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The system likely parses source metadata (table schemas, column types, documentation) and applies templating logic to produce production-ready dbt model definitions with proper naming conventions, materialization settings, and column-level documentation stubs.","intents":["I want to quickly create dbt models from existing database tables without writing YAML by hand","I need to scaffold multiple related models at once with consistent naming and structure","I want to generate model documentation templates that my team can fill in"],"best_for":["Data engineers building dbt projects from scratch or expanding existing ones","Teams with large numbers of source tables needing rapid model onboarding","Organizations standardizing dbt conventions across multiple projects"],"limitations":["Generated models are templates requiring manual refinement for business logic and transformations","Limited to dbt-compatible data warehouses (Snowflake, BigQuery, Redshift, Postgres, etc.)","Cannot infer complex transformation logic from schema alone — requires developer input for CTEs, aggregations, and joins"],"requires":["Active dbt project with configured profiles.yml","Database connection credentials (Snowflake, BigQuery, Redshift, or Postgres)","dbt 1.0 or later","Access to source schema metadata"],"input_types":["database schema metadata","natural language descriptions","existing dbt source definitions"],"output_types":["dbt model SQL files","dbt YAML configuration files","documentation stubs"],"categories":["code-generation-editing","data-modeling"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_1","uri":"capability://data.processing.analysis.automated.lineage.documentation.and.dependency.mapping","name":"automated lineage documentation and dependency mapping","description":"Analyzes dbt project DAGs (directed acyclic graphs) and source-to-model relationships to automatically generate lineage documentation, dependency diagrams, and impact analysis. The system parses dbt manifest.json and parses SQL to extract upstream/downstream dependencies, then renders interactive or static documentation showing data flow, transformation stages, and column-level lineage.","intents":["I need to understand which models depend on a source table I'm modifying","I want to generate lineage diagrams for stakeholder communication without manual drawing","I need to identify orphaned models or unused transformations in my dbt project"],"best_for":["Data teams managing complex dbt projects with 50+ models","Organizations requiring data governance and impact analysis before schema changes","Teams onboarding new engineers who need to understand data architecture quickly"],"limitations":["Lineage accuracy depends on dbt manifest freshness — requires recent dbt runs to reflect current state","Cannot trace lineage through external tools (Airflow, Spark jobs) outside dbt ecosystem","Column-level lineage limited to explicit dbt ref() and source() calls; implicit joins or dynamic SQL may not be fully captured","No real-time lineage updates — requires manual dbt parse/run cycle to refresh"],"requires":["dbt project with compiled manifest.json","dbt 1.0 or later","Recent dbt run or parse to generate up-to-date metadata"],"input_types":["dbt manifest.json","dbt project YAML files","SQL model definitions"],"output_types":["lineage diagrams (SVG/PNG or interactive HTML)","dependency matrices","impact analysis reports","documentation markdown"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_2","uri":"capability://automation.workflow.dbt.configuration.and.variable.management.automation","name":"dbt configuration and variable management automation","description":"Automates the creation and management of dbt configuration files (dbt_project.yml, profiles.yml, variables, and environment-specific configs) by inferring settings from project structure and user inputs. The system generates proper YAML syntax, handles environment variable substitution, manages multiple target configurations, and applies dbt best practices for variable scoping and macro defaults.","intents":["I want to set up dbt profiles and configurations without manually writing YAML","I need to manage different configurations for dev, staging, and production environments","I want to define and inject dbt variables across my project without manual configuration"],"best_for":["Teams new to dbt who struggle with configuration syntax and best practices","Organizations managing multiple dbt projects with similar configuration patterns","DevOps/data engineers automating dbt deployment pipelines"],"limitations":["Cannot validate credentials or database connectivity until dbt run-time","Limited to dbt-supported adapters (Snowflake, BigQuery, Redshift, Postgres, Databricks, etc.)","Complex custom macro configurations or advanced dbt features may require manual YAML editing","Environment variable injection depends on proper CI/CD setup — no built-in secrets management"],"requires":["dbt 1.0 or later","Database credentials or connection parameters","Understanding of target environment (dev/staging/prod)"],"input_types":["database connection parameters","environment names and settings","variable definitions","dbt adapter selection"],"output_types":["dbt_project.yml","profiles.yml","variables YAML files","environment configuration templates"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_3","uri":"capability://code.generation.editing.sql.and.dbt.macro.code.generation.from.natural.language","name":"sql and dbt macro code generation from natural language","description":"Converts natural language descriptions or business requirements into dbt-compatible SQL and macro definitions. The system likely uses LLM-based code generation with dbt-specific prompting to produce SQL that follows dbt conventions (using ref(), source(), and dbt macros), includes proper documentation, and adheres to team style guides. Generated code includes CTEs, window functions, and other SQL patterns appropriate for data transformation.","intents":["I want to describe a transformation in plain English and get dbt SQL code","I need to create a reusable dbt macro from a business requirement description","I want to generate SQL for common patterns (aggregations, deduplication, slowly changing dimensions) without writing from scratch"],"best_for":["Data analysts