{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-hex-magic","slug":"hex-magic","name":"Hex Magic","type":"product","url":"https://hex.tech/product/magic-ai/","page_url":"https://unfragile.ai/hex-magic","categories":["app-builders"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-hex-magic__cap_0","uri":"capability://code.generation.editing.natural.language.to.sql.code.generation.with.data.context.awareness","name":"natural-language-to-sql code generation with data context awareness","description":"Converts natural language queries into executable SQL by analyzing the connected data warehouse schema, table relationships, and column metadata. The system maintains awareness of the user's data context (tables, columns, data types) and generates contextually appropriate queries that reference actual schema elements rather than generic placeholders. Uses LLM-based code generation with schema-aware prompt engineering to produce valid, executable SQL across multiple database backends.","intents":["Write SQL queries without memorizing exact table and column names","Generate complex multi-table joins from plain English descriptions","Quickly prototype data exploration queries without manual SQL syntax","Reduce time spent on query debugging by generating syntactically correct SQL on first attempt"],"best_for":["Data analysts and business users without SQL expertise","Teams using Hex notebooks for collaborative data exploration","Organizations wanting to democratize SQL query writing across non-technical stakeholders"],"limitations":["Requires pre-connected data warehouse with accessible schema metadata","May generate suboptimal query plans for complex analytical queries — human review recommended for production workloads","Limited to SQL dialects supported by connected warehouse (Snowflake, BigQuery, Redshift, etc.)","Cannot infer business logic or domain-specific transformations not explicitly documented in schema"],"requires":["Active Hex workspace with authenticated data warehouse connection","Data warehouse with queryable schema metadata (INFORMATION_SCHEMA or equivalent)","Appropriate database user permissions to read schema and execute generated queries"],"input_types":["natural language text","conversational queries"],"output_types":["SQL code","executable query strings"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_1","uri":"capability://code.generation.editing.python.code.generation.with.notebook.aware.execution.context","name":"python code generation with notebook-aware execution context","description":"Generates executable Python code snippets within Hex notebooks by understanding the notebook's execution context, previously defined variables, imported libraries, and data frames in scope. The code generator maintains awareness of what's already been computed in the notebook and generates code that builds on existing state rather than requiring full re-implementation. Uses LLM-based generation with execution context injection to produce code that runs correctly on first execution within the notebook environment.","intents":["Generate data transformation and analysis code without writing boilerplate","Create visualization code that references already-loaded data frames","Quickly prototype statistical analyses or machine learning workflows","Generate code that integrates with existing notebook computations without manual variable name matching"],"best_for":["Data scientists and analysts working in Hex notebooks","Teams building reproducible analytical workflows","Users wanting to accelerate exploratory data analysis without context-switching to IDE"],"limitations":["Code generation quality depends on clarity of variable names and data frame structure in notebook context","Cannot automatically detect or resolve dependency conflicts if generated code requires libraries not yet imported","Limited to Python ecosystem — no native support for R, Julia, or other languages","Generated code may not follow team-specific coding standards or optimization patterns"],"requires":["Active Hex notebook with Python kernel","Hex workspace with sufficient compute resources to execute generated code","Python 3.8+ runtime environment"],"input_types":["natural language descriptions","conversational requests for code"],"output_types":["Python code","executable notebook cells"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_2","uri":"capability://data.processing.analysis.ai.assisted.data.exploration.and.insight.generation","name":"ai-assisted data exploration and insight generation","description":"Analyzes uploaded or connected datasets to automatically generate exploratory data analysis (EDA) code, identify statistical patterns, detect anomalies, and suggest relevant visualizations. The system profiles data distributions, cardinality, missing values, and correlations, then uses LLM reasoning to translate these profiles into natural language insights and recommended analytical directions. Generates executable code (SQL or Python) that implements the suggested analyses without requiring manual specification.","intents":["Quickly understand the structure and quality of a new dataset without manual EDA","Identify interesting patterns, outliers, or correlations that warrant deeper investigation","Generate a starting set of exploratory analyses and visualizations automatically","Get natural language summaries of data characteristics to share with non-technical stakeholders"],"best_for":["Data analysts onboarding new datasets","Teams needing rapid data quality assessment before analysis","Organizations wanting to standardize exploratory analysis