Warehouse credentials (service account, API key, or connection string)","Network access from Elementary CLI to warehouse"],"input_types":["Warehouse connection configuration (host, credentials, database)","SQL queries (information_schema, system tables)"],"output_types":["Normalized metadata objects (TableMetadata, ColumnMetadata, FreshnessMetadata)","Row counts, column statistics, schema definitions","Freshness timestamps (last modified, last query time)"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_4","uri":"capability://automation.workflow.configurable.alert.filtering.grouping.and.routing","name":"configurable alert filtering, grouping, and routing","description":"Elementary's alerting system processes test failures and anomalies through a configuration-driven pipeline that filters alerts by severity/tags, groups related failures (e.g., all failures in a data mart), and routes to different channels (Slack, Teams, email) based on owner tags or custom rules. Alert deduplication prevents duplicate notifications for the same failure across multiple runs.","intents":["Route critical data quality failures to on-call engineers immediately","Group related test failures to reduce alert fatigue","Send different alert types to different teams (data engineers vs analytics)","Suppress non-critical alerts during maintenance windows"],"best_for":["organizations with large test suites (100+ tests) needing alert prioritization","teams with multiple stakeholders requiring different alert thresholds","on-call rotations needing escalation rules"],"limitations":["Alert routing based on static owner tags; no dynamic escalation based on time-of-day or on-call schedules","Grouping logic limited to model/tag-based rules; no ML-based anomaly correlation","No built-in alert suppression for known issues; requires manual configuration per incident","Slack/Teams integration sends messages only; no interactive acknowledgment or snooze buttons"],"requires":["Elementary dbt package with alert configuration in YAML","Slack webhook URL or Teams bot token for notifications","Owner tags defined in dbt model/test YAML"],"input_types":["Test results (pass/fail, severity)","Anomaly detection results (anomaly score, baseline)","Alert configuration YAML (filters, routing rules, owner tags)"],"output_types":["Slack/Teams messages (formatted alerts with context)","Alert metadata (grouped alerts, routing decisions)","Alert history (for deduplication and trend analysis)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_5","uri":"capability://automation.workflow.dbt.selector.based.model.and.test.filtering","name":"dbt selector-based model and test filtering","description":"Elementary CLI implements dbt's selector syntax (--select, --exclude, --selector) to filter which models and tests to monitor, enabling targeted monitoring of specific data marts or critical paths. Selector evaluation happens during CLI execution, allowing users to run `edr monitor --select tag:critical` to monitor only critical models without modifying dbt configuration.","intents":["Monitor only critical data models without running observability on the entire DAG","Run focused monitoring during development on a subset of models","Exclude staging models from alerting while monitoring production models","Create monitoring profiles for different teams (analytics vs engineering)"],"best_for":["large dbt projects (500+ models) needing selective monitoring","teams with different monitoring requirements per data mart","development workflows requiring fast feedback on specific models"],"limitations":["Selector syntax limited to dbt's built-in operators (tag, path, package); no custom selector logic","Selector evaluation happens at CLI runtime; no pre-computed selector caching","No UI for building selectors; requires command-line expertise or documentation"],"requires":["dbt 1.0+ with selector definitions in dbt_project.yml (optional)","Elementary CLI (edr)","Knowledge of dbt selector syntax"],"input_types":["dbt manifest.json (model definitions, tags, paths)","CLI arguments (--select, --exclude, --selector)"],"output_types":["Filtered model/test list (JSON)","Monitoring results for selected models only"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_6","uri":"capability://data.processing.analysis.schema.change.detection.and.column.level.monitoring","name":"schema change detection and column-level monitoring","description":"Elementary's schema monitoring tests detect additions, deletions, and type changes in table columns by comparing current schema against historical snapshots stored in Elementary's metadata tables. Tests execute natively in dbt, flagging schema changes as test failures and enabling alerts when unexpected schema modifications occur (e.g., column drops in production).","intents":["Detect when upstream sources add or remove columns unexpectedly","Alert when column data types change (e.g., string