transitioning to dbt who are less comfortable with SQL","Teams accelerating model development by reducing SQL writing time","Organizations standardizing transformation patterns across projects"],"limitations":["Generated SQL requires review and testing — LLM outputs may contain logical errors or suboptimal query patterns","Complex business logic with multiple conditions or domain-specific rules may require significant manual refinement","Performance optimization (indexing, materialization strategy) not automatically applied — requires manual tuning","Cannot generate SQL for proprietary or highly specialized transformations without extensive context"],"requires":["dbt project with configured profiles","Understanding of source data structure and business logic","dbt 1.0 or later"],"input_types":["natural language descriptions","business requirements","transformation specifications"],"output_types":["dbt model SQL files","dbt macro definitions","SQL transformation code","documentation comments"],"categories":["code-generation-editing","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_4","uri":"capability://code.generation.editing.dbt.test.generation.and.validation.rule.automation","name":"dbt test generation and validation rule automation","description":"Automatically generates dbt tests (uniqueness, not-null, referential integrity, custom SQL tests) based on data profiling, schema analysis, and business rules. The system analyzes column cardinality, data types, and relationships to recommend appropriate tests, then generates dbt test YAML configurations that can be customized and executed within the dbt test framework.","intents":["I want to add comprehensive tests to my dbt models without manually writing test definitions","I need to identify which columns should have uniqueness or not-null constraints based on data patterns","I want to generate referential integrity tests between related models"],"best_for":["Data teams improving data quality and test coverage in dbt projects","Organizations implementing data governance and validation frameworks","Teams lacking dedicated QA resources for data pipeline testing"],"limitations":["Generated tests are recommendations — false positives possible if data patterns don't reflect business rules","Cannot infer complex business logic constraints (e.g., 'revenue must be positive only for certain product categories')","Requires recent data samples to profile — may miss edge cases or seasonal patterns","Custom test logic still requires manual SQL writing for domain-specific validations"],"requires":["dbt project with data warehouse connection","Recent data in source tables for profiling","dbt 1.0 or later"],"input_types":["dbt model definitions","database schema metadata","data samples for profiling"],"output_types":["dbt test YAML configurations","test definitions","validation rule recommendations"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_5","uri":"capability://code.generation.editing.dbt.project.structure.and.best.practices.enforcement","name":"dbt project structure and best practices enforcement","description":"Analyzes existing dbt projects and recommends or automatically applies structural improvements aligned with dbt best practices (proper folder organization, naming conventions, materialization strategies, macro organization). The system scans project files, identifies deviations from conventions, and can auto-refactor code to standardize structure, naming, and organization patterns.","intents":["I want to audit my dbt project against best practices and get recommendations for improvement","I need to refactor a messy dbt project to follow consistent naming and folder conventions","I want to enforce team standards for dbt project structure across multiple projects"],"best_for":["Teams inheriting or maintaining legacy dbt projects with inconsistent structure","Organizations establishing dbt governance and standardization policies","Data teams scaling dbt usage across multiple projects and needing consistency"],"limitations":["Refactoring requires careful testing — automated changes may break downstream dependencies if not validated","Cannot enforce business logic patterns or transformation semantics — only structural and naming conventions","Recommendations may conflict with team-specific preferences or legacy constraints","Requires manual review and approval before applying large-scale refactoring changes"],"requires":["dbt project with accessible source files","dbt 1.0 or later","Git repository for version control (recommended for refactoring)"],"input_types":["dbt project files","dbt manifest.json","project configuration"],"output_types":["audit reports","refactoring recommendations","refactored project files","style guide documentation"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_6","uri":"capability://text.generation.language.dbt.documentation.generation.and.enrichment","name":"dbt documentation generation and enrichment","description":"Automatically generates comprehensive dbt documentation (model descriptions, column-level documentation, data dictionaries) from database metadata, SQL analysis, and optional natural language inputs. The system extracts column names, data types, and relationships, then enriches documentation with business context, usage examples, and lineage information, producing dbt-compatible YAML documentation that integrates with dbt docs.","intents":["I want to generate documentation for all my dbt models without manually writing descriptions","I need to create a data dictionary that shows column definitions, data types, and business meaning","I want to enrich auto-generated documentation with business context and usage examples"],"best_for":["Data teams lacking dedicated documentation resources","Organizations improving data discoverability and self-service analytics","Teams scaling dbt projects and needing to maintain documentation alongside code"],"limitations":["Auto-generated descriptions are generic — require manual refinement for business context and domain-specific meaning","Cannot infer business logic or transformation intent from SQL alone — requires human input for semantic documentation","Documentation quality depends on source metadata quality — poor column naming or missing comments in source systems limits output quality","Requires manual updates when business logic or column meanings