workflows","Business users seeking data-driven insights without statistical expertise"],"limitations":["Insight generation may miss domain-specific patterns that require business context","Automated anomaly detection uses statistical methods that may flag expected variations in domain-specific data","Correlation detection does not imply causation — generated insights require human validation","Performance degrades on very wide datasets (1000+ columns) due to correlation computation complexity"],"requires":["Dataset with at least 100 rows for meaningful statistical profiling","Hex workspace with data warehouse connection or file upload capability","Sufficient compute resources to profile and analyze dataset (scales with dataset size)"],"input_types":["CSV files","database tables","data warehouse queries","structured data"],"output_types":["natural language insights","Python or SQL code","visualization specifications","data quality reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_3","uri":"capability://text.generation.language.conversational.data.query.refinement.and.iteration","name":"conversational data query refinement and iteration","description":"Enables multi-turn conversation where users can ask follow-up questions, request modifications, or refine queries based on results. The system maintains conversation history and context, allowing users to say things like 'filter that to just Q4' or 'show me the top 10' without re-specifying the full query. Uses conversation state management to track the current query context and incrementally modify generated code or SQL based on natural language refinements.","intents":["Iteratively refine data queries through natural conversation without rewriting full queries","Ask follow-up questions about results without losing context of previous queries","Modify queries incrementally ('add a filter', 'sort by date', 'group by region') through conversational language","Explore data interactively without switching between natural language and code"],"best_for":["Exploratory data analysis workflows where direction changes frequently","Non-technical users who benefit from conversational interfaces","Teams using Hex for collaborative data exploration with multiple stakeholders"],"limitations":["Conversation context window is limited — very long conversations may lose earlier context","Ambiguous refinements (e.g., 'that' without clear antecedent) may be misinterpreted","Cannot track implicit business logic across conversation turns — requires explicit specification","Conversation state is notebook-scoped and not persisted across sessions"],"requires":["Active Hex notebook with Magic AI enabled","Connected data warehouse or uploaded dataset","Conversation maintained within single notebook session"],"input_types":["natural language text","conversational queries","refinement requests"],"output_types":["modified SQL or Python code","query results","natural language responses"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_4","uri":"capability://code.generation.editing.ai.generated.visualization.recommendations.and.code","name":"ai-generated visualization recommendations and code","description":"Analyzes data characteristics (dimensionality, cardinality, data types, distributions) and automatically recommends appropriate visualization types, then generates executable code to render those visualizations. The system understands visualization semantics (scatter plots for correlation, histograms for distributions, time series for temporal data) and maps data columns to appropriate visual encodings. Generates code using Hex's visualization libraries (or standard Python libraries like matplotlib, plotly) that can be executed directly in the notebook.","intents":["Get visualization recommendations without manually trying different chart types","Generate visualization code without learning visualization library APIs","Quickly create publication-ready charts from raw data","Explore data through multiple visualization perspectives automatically"],"best_for":["Data analysts creating exploratory visualizations","Non-technical users wanting to visualize data without learning charting libraries","Teams standardizing visualization approaches across analyses"],"limitations":["Recommendations are based on statistical properties, not domain-specific visualization best practices","Generated visualizations may require manual tuning (colors, labels, scales) for publication quality","Limited to 2D and basic 3D visualizations — no support for complex interactive dashboards","Recommendation quality degrades with high-dimensional data (10+ dimensions)"],"requires":["Dataset with identified column types and distributions","Hex notebook with visualization rendering capability","Python visualization libraries (matplotlib, plotly, or Hex native charting)"],"input_types":["data frames","database query results","structured data with column metadata"],"output_types":["Python visualization code","rendered charts","visualization specifications"],"categories":["code-generation-editing","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_5","uri":"capability://code.generation.editing.data.transformation.code.generation.with.schema.validation","name":"data transformation code generation with schema validation","description":"Generates Python or SQL code for common data transformation operations (filtering, grouping, joining, pivoting, aggregating) by understanding the input data schema and validating that generated transformations produce expected output schemas. The system infers transformation intent from natural language