{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_anomalo","slug":"anomalo","name":"Anomalo","type":"product","url":"https://www.anomalo.com","page_url":"https://unfragile.ai/anomalo","categories":["data-pipelines"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_anomalo__cap_0","uri":"capability://data.quality.automated.anomaly.detection","name":"automated-anomaly-detection","description":"Automatically detects statistical anomalies and deviations in data warehouse tables without requiring manual threshold configuration. Uses machine learning to learn normal patterns from historical data and flags unusual values, distributions, or trends in real-time.","intents":["I want to catch data quality issues before they impact downstream analytics","I need to detect unusual patterns in my data without writing custom SQL checks","I want to identify outliers and unexpected changes automatically"],"best_for":["Data engineering teams","Analytics organizations","Data quality managers"],"limitations":["Requires several weeks of learning to optimize false positive tuning for domain-specific logic","Pricing scales steeply with data volume making it expensive for small organizations","Effectiveness depends on quality and consistency of historical data patterns"],"requires":["Connected data warehouse (Snowflake, BigQuery, Redshift, etc.)","Historical data with sufficient volume to establish baseline patterns","Regular data ingestion and updates"],"input_types":["database tables","data warehouse schemas"],"output_types":["anomaly alerts","deviation reports","statistical summaries"],"categories":["data-quality","monitoring","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_1","uri":"capability://monitoring.intelligent.alerting.with.context","name":"intelligent-alerting-with-context","description":"Delivers smart alerts that reduce false positives by understanding business context and data patterns. Uses machine learning to distinguish between legitimate data variations and actual anomalies, preventing alert fatigue.","intents":["I want to receive only meaningful alerts instead of being overwhelmed by false positives","I need alerts that understand my business context and seasonal patterns","I want to reduce time spent investigating non-critical data issues"],"best_for":["Data teams managing large critical datasets","Organizations with high alert volume","Teams struggling with alert fatigue"],"limitations":["Requires tuning period to learn business-specific patterns","May miss edge cases not represented in training data","Context understanding improves over time but is not perfect initially"],"requires":["Historical anomaly data and business context","Integration with alerting channels (Slack, email, etc.)","Time for ML model to learn patterns"],"input_types":["data warehouse metrics","historical anomaly patterns","business context metadata"],"output_types":["prioritized alerts","confidence scores","contextual explanations"],"categories":["monitoring","alerting","data-quality"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_10","uri":"capability://analytics.historical.anomaly.analysis","name":"historical-anomaly-analysis","description":"Analyzes historical data to identify past anomalies, trends, and patterns that may have been missed. Provides insights into data quality issues over time and their frequency.","intents":["I want to understand what data quality issues occurred in the past","I need to analyze trends in data anomalies over time","I want to identify recurring data quality problems"],"best_for":["Data quality analysts","Organizations conducting data audits","Teams investigating historical data issues"],"limitations":["Analysis depends on data retention and historical records","May not capture all past issues if detection wasn't enabled","Requires interpretation of historical patterns"],"requires":["Historical data in warehouse","Anomaly detection history","Time period for analysis"],"input_types":["historical warehouse data","past anomaly records","time-range specifications"],"output_types":["historical anomaly reports","trend analysis","pattern summaries","frequency reports"],"categories":["analytics","reporting","data-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_2","uri":"capability://integration.data.warehouse.integration","name":"data-warehouse-integration","description":"Seamlessly connects to major data warehouses and BI tools, enabling anomaly detection and validation without requiring data export or custom connectors. Supports Snowflake, BigQuery, Redshift, and other major platforms.","intents":["I want to monitor data quality directly in my existing data warehouse","I need to integrate anomaly detection without disrupting my current workflows","I want to connect my BI tools to automated data quality checks"],"best_for":["Organizations using Snowflake, BigQuery, or Redshift","Teams with existing BI tool investments","Data teams wanting minimal integration overhead"],"limitations":["Limited to supported warehouse platforms","Integration complexity depends on warehouse configuration and permissions","May require data warehouse admin access for setup"],"requires":["Supported data warehouse (Snowflake, BigQuery, Redshift, etc.)","Appropriate database credentials and permissions","Network connectivity between Anomalo and warehouse"],"input_types":["warehouse connection credentials","schema and table metadata","BI tool configurations"],"output_types":["connected data sources","monitored tables and metrics","integration status