change"],"requires":["dbt project with models and sources defined","Database connection for metadata extraction","dbt 1.0 or later"],"input_types":["dbt model definitions","database schema metadata","natural language descriptions (optional)"],"output_types":["dbt YAML documentation files","data dictionaries","column-level documentation","dbt docs-compatible markdown"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_7","uri":"capability://data.processing.analysis.dbt.performance.optimization.and.query.analysis","name":"dbt performance optimization and query analysis","description":"Analyzes dbt models and generated SQL to identify performance bottlenecks, suggest materialization strategy changes (table vs. view vs. incremental), and recommend query optimizations. The system profiles query execution times, analyzes SQL complexity, and suggests improvements like adding indexes, changing materialization, or refactoring CTEs for better performance.","intents":["I want to identify which dbt models are slow and get recommendations to optimize them","I need to decide whether a model should be materialized as a table or view based on usage patterns","I want to understand why a dbt run is taking too long and where to focus optimization efforts"],"best_for":["Data teams managing large dbt projects with performance concerns","Organizations optimizing data warehouse costs by reducing query execution time","Teams scaling dbt projects and needing to maintain acceptable run times"],"limitations":["Optimization recommendations depend on data warehouse-specific capabilities (Snowflake clustering, BigQuery partitioning, etc.)","Cannot optimize without access to query execution metrics — requires recent dbt runs with performance data","Materialization strategy changes may require downstream model adjustments or testing","Performance improvements are data-dependent — recommendations may not apply as data volume or distribution changes"],"requires":["dbt project with recent run history","Data warehouse connection with query performance metrics access","dbt 1.0 or later"],"input_types":["dbt model definitions","query execution metrics","data warehouse statistics"],"output_types":["performance analysis reports","optimization recommendations","materialization strategy suggestions","refactored model definitions"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_euno__cap_8","uri":"capability://automation.workflow.dbt.version.migration.and.upgrade.automation","name":"dbt version migration and upgrade automation","description":"Automates the process of upgrading dbt projects to newer versions by analyzing code for deprecated syntax, updating configurations, and refactoring breaking changes. The system identifies dbt version-specific issues, applies automated migrations for known breaking changes, and generates reports on manual changes required.","intents":["I want to upgrade my dbt project from v1.0 to v1.5 without manually fixing breaking changes","I need to identify which parts of my dbt project use deprecated syntax or features","I want to automate the process of updating dbt configurations for a new version"],"best_for":["Teams maintaining dbt projects and needing to stay current with dbt releases","Organizations managing multiple dbt projects requiring coordinated version upgrades","Data teams with limited engineering resources for manual migration work"],"limitations":["Automated migrations only handle known breaking changes — custom macros or edge cases may require manual fixes","Cannot validate that refactored code produces identical results — requires testing after migration","Some dbt features may have no direct equivalent in newer versions, requiring architectural changes","Requires comprehensive test coverage to validate that migrations don't break functionality"],"requires":["dbt project with accessible source files","Git repository for version control","dbt 1.0 or later (source version)"],"input_types":["dbt project files","dbt version specifications","dbt configuration files"],"output_types":["migrated dbt project files","migration reports","breaking change analysis","manual change recommendations"],"categories":["automation-workflow","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"high","permissions":["Active dbt project with configured profiles.yml","Database connection credentials (Snowflake, BigQuery, Redshift, or Postgres)","dbt 1.0 or later","Access to source schema metadata","dbt project with compiled manifest.json","Recent dbt run or parse to generate up-to-date metadata","Database credentials or connection parameters","Understanding of target environment (dev/staging/prod)","dbt project with configured profiles","Understanding of source data structure and business logic"],"failure_modes":["Generated models are templates requiring manual refinement for business logic and transformations","Limited to dbt-compatible data warehouses (Snowflake, BigQuery, Redshift, Postgres, etc.)","Cannot infer complex transformation logic from schema alone — requires developer input for CTEs, aggregations, and joins","Lineage accuracy depends on dbt manifest freshness — requires recent dbt runs to reflect current state","Cannot trace lineage through external tools (Airflow, Spark jobs) outside dbt ecosystem","Column-level lineage limited to explicit dbt ref() and source() calls; implicit joins or dynamic SQL may not be fully captured","No real-time lineage updates — requires manual dbt parse/run cycle to refresh","Cannot validate credentials or database connectivity until dbt run-time","Limited to dbt-supported adapters (Snowflake, BigQuery, Redshift, Postgres, Databricks, etc.)","Complex custom macro configurations or advanced dbt features may require manual YAML editing","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.2,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:30.284Z","last_scraped_at":"2026-04-05T13:23:42.552Z","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=euno","compare_url":"https://unfragile.ai/compare?artifact=euno"}},"signature":"28dsa9EbqsqK/pZQB8QOzvyAUSadLddAe7ZJBPrI8WpV3mHUs0fQAdf7pyMKNqT/uI2YmbjDgi7p1xxpKq/SDQ==","signedAt":"2026-06-21T14:47:49.118Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/euno","artifact":"https://unfragile.ai/euno","verify":"https://unfragile.ai/api/v1/verify?slug=euno","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"}}