{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_vizly","slug":"vizly","name":"Vizly","type":"product","url":"https://www.vizly.fyi","page_url":"https://unfragile.ai/vizly","categories":["data-analysis"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_vizly__cap_0","uri":"capability://data.processing.analysis.natural.language.to.visualization.generation","name":"natural-language-to-visualization generation","description":"Converts natural language queries into executable visualization specifications by parsing user intent through an LLM layer, mapping semantic meaning to chart types (bar, line, scatter, etc.), and automatically selecting appropriate data dimensions and aggregations. The system infers visualization intent from conversational input without requiring users to specify chart type, axes, or grouping logic explicitly.","intents":["I want to ask my data a question in plain English and get a chart back without learning visualization syntax","I need to quickly explore what my dataset looks like without writing SQL or using a visual query builder","I want to generate presentation-ready charts from raw data in seconds, not hours"],"best_for":["non-technical business analysts exploring datasets for the first time","freelancers and solopreneurs who need fast exploratory analysis without BI tool training","small teams doing ad-hoc reporting without dedicated data engineering"],"limitations":["LLM-based interpretation may misunderstand ambiguous queries or generate incorrect aggregations for complex multi-table scenarios","No explicit control over visualization parameters — users cannot fine-tune axis ranges, color schemes, or statistical methods without regenerating","Performance degrades on datasets with >100K rows or >50 columns due to context window constraints in LLM processing"],"requires":["Structured tabular data (CSV, Excel, or database table)","Internet connection for LLM inference","Dataset with clear column headers and consistent data types"],"input_types":["natural language query","tabular data (CSV, XLSX, database export)"],"output_types":["interactive chart (SVG or canvas-based)","visualization specification (likely JSON or Vega-Lite format)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_1","uri":"capability://data.processing.analysis.automatic.insight.detection.and.anomaly.surfacing","name":"automatic-insight-detection-and-anomaly-surfacing","description":"Applies statistical analysis and pattern recognition algorithms (likely variance detection, trend analysis, outlier identification) to raw datasets to automatically surface meaningful patterns, anomalies, and correlations without user-defined rules. The system likely computes descriptive statistics, performs time-series decomposition, and flags data points that deviate significantly from expected distributions.","intents":["I want the system to tell me what's interesting or unusual in my data without me having to manually inspect every column","I need to identify anomalies or unexpected patterns that might indicate data quality issues or business opportunities","I want to surface correlations between variables that I might not have thought to investigate"],"best_for":["analysts who lack statistical expertise but need to identify actionable patterns quickly","teams performing root-cause analysis on operational datasets","business users who want data-driven insights without writing custom queries"],"limitations":["Automatic anomaly detection may produce false positives on seasonal or cyclical data without domain-specific tuning","No ability to define custom business rules or domain-specific thresholds for what constitutes an 'insight'","Insights are likely limited to univariate and bivariate analysis — multivariate pattern detection is computationally expensive and may not be included","Cannot distinguish between statistically significant anomalies and business-meaningful ones without user feedback"],"requires":["Minimum dataset size (likely 50+ rows) for statistical significance","Numeric or categorical columns with sufficient variance","No requirement for labeled training data"],"input_types":["tabular data (CSV, XLSX, database export)"],"output_types":["structured insight objects (JSON with anomaly type, severity, affected rows)","natural language summary of detected patterns"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_2","uri":"capability://data.processing.analysis.predictive.analytics.and.forecasting","name":"predictive-analytics-and-forecasting","description":"Applies time-series forecasting or regression models to historical data to generate forward-looking predictions and trend projections. The system likely uses statistical methods (ARIMA, exponential smoothing) or lightweight ML models (linear regression, simple neural networks) to extrapolate patterns and estimate future values with confidence intervals.","intents":["I want to forecast future revenue, sales, or demand based on historical trends","I need to predict the next N periods of a metric without building a custom ML model","I want to understand confidence intervals around predictions to assess forecast reliability"],"best_for":["small business owners doing basic demand forecasting or revenue projections","analysts who need quick predictions without ML expertise","teams evaluating trends before investing in dedicated forecasting infrastructure"],"limitations":["Predictions are likely limited to univariate time-series (single metric) — multivariate forecasting with external features is probably not supported","No ability to incorporate external variables (e.g., marketing spend, seasonality adjustments) into predictions","Forecast accuracy degrades significantly for data with structural breaks, regime changes, or non-stationary patterns","Limited to short-term forecasting (likely 1-3 months ahead) due to model simplicity and data constraints","No retraining or model versioning — predictions use a single static model"],"requires":["Time-series data with consistent temporal intervals (daily, weekly, monthly)","Minimum 20-30 historical data points for statistical validity","Numeric target column with clear