descriptions, generates code, and validates that column names, data types, and cardinality match expectations before execution. Uses schema-aware code generation with post-generation validation to catch common transformation errors.","intents":["Generate data cleaning and transformation code without manual specification of column operations","Quickly prototype data pipelines for ETL workflows","Validate that transformations produce expected output structure before execution","Reduce debugging time by catching schema mismatches before data processing"],"best_for":["Data engineers building ETL pipelines in Hex","Analysts prototyping data transformations before production implementation","Teams wanting to reduce manual data transformation code writing"],"limitations":["Schema validation only checks structure, not data quality or business logic correctness","Complex transformations requiring custom functions may not be generated correctly","Performance implications of generated transformations are not analyzed or optimized","Requires explicit schema specification or inference from sample data"],"requires":["Input data with accessible schema metadata","Hex notebook with Python or SQL execution capability","Data warehouse connection or in-memory data frame"],"input_types":["natural language transformation descriptions","data frames with schema","database tables"],"output_types":["Python or SQL transformation code","transformed data frames","schema validation reports"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_6","uri":"capability://code.generation.editing.natural.language.to.dashboard.specification.generation","name":"natural language to dashboard specification generation","description":"Converts natural language descriptions of desired dashboards into executable specifications that render interactive dashboards in Hex. The system understands dashboard composition (multiple charts, filters, layout), maps natural language descriptions to specific visualization types and data queries, and generates the code or configuration needed to render the dashboard. Supports interactive elements like filters and drill-downs that are automatically wired to underlying data queries.","intents":["Create multi-chart dashboards from natural language descriptions without manual layout and wiring","Generate dashboards with interactive filters and drill-down capabilities automatically","Quickly prototype dashboard designs before investing in formal BI tool configuration","Enable non-technical users to specify dashboard requirements in natural language"],"best_for":["Business users and analysts creating self-service dashboards","Teams prototyping dashboard designs in Hex before production deployment","Organizations wanting to democratize dashboard creation"],"limitations":["Generated dashboards may require manual layout adjustments for optimal visual hierarchy","Complex interactivity (cascading filters, custom drill-downs) may require manual code modification","Dashboard performance is limited by underlying data warehouse query performance","Styling and branding customization requires manual code editing"],"requires":["Hex workspace with dashboard rendering capability","Connected data warehouse with queryable tables","Appropriate database permissions to execute dashboard queries"],"input_types":["natural language dashboard descriptions","specification of desired metrics and dimensions"],"output_types":["Hex dashboard code","interactive dashboard specifications","rendered dashboards"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_7","uri":"capability://text.generation.language.ai.assisted.documentation.and.code.commenting.generation","name":"ai-assisted documentation and code commenting generation","description":"Automatically generates documentation, docstrings, and inline comments for data analysis code by analyzing the code's intent, data transformations, and outputs. The system understands what the code does (not just syntactic structure) and generates human-readable explanations that describe the business logic, data flow, and expected outputs. Uses LLM-based code understanding to produce documentation that explains 'why' the code exists, not just 'what' it does.","intents":["Generate documentation for analytical code without manual writing","Create docstrings that explain data transformations and business logic","Improve code readability and maintainability through automated commenting","Enable knowledge transfer by documenting analytical intent for team members"],"best_for":["Data teams wanting to improve code documentation practices","Organizations standardizing analytical code quality","Teams with high turnover needing to preserve analytical knowledge"],"limitations":["Generated documentation may miss domain-specific context or business rationale","Comments may be verbose or redundant if code is already self-documenting","Cannot infer implicit business rules or constraints not expressed in code","Requires manual review to ensure documentation accuracy"],"requires":["Python or SQL code in Hex notebook","Code with sufficient context to infer intent (variable names, structure)"],"input_types":["Python code","SQL queries","notebook cells"],"output_types":["docstrings","inline comments","documentation text"],"categories":["text-generation-language","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_8","uri":"capability://code.generation.editing.intelligent.error.diagnosis.and.code.repair.suggestions","name":"intelligent error diagnosis and code repair suggestions","description":"Analyzes failed code execution, error messages, and execution context