to numeric)","Prevent downstream models from breaking due to schema changes","Track schema evolution over time for compliance and documentation"],"best_for":["organizations with external data sources prone to schema changes","teams needing early warning of breaking changes before models fail","regulated industries requiring schema change audit trails"],"limitations":["Schema change detection requires baseline snapshot; first run produces no alerts","Cannot distinguish intentional schema changes from accidental ones without manual review","No automatic schema migration suggestions; requires manual model updates","Column-level lineage impact analysis only in Elementary Cloud"],"requires":["Elementary dbt package with schema monitoring tests","Historical schema snapshots in Elementary metadata tables (requires prior runs)"],"input_types":["Current table schema (from information_schema)","Historical schema snapshots (from Elementary metadata)"],"output_types":["Schema change test results (pass/fail with change details)","Schema change alerts (added/removed/modified columns)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_7","uri":"capability://data.processing.analysis.model.execution.performance.tracking.and.sla.monitoring","name":"model execution performance tracking and sla monitoring","description":"Elementary tracks dbt model execution metrics (runtime, row counts, resource utilization) across runs and compares against configurable SLAs (e.g., 'model must complete in <5 minutes'). Performance degradation is detected via statistical analysis of historical runtimes, enabling alerts when models exceed expected execution time or resource consumption.","intents":["Detect when models start running slower than historical baseline","Alert when models consume unexpected amounts of compute resources","Track SLA compliance for critical models over time","Identify performance regressions introduced by dbt code changes"],"best_for":["organizations with large-scale dbt projects (1000+ models) needing performance visibility","teams with compute cost constraints (BigQuery, Snowflake credits)","SLA-driven organizations requiring performance guarantees"],"limitations":["Performance metrics limited to dbt execution time; no query-level profiling or resource breakdown","SLA thresholds must be manually configured; no automatic baseline learning","Performance tracking requires multiple runs for meaningful baselines (7-30 days)","No cost estimation; tracks execution time but not compute credits or cloud costs"],"requires":["Elementary dbt package for metric collection","Multiple dbt runs (7+ days) for baseline establishment","SLA configuration in dbt YAML"],"input_types":["dbt run_results.json (execution times, row counts)","Warehouse query logs (resource utilization, if available)","SLA configuration YAML"],"output_types":["Performance metrics (runtime, row counts, resource usage)","SLA compliance reports (pass/fail vs thresholds)","Performance trend analysis (JSON, HTML)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_8","uri":"capability://data.processing.analysis.data.freshness.tracking.and.staleness.alerting","name":"data freshness tracking and staleness alerting","description":"Elementary monitors table freshness by tracking the timestamp of the last data update (via dbt's `updated_at` metadata or warehouse-specific last_modified timestamps). Freshness tests compare current time against the last update timestamp and alert when data exceeds configured freshness SLAs (e.g., 'data must be updated within 24 hours').","intents":["Alert when critical tables become stale (e.g., daily refresh didn't run)","Track SLA compliance for data freshness across the organization","Identify which tables are at risk of staleness before they breach SLAs","Monitor external data source freshness without access to source systems"],"best_for":["organizations with time-sensitive analytics (daily/hourly refreshes)","teams supporting multiple downstream consumers with different freshness requirements","regulated industries requiring data freshness audit trails"],"limitations":["Freshness tracking depends on reliable `updated_at` timestamps; missing or incorrect timestamps produce false positives","Cannot distinguish between intentional pauses and actual failures without manual configuration","Freshness SLAs must be manually configured per table; no automatic SLA learning","No root-cause analysis; alerts only indicate staleness, not why refresh failed"],"requires":["Elementary dbt package with freshness tests","Reliable `updated_at` or `dbt_updated_at` column in tables","Freshness SLA configuration in dbt YAML"],"input_types":["Table metadata (last_modified timestamp from information_schema)","dbt model metadata (updated_at column)","Freshness SLA configuration YAML"],"output_types":["Freshness test results (pass/fail with staleness duration)","Freshness alerts (hours/days since