reports"],"categories":["integration","data-infrastructure","connectivity"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_3","uri":"capability://machine.learning.pattern.learning.from.history","name":"pattern-learning-from-history","description":"Automatically learns normal data patterns and baselines from historical data without manual configuration. Establishes what 'normal' looks like for each metric, enabling detection of meaningful deviations.","intents":["I want the system to understand what normal data looks like for my specific datasets","I need to avoid manually setting thresholds for each metric","I want anomaly detection that adapts to seasonal and cyclical patterns"],"best_for":["Organizations with consistent historical data","Teams wanting to avoid threshold configuration","Data with seasonal or cyclical patterns"],"limitations":["Requires sufficient historical data volume to establish reliable baselines","May struggle with new metrics or recently added data sources","Seasonal patterns need at least one full cycle of historical data","Sudden legitimate business changes may be flagged as anomalies"],"requires":["Minimum historical data period (typically 30+ days)","Regular, consistent data updates","Stable baseline patterns in historical data"],"input_types":["historical time-series data","data warehouse tables","metric definitions"],"output_types":["learned baselines","pattern models","confidence intervals"],"categories":["machine-learning","data-analysis","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_4","uri":"capability://monitoring.real.time.data.validation","name":"real-time-data-validation","description":"Continuously validates incoming data against learned patterns and business rules in real-time. Detects data quality issues as they occur rather than in batch processes.","intents":["I want to catch data quality issues immediately when they occur","I need real-time validation of critical data pipelines","I want to prevent bad data from reaching downstream systems"],"best_for":["Organizations with critical real-time data pipelines","Teams needing immediate data quality visibility","Data-dependent business operations"],"limitations":["Requires continuous warehouse connection and monitoring","May have latency depending on warehouse query performance","Real-time detection depends on data freshness in warehouse"],"requires":["Connected data warehouse with real-time data ingestion","Established baseline patterns from historical data","Sufficient warehouse compute for continuous queries"],"input_types":["streaming or continuously updated warehouse tables","real-time metric definitions"],"output_types":["real-time alerts","validation status","immediate anomaly notifications"],"categories":["monitoring","data-quality","real-time-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_5","uri":"capability://configuration.false.positive.tuning","name":"false-positive-tuning","description":"Provides tools and workflows to refine anomaly detection rules and reduce false positives through feedback and configuration. Allows teams to adjust sensitivity and context understanding based on domain-specific business logic.","intents":["I want to reduce the number of false positive alerts I receive","I need to teach the system about legitimate data variations in my business","I want to fine-tune sensitivity for different metrics and tables"],"best_for":["Data teams with domain expertise","Organizations with complex business logic","Teams willing to invest time in optimization"],"limitations":["Requires several weeks of tuning to optimize effectively","Requires domain knowledge to understand legitimate variations","Tuning is ongoing as business logic and data patterns change","May require multiple iterations to achieve optimal balance"],"requires":["Access to anomaly detection interface","Domain expertise about data and business logic","Time investment for iterative tuning","Historical anomaly data for learning"],"input_types":["false positive feedback","business rule definitions","sensitivity parameters"],"output_types":["refined detection rules","updated sensitivity settings","tuning recommendations"],"categories":["configuration","machine-learning","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_6","uri":"capability://visualization.data.quality.metrics.dashboard","name":"data-quality-metrics-dashboard","description":"Provides visual dashboards and reports showing data quality metrics, anomaly trends, and validation status across data warehouse tables. Enables monitoring and analysis of data health over time.","intents":["I want to see the overall health and quality of my data at a glance","I need to track data quality trends over time","I want to identify which tables and metrics have the most issues"],"best_for":["Data quality managers","Analytics leaders","Teams needing visibility into data health"],"limitations":["Dashboard effectiveness depends on proper metric selection","Requires interpretation of metrics by domain experts","May not capture all aspects of data quality"],"requires":["Connected data warehouse","Configured anomaly detection rules","Regular data updates"],"input_types":["anomaly detection results","validation metrics","historical data quality data"],"output_types":["visual dashboards","quality reports","trend analysis","metric summaries"],"categories":["visualization","reporting","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_7","uri":"capability://data.analysis.multi.table.correlation.detection","name":"multi-table-correlation-detection","description":"Detects