temporal ordering"],"input_types":["time-series tabular data (CSV, XLSX with date column)"],"output_types":["forecast values with confidence intervals","trend visualization (line chart with prediction band)","forecast accuracy metrics (MAE, RMSE)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_3","uri":"capability://data.processing.analysis.multi.format.data.ingestion.and.parsing","name":"multi-format-data-ingestion-and-parsing","description":"Accepts data from multiple file formats (CSV, Excel, JSON, potentially database connections) and automatically infers schema, data types, and structure without requiring manual schema definition. The system likely uses heuristic-based type inference (checking first N rows for numeric/date/categorical patterns) and handles common data quality issues like missing values, inconsistent formatting, and encoding mismatches.","intents":["I want to upload a CSV or Excel file and start analyzing immediately without defining column types or data schemas","I need to handle messy real-world data with missing values, inconsistent formatting, or mixed data types","I want to connect to a database or API and pull data without writing connection strings or SQL"],"best_for":["non-technical users who have data in spreadsheets or exports but no data engineering skills","teams doing one-off analysis on ad-hoc datasets","analysts who need fast data onboarding without IT involvement"],"limitations":["Automatic type inference may misclassify columns (e.g., ZIP codes as numbers, dates in ambiguous formats)","No support for nested or semi-structured data (JSON arrays, XML) — likely limited to flat tabular formats","File size limits on free tier (probably <10MB or <100K rows) restrict ability to ingest large datasets","No incremental data refresh or streaming ingestion — each analysis requires full re-upload","Database connections likely not supported on free tier; may require paid plan"],"requires":["Data file in supported format (CSV, XLSX, JSON, or database credentials)","Consistent column headers and row structure","File size within platform limits"],"input_types":["CSV","Excel (XLSX)","JSON","potentially database exports"],"output_types":["normalized tabular representation","inferred schema (column names, data types)","data quality report (missing values, outliers)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_4","uri":"capability://image.visual.interactive.chart.customization.and.export","name":"interactive-chart-customization-and-export","description":"Provides UI controls to modify generated visualizations (colors, labels, axis ranges, legend placement) and export results in multiple formats (PNG, SVG, PDF, potentially interactive HTML). The system likely uses a declarative visualization library (Vega-Lite, Plotly, or similar) that allows parameter adjustments without regenerating the underlying data query.","intents":["I want to adjust colors and labels on a generated chart to match my brand or presentation style","I need to export a chart as an image or PDF for inclusion in a report or presentation","I want to share an interactive version of the chart with stakeholders who can explore the data themselves"],"best_for":["analysts creating presentation-ready visualizations from exploratory analysis","teams sharing insights with non-technical stakeholders","users who need quick customization without learning visualization tools"],"limitations":["Customization options are likely limited to basic styling (colors, fonts, labels) — no ability to create custom chart types or complex multi-panel layouts","Export quality may be lower than native BI tools; SVG/PDF exports may not preserve all interactive features","No version control or collaboration features — multiple users cannot simultaneously edit the same visualization","Interactive HTML exports may not be embeddable in external websites or dashboards without additional configuration"],"requires":["Generated visualization object from Vizly","Export format support (PNG requires browser canvas rendering, PDF requires server-side rendering)"],"input_types":["visualization specification (internal format)"],"output_types":["PNG image","SVG vector graphic","PDF document","interactive HTML (potentially)"],"categories":["image-visual","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_5","uri":"capability://tool.use.integration.collaborative.dashboard.sharing.and.embedding","name":"collaborative-dashboard-sharing-and-embedding","description":"Enables users to share generated visualizations and insights with team members via shareable links or embedded widgets, likely with read-only or limited-edit permissions. The system probably generates unique URLs with access controls and may support embedding charts in external websites or internal wikis via iframe or API.","intents":["I want to share a dashboard or chart with my team without giving them access to the raw data","I need to embed a live or static visualization in a website, wiki, or internal tool","I want to control who can view, edit, or download shared visualizations"],"best_for":["teams collaborating on data analysis and reporting","organizations sharing insights with external stakeholders or clients","teams embedding analytics into internal tools or documentation"],"limitations":["Sharing likely limited to read-only access on free tier — no collaborative editing or commenting","No version history or audit trail for shared visualizations","Embedded charts may not update automatically if underlying data changes — likely static snapshots","Access control is probably basic (public/private) without granular permission management","No integration with SSO or enterprise identity providers"],"requires":["Generated visualization or dashboard in Vizly","Recipient email or shareable link","Internet access for recipients to view shared content"],"input_types":["visualization or dashboard object"],"output_types":["shareable URL","embed code (iframe or script tag)","access token or API key"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_6","uri":"capability://data.processing.analysis.data.quality.assessment.and.validation","name":"data-quality-assessment-and-validation","description":"Automatically