{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-superluminal","slug":"superluminal","name":"Superluminal","type":"product","url":"https://superluminal.dev","page_url":"https://unfragile.ai/superluminal","categories":["app-builders","code-editors"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-superluminal__cap_0","uri":"capability://text.generation.language.natural.language.to.dashboard.query.translation","name":"natural-language-to-dashboard-query-translation","description":"Converts natural language questions into executable dashboard queries by parsing user intent and mapping it to underlying data schema. The system likely uses LLM-based semantic understanding combined with schema introspection to identify relevant metrics, dimensions, and filters, then generates the appropriate query syntax (SQL, dashboard API calls, or proprietary query language) without requiring users to understand the technical query structure.","intents":["Ask questions about my product metrics in plain English without learning SQL or dashboard syntax","Get instant answers to ad-hoc analytics questions without navigating complex UI","Explore data relationships and drill-down without manual query construction"],"best_for":["Product managers and non-technical stakeholders who need data insights","Data analysts seeking faster ad-hoc query generation","Teams using complex dashboards (Tableau, Looker, Mixpanel, Amplitude)"],"limitations":["Accuracy depends on schema documentation quality and LLM understanding of domain-specific metrics","May struggle with ambiguous natural language that maps to multiple valid queries","Requires pre-configured dashboard connection and schema mapping"],"requires":["Connected dashboard platform (Tableau, Looker, Mixpanel, Amplitude, or similar)","API credentials or authentication token for dashboard access","Schema metadata exposed via dashboard API or manual configuration"],"input_types":["natural language text (conversational questions)"],"output_types":["dashboard query results","visualizations","structured data tables"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_1","uri":"capability://planning.reasoning.contextual.metric.recommendation.and.discovery","name":"contextual-metric-recommendation-and-discovery","description":"Proactively suggests relevant metrics, KPIs, and drill-down paths based on user context and historical query patterns. The system analyzes what questions users ask, what data they access, and their role/team to recommend related metrics they might want to explore, using collaborative filtering or usage-based heuristics combined with domain knowledge about common metric relationships.","intents":["Discover related metrics I should be monitoring without manually exploring the dashboard","Get smart suggestions for what to analyze next based on my current question","Understand which metrics correlate with or explain the trends I'm seeing"],"best_for":["Product teams doing exploratory data analysis","Analysts who want guided discovery rather than blank-slate querying","Organizations with large metric catalogs where discoverability is a pain point"],"limitations":["Recommendations quality depends on sufficient historical usage data and well-structured metric metadata","May surface irrelevant suggestions if metric relationships aren't properly configured","Cold-start problem for new users or new metrics with limited usage history"],"requires":["Dashboard platform with usage analytics/audit logs","Metric metadata with relationships and descriptions","Minimum baseline of user query history for pattern detection"],"input_types":["user query context","current metric being viewed","user role/team information"],"output_types":["ranked list of recommended metrics","suggested drill-down paths","related metric relationships"],"categories":["planning-reasoning","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_2","uri":"capability://text.generation.language.multi.turn.conversational.analytics.session","name":"multi-turn-conversational-analytics-session","description":"Maintains conversational context across multiple turns, allowing users to ask follow-up questions that reference previous queries, results, and implicit context. The system uses conversation history management with state tracking to understand pronouns, relative references ('that metric', 'the previous result'), and implicit drill-down requests, enabling natural dialogue rather than isolated queries.","intents":["Have a natural back-and-forth conversation about my data without restating context each time","Ask follow-up questions like 'why did that drop?' or 'show me the breakdown' without re-specifying the metric","Build exploratory analysis narratives through conversational interaction"],"best_for":["Analysts conducting exploratory data analysis sessions","Non-technical stakeholders who prefer conversational interaction","Teams using Superluminal as a collaborative analytics assistant"],"limitations":["Context window limitations may cause loss of earlier conversation context in long sessions","Ambiguous pronouns or references may be misinterpreted without explicit clarification","Requires stateful session management which adds complexity to deployment"],"requires":["Session storage/persistence layer (in-memory or database)","Conversation history tracking with timestamp and result caching","LLM with sufficient context window to maintain multi-turn dialogue"],"input_types":["natural