last update)","Freshness trend reports (historical SLA compliance)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__cap_9","uri":"capability://tool.use.integration.dbt.cloud.and.dbt.core.integration.with.artifact.parsing","name":"dbt cloud and dbt core integration with artifact parsing","description":"Elementary CLI parses dbt artifacts (manifest.json, run_results.json) generated by both dbt Cloud and dbt Core, extracting test results, model metadata, and execution logs. Supports both local artifact paths and remote dbt Cloud API integration, enabling Elementary to work with any dbt deployment model without requiring dbt-specific SDKs.","intents":["Use Elementary with dbt Cloud without additional configuration","Parse dbt artifacts from local dbt Core runs for self-hosted monitoring","Integrate Elementary into CI/CD pipelines that generate dbt artifacts","Support hybrid dbt deployments (Cloud + Core) with unified monitoring"],"best_for":["organizations using dbt Cloud wanting open-source observability","teams with self-hosted dbt Core needing monitoring without SaaS","CI/CD-driven dbt deployments requiring artifact-based monitoring"],"limitations":["dbt Cloud integration requires API token; no OAuth or service account support","Artifact parsing limited to manifest.json and run_results.json; no access to dbt Cloud's query history or performance insights","Local artifact paths require manual file management; no automatic artifact discovery","No real-time monitoring; Elementary processes artifacts post-run, not during execution"],"requires":["dbt 1.0+ (Core or Cloud)","dbt artifacts (manifest.json, run_results.json) from latest run","For dbt Cloud: API token and project/job IDs"],"input_types":["dbt manifest.json (DAG, model definitions, test configurations)","dbt run_results.json (test execution results, timing)","dbt Cloud API responses (if using Cloud integration)"],"output_types":["Parsed test results (test name, status, execution time)","Model metadata (description, tags, owners)","Execution logs (error messages, warnings)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"elementary__headline","uri":"capability://data.processing.analysis.dbt.native.data.observability.platform","name":"dbt-native data observability platform","description":"Elementary is an open-source data observability platform designed specifically for dbt, enabling data teams to monitor data quality, detect anomalies, and ensure the integrity of data pipelines through automated alerts and comprehensive dashboards.","intents":["best data observability tool for dbt","data quality monitoring for dbt projects","how to detect data anomalies in dbt","dbt integration for data quality assurance","top tools for data pipeline monitoring"],"best_for":["data teams","analytics engineers"],"limitations":[],"requires":["dbt"],"input_types":["dbt models","data metrics"],"output_types":["alerts","reports"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["dbt 1.0+","Elementary dbt package installed in dbt_packages","Supported data warehouse (Snowflake, BigQuery, Redshift, Databricks, Postgres)","Python 3.8+ for Elementary CLI","dbt 1.0+ with manifest.json and run_results.json artifacts enabled","Elementary dbt package for metadata collection","Elementary CLI (edr) for artifact parsing and lineage computation","Elementary Cloud subscription","Elementary CLI (edr) for data synchronization","dbt artifacts (manifest.json, run_results.json)"],"failure_modes":["Anomaly detection baselines require historical data (typically 7-30 days minimum) before meaningful detection","Statistical methods (z-score, IQR) assume normal distributions; skewed metrics may produce false positives","No built-in ML-based forecasting; relies on simple statistical models rather than ARIMA or Prophet","Requires dbt runs to complete for metric collection; cannot detect anomalies between scheduled runs","Lineage analysis limited to dbt DAG; cannot trace impacts into downstream BI tools or ML pipelines","Test failure reasons extracted from dbt logs; custom test logic may not produce parseable error messages","No real-time impact analysis; lineage computed post-run, not during execution","Column-level lineage only available in Elementary Cloud; open-source limited to model-level","Elementary Cloud requires SaaS subscription; no self-hosted Cloud option","Data sync is asynchronous (via CLI push); no real-time dashboard updates","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=elementary","compare_url":"https://unfragile.ai/compare?artifact=elementary"}},"signature":"EoIMDtvXl2H71aob3rBxMJhxKS6VocVxKa6F7bOZjSaXPy1YtRcSxf/tXVYa7j6xshNW/ff2sEj8teQ24cdFAA==","signedAt":"2026-06-23T02:07:12.828Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/elementary","artifact":"https://unfragile.ai/elementary","verify":"https://unfragile.ai/api/v1/verify?slug=elementary","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}