analyzes ingested data to identify quality issues (missing values, duplicates, outliers, inconsistent formatting) and provides a quality report with recommendations for cleaning or handling problematic data. The system likely computes completeness metrics, detects duplicate rows, and flags columns with unusual distributions or data type mismatches.","intents":["I want to understand data quality issues before investing time in analysis","I need to identify missing values, duplicates, or inconsistent formatting that might skew results","I want recommendations on how to handle data quality problems without manual inspection"],"best_for":["analysts working with unfamiliar or external datasets","teams validating data before using it for critical decisions","users who lack data engineering expertise but need to assess data reliability"],"limitations":["Quality assessment is likely limited to basic statistical checks — no domain-specific validation rules","No automated data cleaning or transformation — users must manually address identified issues","Cannot detect logical inconsistencies or business rule violations without explicit configuration","Quality scoring may not align with domain-specific requirements (e.g., what constitutes 'acceptable' missing data varies by use case)"],"requires":["Ingested tabular data","Minimum dataset size for statistical validity"],"input_types":["tabular data"],"output_types":["quality report (JSON or HTML)","quality score (0-100 or similar)","list of identified issues with severity levels"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_vizly__cap_7","uri":"capability://data.processing.analysis.multi.dataset.correlation.and.relationship.analysis","name":"multi-dataset-correlation-and-relationship-analysis","description":"Analyzes relationships and correlations across multiple columns or datasets to identify dependencies and predictive relationships. The system likely computes correlation matrices, performs association analysis on categorical variables, and may suggest which variables are most predictive of a target metric.","intents":["I want to understand which variables are most correlated with my target metric","I need to identify hidden relationships between columns that might explain business outcomes","I want to find which factors are most predictive for forecasting or decision-making"],"best_for":["analysts exploring feature importance for predictive modeling","teams identifying root causes of business metrics","users who need to understand variable dependencies without statistical expertise"],"limitations":["Correlation analysis assumes linear relationships — nonlinear dependencies may be missed","No causal inference — correlations are presented without distinguishing correlation from causation","Likely limited to pairwise correlations; higher-order interactions are probably not analyzed","Categorical variable analysis may be limited to simple association measures (Cramér's V) without more sophisticated methods","No control for confounding variables or multicollinearity"],"requires":["Multiple numeric or categorical columns","Sufficient data variance to compute meaningful correlations (likely 30+ rows minimum)"],"input_types":["tabular data with multiple columns"],"output_types":["correlation matrix (numeric or heatmap visualization)","ranked list of correlations by strength","feature importance scores"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":39,"verified":false,"data_access_risk":"high","permissions":["Structured tabular data (CSV, Excel, or database table)","Internet connection for LLM inference","Dataset with clear column headers and consistent data types","Minimum dataset size (likely 50+ rows) for statistical significance","Numeric or categorical columns with sufficient variance","No requirement for labeled training data","Time-series data with consistent temporal intervals (daily, weekly, monthly)","Minimum 20-30 historical data points for statistical validity","Numeric target column with clear temporal ordering","Data file in supported format (CSV, XLSX, JSON, or database credentials)"],"failure_modes":["LLM-based interpretation may misunderstand ambiguous queries or generate incorrect aggregations for complex multi-table scenarios","No explicit control over visualization parameters — users cannot fine-tune axis ranges, color schemes, or statistical methods without regenerating","Performance degrades on datasets with >100K rows or >50 columns due to context window constraints in LLM processing","Automatic anomaly detection may produce false positives on seasonal or cyclical data without domain-specific tuning","No ability to define custom business rules or domain-specific thresholds for what constitutes an 'insight'","Insights are likely limited to univariate and bivariate analysis — multivariate pattern detection is computationally expensive and may not be included","Cannot distinguish between statistically significant anomalies and business-meaningful ones without user feedback","Predictions are likely limited to univariate time-series (single metric) — multivariate forecasting with external features is probably not supported","No ability to incorporate external variables (e.g., marketing spend, seasonality adjustments) into predictions","Forecast accuracy degrades significantly for data with structural breaks, regime changes, or non-stationary patterns","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.31666666666666665,"quality":0.67,"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":"2026-05-24T12:16:34.117Z","last_scraped_at":"2026-04-05T13:23:42.559Z","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=vizly","compare_url":"https://unfragile.ai/compare?artifact=vizly"}},"signature":"vPCXc1z3ChbZL4jW/kfj7v874mfnrN46ggNjdAiaQQgkDSYvD93vlzl4pA/ysXFEP2LAqqIurPi0XAS5Qrm7Ag==","signedAt":"2026-06-20T02:30:16.644Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/vizly","artifact":"https://unfragile.ai/vizly","verify":"https://unfragile.ai/api/v1/verify?slug=vizly","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"}}