language follow-up questions","implicit references to previous results"],"output_types":["contextually-aware query results","clarification requests when ambiguous","conversation summary"],"categories":["text-generation-language","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_3","uri":"capability://data.processing.analysis.dashboard.schema.introspection.and.mapping","name":"dashboard-schema-introspection-and-mapping","description":"Automatically discovers and maps dashboard structure, metrics, dimensions, filters, and data relationships by introspecting the connected dashboard platform's API and metadata. The system builds an internal semantic model of available data, metric definitions, and valid query combinations, enabling the LLM to generate accurate queries without manual schema configuration.","intents":["Connect my dashboard and have the copilot automatically understand what metrics and dimensions are available","Ensure the copilot generates valid queries that match my dashboard's actual structure","Update the copilot's understanding when I add new metrics or change dashboard structure"],"best_for":["Teams using Tableau, Looker, Mixpanel, Amplitude, or other API-enabled dashboards","Organizations that want zero-configuration setup after initial dashboard connection","Environments where dashboard schema changes frequently"],"limitations":["Only works with dashboards that expose schema via API; custom/proprietary dashboards require manual configuration","Schema introspection may be incomplete if dashboard API doesn't expose all metadata","Requires API credentials with sufficient permissions to read schema and metadata"],"requires":["Dashboard platform with public API supporting schema/metadata queries","API credentials with read access to dashboard structure and definitions","Network connectivity to dashboard platform for periodic schema refresh"],"input_types":["dashboard API endpoint","authentication credentials"],"output_types":["internal semantic schema model","metric/dimension catalog","query validation rules"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_4","uri":"capability://text.generation.language.query.result.explanation.and.insight.generation","name":"query-result-explanation-and-insight-generation","description":"Analyzes query results and generates natural language explanations of what the data shows, including trend identification, anomaly detection, and contextual insights. The system compares results against historical baselines, identifies statistically significant changes, and articulates business implications in plain language, helping users understand not just the numbers but their meaning.","intents":["Get an English explanation of what my query results mean, not just raw numbers","Identify anomalies or unexpected trends in the data automatically","Understand the business impact of the metrics I'm looking at"],"best_for":["Non-technical stakeholders who need data interpretation","Analysts who want automated insight generation to accelerate analysis","Teams using dashboards for decision-making who need context, not just data"],"limitations":["Explanations are only as good as the underlying data quality and metric definitions","May generate false insights if baseline/historical data is incomplete or unrepresentative","Requires domain knowledge to generate business-relevant insights; generic insights may be obvious or unhelpful"],"requires":["Historical data or baseline for comparison","Metric definitions with business context/descriptions","Sufficient data points to identify meaningful trends"],"input_types":["query results (structured data)","metric metadata and definitions","historical baseline data"],"output_types":["natural language explanation","anomaly alerts","trend analysis","business impact summary"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_5","uri":"capability://data.processing.analysis.cross.dashboard.metric.correlation.analysis","name":"cross-dashboard-metric-correlation-analysis","description":"Analyzes relationships and correlations between metrics across multiple connected dashboards or data sources, identifying which metrics move together and which are independent. The system likely uses time-series correlation analysis combined with semantic understanding of metric relationships to surface non-obvious connections and help users understand multi-dimensional cause-and-effect relationships in their data.","intents":["Understand which metrics are correlated and which are independent","Identify potential cause-and-effect relationships between different product metrics","Explore how changes in one area of the product affect other metrics"],"best_for":["Product teams analyzing complex multi-dimensional product behavior","Data analysts investigating metric relationships","Organizations with multiple data sources that need unified analysis"],"limitations":["Correlation analysis requires sufficient historical data with aligned timestamps","May identify spurious correlations without domain knowledge to validate","Cross-dashboard analysis adds latency and complexity; may be slow for large datasets"],"requires":["Multiple connected dashboards or data sources","Time-aligned data across sources","Sufficient historical data for correlation calculation"],"input_types":["multiple metric time series","metric definitions and relationships"],"output_types":["correlation matrix","identified relationships","causal