anomalies and relationships across multiple related tables in the data warehouse. Identifies when anomalies in one table correlate with issues in related tables.","intents":["I want to understand how data quality issues in one table affect related tables","I need to detect correlated anomalies across my data warehouse","I want to trace data quality issues back to their root causes"],"best_for":["Organizations with complex data models","Teams managing interconnected data pipelines","Data teams needing root cause analysis"],"limitations":["Requires well-defined table relationships","Correlation detection may be complex with many tables","Requires sufficient data volume to establish correlations"],"requires":["Multiple related tables in data warehouse","Defined relationships between tables","Historical data for correlation analysis"],"input_types":["multiple warehouse tables","table relationship definitions","anomaly detection results"],"output_types":["correlation reports","related anomalies","impact analysis"],"categories":["data-analysis","monitoring","analytics"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_8","uri":"capability://configuration.custom.validation.rule.creation","name":"custom-validation-rule-creation","description":"Allows creation of custom validation rules and checks beyond automated anomaly detection. Enables teams to define business-specific data quality requirements without writing code.","intents":["I want to enforce business-specific data quality rules","I need to validate data against custom business logic","I want to create checks that automated detection doesn't cover"],"best_for":["Organizations with specific business requirements","Teams needing custom validation logic","Non-technical data quality managers"],"limitations":["Custom rules require clear definition of business logic","May require technical expertise depending on rule complexity","Custom rules need maintenance as business logic changes"],"requires":["Clear definition of business validation requirements","Access to rule creation interface","Understanding of data structure and business logic"],"input_types":["business rule definitions","validation criteria","data specifications"],"output_types":["custom validation rules","validation results","rule execution reports"],"categories":["configuration","validation","customization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_anomalo__cap_9","uri":"capability://monitoring.data.downtime.prevention","name":"data-downtime-prevention","description":"Proactively identifies and alerts on data quality issues that could cause data downtime or pipeline failures. Helps prevent cascading failures and data unavailability.","intents":["I want to prevent data pipeline failures before they happen","I need to minimize data downtime and unavailability","I want to catch issues that could break downstream systems"],"best_for":["Organizations with critical data dependencies","Teams managing production data pipelines","Data-dependent business operations"],"limitations":["Requires understanding of pipeline dependencies","Cannot prevent all types of failures","Effectiveness depends on alert response time"],"requires":["Connected data warehouse","Configured anomaly detection","Alert notification channels","Incident response procedures"],"input_types":["anomaly detection results","pipeline dependency information","critical metric definitions"],"output_types":["preventive alerts","failure risk assessments","incident notifications"],"categories":["monitoring","reliability","incident-prevention"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":45,"verified":false,"data_access_risk":"high","permissions":["Connected data warehouse (Snowflake, BigQuery, Redshift, etc.)","Historical data with sufficient volume to establish baseline patterns","Regular data ingestion and updates","Historical anomaly data and business context","Integration with alerting channels (Slack, email, etc.)","Time for ML model to learn patterns","Historical data in warehouse","Anomaly detection history","Time period for analysis","Supported data warehouse (Snowflake, BigQuery, Redshift, etc.)"],"failure_modes":["Requires several weeks of learning to optimize false positive tuning for domain-specific logic","Pricing scales steeply with data volume making it expensive for small organizations","Effectiveness depends on quality and consistency of historical data patterns","Requires tuning period to learn business-specific patterns","May miss edge cases not represented in training data","Context understanding improves over time but is not perfect initially","Analysis depends on data retention and historical records","May not capture all past issues if detection wasn't enabled","Requires interpretation of historical patterns","Limited to supported warehouse platforms","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.4083333333333333,"quality":0.83,"ecosystem":0.15000000000000002,"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":"2026-05-24T12:16:29.133Z","last_scraped_at":"2026-04-05T13:23:42.538Z","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=anomalo","compare_url":"https://unfragile.ai/compare?artifact=anomalo"}},"signature":"BbHCmwgi+azmNksm8tHczYv2bX6vhRzpSQqAd0lvmz1ndMKp1TuTOhDPBmMHk3XWzMjpzwXMQFNWSZMnG/UuAg==","signedAt":"2026-06-22T19:04:22.533Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/anomalo","artifact":"https://unfragile.ai/anomalo","verify":"https://unfragile.ai/api/v1/verify?slug=anomalo","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"}}