to diagnose root causes and suggest code repairs. The system understands common data analysis errors (schema mismatches, type errors, missing values, query timeouts) and generates corrected code that addresses the underlying issue. Uses error message parsing combined with code analysis to produce targeted fixes rather than generic suggestions.","intents":["Quickly diagnose why code failed without manual debugging","Get suggested fixes for common data analysis errors","Reduce time spent on error investigation and code repair","Learn from errors by understanding root causes and solutions"],"best_for":["Data analysts and scientists debugging analytical code","Teams wanting to reduce time spent on error resolution","Users learning data analysis and needing guidance on error resolution"],"limitations":["Diagnosis accuracy depends on error message clarity and execution context availability","Suggested fixes may not address root cause if error is due to data quality issues","Cannot diagnose errors in external systems (database, API failures) without detailed error context","Fixes may require manual validation before execution"],"requires":["Failed code execution with error message","Access to execution context (variables, data frames, schema)","Hex notebook with error capture capability"],"input_types":["error messages","failed code","execution context"],"output_types":["error diagnosis","suggested code fixes","explanations of root causes"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-hex-magic__cap_9","uri":"capability://text.generation.language.collaborative.notebook.annotation.and.explanation.generation","name":"collaborative notebook annotation and explanation generation","description":"Generates natural language explanations of notebook cells, analyses, and results that can be shared with non-technical stakeholders. The system analyzes code, data outputs, and visualizations to produce summaries that explain what was analyzed, what was found, and what the implications are. Supports annotation of specific cells or entire analyses to create shareable explanations without requiring manual documentation writing.","intents":["Create natural language summaries of analyses for non-technical stakeholders","Generate explanations of notebook results without manual writing","Annotate analyses to improve collaboration and knowledge sharing","Translate technical analyses into business-friendly language"],"best_for":["Data teams communicating results to business stakeholders","Organizations wanting to improve analytical transparency","Teams using Hex for collaborative analysis with mixed technical backgrounds"],"limitations":["Generated explanations may lack business context or strategic implications","Tone and level of technical detail may not match audience expectations","Cannot infer unstated assumptions or caveats in analyses","Requires manual review to ensure explanations are accurate and appropriate"],"requires":["Hex notebook with executed cells and results","Code and outputs with sufficient context for explanation generation"],"input_types":["notebook cells","code","data outputs","visualizations"],"output_types":["natural language explanations","summaries","annotations"],"categories":["text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"high","permissions":["Active Hex workspace with authenticated data warehouse connection","Data warehouse with queryable schema metadata (INFORMATION_SCHEMA or equivalent)","Appropriate database user permissions to read schema and execute generated queries","Active Hex notebook with Python kernel","Hex workspace with sufficient compute resources to execute generated code","Python 3.8+ runtime environment","Dataset with at least 100 rows for meaningful statistical profiling","Hex workspace with data warehouse connection or file upload capability","Sufficient compute resources to profile and analyze dataset (scales with dataset size)","Active Hex notebook with Magic AI enabled"],"failure_modes":["Requires pre-connected data warehouse with accessible schema metadata","May generate suboptimal query plans for complex analytical queries — human review recommended for production workloads","Limited to SQL dialects supported by connected warehouse (Snowflake, BigQuery, Redshift, etc.)","Cannot infer business logic or domain-specific transformations not explicitly documented in schema","Code generation quality depends on clarity of variable names and data frame structure in notebook context","Cannot automatically detect or resolve dependency conflicts if generated code requires libraries not yet imported","Limited to Python ecosystem — no native support for R, Julia, or other languages","Generated code may not follow team-specific coding standards or optimization patterns","Insight generation may miss domain-specific patterns that require business context","Automated anomaly detection uses statistical methods that may flag expected variations in domain-specific data","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.3,"ecosystem":0.25,"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":"2026-06-17T09:51:03.041Z","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=hex-magic","compare_url":"https://unfragile.ai/compare?artifact=hex-magic"}},"signature":"dvTiUUcgzJlUbTynjWI4g+HrKhj0vZqDLH05omlfe/zowtOOXroH+p8ZvnWdBP4nOZy6NjNoqdYDpDCaGTkLDg==","signedAt":"2026-06-21T08:47:46.358Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hex-magic","artifact":"https://unfragile.ai/hex-magic","verify":"https://unfragile.ai/api/v1/verify?slug=hex-magic","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"}}