hypotheses"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_6","uri":"capability://text.generation.language.natural.language.filter.and.segmentation.generation","name":"natural-language-filter-and-segmentation-generation","description":"Translates natural language filter requests into dashboard-specific filter syntax and generates dynamic segmentation queries. When users ask questions like 'show me results for enterprise customers in the US', the system parses the intent, identifies relevant dimensions and values, and constructs the appropriate filter expressions without requiring users to manually select filters from dropdown menus.","intents":["Apply complex filters to my dashboard queries using natural language instead of clicking dropdowns","Create dynamic segments based on natural language descriptions","Filter results by multiple dimensions without manually constructing filter logic"],"best_for":["Non-technical users who find dashboard filter UIs cumbersome","Analysts building complex multi-dimensional filters quickly","Teams needing to apply consistent filtering logic across multiple queries"],"limitations":["Requires dimension values to be available in dashboard metadata or data","May fail if natural language references ambiguous dimension values","Complex nested filter logic may be misinterpreted or generate invalid syntax"],"requires":["Dashboard dimension metadata with available values","Filter syntax documentation for target dashboard platform","Access to dimension value lists for validation"],"input_types":["natural language filter descriptions"],"output_types":["dashboard-specific filter expressions","segmentation queries","filter validation results"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-superluminal__cap_7","uri":"capability://memory.knowledge.saved.query.and.analysis.template.management","name":"saved-query-and-analysis-template-management","description":"Stores and retrieves previously asked questions and analysis patterns, allowing users to reuse and modify past queries without re-asking. The system maintains a searchable library of queries with metadata (intent, results, timestamp, user), enabling users to find similar past analyses and adapt them for new questions, reducing repetitive work.","intents":["Save my frequently-asked questions so I can re-run them without re-asking","Find similar past analyses to adapt for new questions","Share analysis templates with teammates"],"best_for":["Teams with recurring analysis needs","Organizations wanting to standardize how metrics are calculated","Analysts who want to build on past work"],"limitations":["Requires persistent storage and search indexing","Query templates may become stale if underlying data structure changes","Sharing templates across users requires access control and versioning"],"requires":["Persistent storage for query history and templates","Search/indexing capability for finding past queries","User authentication and access control"],"input_types":["natural language queries","query results","user annotations"],"output_types":["saved query templates","query history search results","reusable analysis patterns"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Connected dashboard platform (Tableau, Looker, Mixpanel, Amplitude, or similar)","API credentials or authentication token for dashboard access","Schema metadata exposed via dashboard API or manual configuration","Dashboard platform with usage analytics/audit logs","Metric metadata with relationships and descriptions","Minimum baseline of user query history for pattern detection","Session storage/persistence layer (in-memory or database)","Conversation history tracking with timestamp and result caching","LLM with sufficient context window to maintain multi-turn dialogue","Dashboard platform with public API supporting schema/metadata queries"],"failure_modes":["Accuracy depends on schema documentation quality and LLM understanding of domain-specific metrics","May struggle with ambiguous natural language that maps to multiple valid queries","Requires pre-configured dashboard connection and schema mapping","Recommendations quality depends on sufficient historical usage data and well-structured metric metadata","May surface irrelevant suggestions if metric relationships aren't properly configured","Cold-start problem for new users or new metrics with limited usage history","Context window limitations may cause loss of earlier conversation context in long sessions","Ambiguous pronouns or references may be misinterpreted without explicit clarification","Requires stateful session management which adds complexity to deployment","Only works with dashboards that expose schema via API; custom/proprietary dashboards require manual configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"ecosystem":0.35000000000000003,"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":"2026-06-17T09:51:04.049Z","last_scraped_at":"2026-05-03T14:00:10.321Z","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=superluminal","compare_url":"https://unfragile.ai/compare?artifact=superluminal"}},"signature":"Y5DxsFYbswFhGDdl8Vq9rMnZ/ZN/SQF72NecNUcEPgiGyelLOIJ3DbPhkMF4UUJ+1zv1CUH0zenICoqzO0+bDA==","signedAt":"2026-06-22T01:31:13.296Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/superluminal","artifact":"https://unfragile.ai/superluminal","verify":"https://unfragile.ai/api/v1/verify?slug=superluminal","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"}}