{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_askcsv","slug":"askcsv","name":"AskCSV","type":"product","url":"https://askcsv.com","page_url":"https://unfragile.ai/askcsv","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_askcsv__cap_0","uri":"capability://data.processing.analysis.natural.language.to.sql.query.translation","name":"natural language to sql query translation","description":"Converts plain English questions into executable SQL queries through an LLM-based semantic parsing pipeline. The system likely uses prompt engineering or fine-tuned models to map natural language intent to SQL syntax, handling entity recognition (column names, aggregation functions) and query structure inference. This eliminates the need for users to write SQL manually while maintaining query correctness for standard analytical operations.","intents":["Ask questions about CSV data without knowing SQL syntax","Quickly explore datasets by phrasing questions conversationally","Generate queries for filtering, aggregation, and basic joins without technical knowledge"],"best_for":["Non-technical business users and analysts","Marketing teams analyzing campaign data exports","Researchers exploring survey or experimental datasets"],"limitations":["Query complexity ceiling is low—complex window functions, CTEs, and multi-table operations often fail or require manual SQL intervention","Column naming ambiguity can cause incorrect query generation if CSV headers are unclear or contain special characters","No support for domain-specific SQL dialects or database-specific functions beyond standard SQL"],"requires":["CSV file with properly formatted headers","Internet connection for LLM inference","Reasonable query complexity (simple filters, aggregations, basic joins only)"],"input_types":["natural language text (English questions)","CSV file structure (inferred from headers)"],"output_types":["SQL query string","query execution results (rows/columns)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_1","uri":"capability://image.visual.automatic.data.visualization.generation","name":"automatic data visualization generation","description":"Generates appropriate charts and visualizations (bar charts, line graphs, scatter plots, etc.) based on query results and inferred data semantics. The system analyzes result structure (dimensions vs measures, cardinality, data types) to recommend visualization types, then renders interactive charts. This removes the manual step of selecting chart types and configuring axes, making insights immediately visual.","intents":["Visualize query results without manually configuring chart types","Quickly generate presentation-ready charts from CSV analysis","Explore data patterns through multiple visualization perspectives"],"best_for":["Business users creating reports and dashboards","Analysts who need quick visual summaries for stakeholder presentations","Teams without dedicated data visualization expertise"],"limitations":["Visualization recommendations are heuristic-based and may not match domain-specific best practices for specialized data types","Limited customization options—users cannot easily adjust colors, labels, or chart styling beyond basic presets","No support for advanced visualization types (Sankey diagrams, network graphs, geographic maps) common in professional BI tools"],"requires":["Query results with at least one dimension and one measure","Data types properly inferred from CSV (numeric vs categorical)"],"input_types":["structured query results (rows and columns with typed data)"],"output_types":["interactive chart (SVG or canvas-based rendering)","chart configuration metadata (chart type, axes, legend)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_2","uri":"capability://data.processing.analysis.csv.file.upload.and.schema.inference","name":"csv file upload and schema inference","description":"Accepts CSV file uploads and automatically infers schema (column names, data types, cardinality) without requiring manual schema definition. The system parses CSV headers, samples rows to detect data types (numeric, categorical, date, text), and builds an internal representation of the dataset structure. This schema is then used for query generation and visualization recommendations, enabling zero-configuration data exploration.","intents":["Upload a CSV file and immediately start asking questions without schema setup","Automatically detect column data types and relationships","Enable quick data exploration without manual metadata configuration"],"best_for":["Users with ad-hoc CSV files from exports or downloads","Teams that need rapid exploratory analysis without ETL setup","Non-technical users who lack database administration skills"],"limitations":["Schema inference is heuristic-based and may misclassify data types (e.g., ZIP codes as numeric, dates in non-standard formats)","Limited to CSV format only—no native support for Excel, Parquet, JSON, or database connections","File size limits likely exist (typical cloud services cap at 100MB–1GB) for performance reasons","No support for multi-file joins or relationships—each CSV is analyzed in isolation"],"requires":["CSV file with headers in first row","File size within platform limits (likely <100MB)","Standard CSV formatting (comma-delimited, proper escaping)"],"input_types":["CSV file (text/csv MIME type)"],"output_types":["schema metadata (column names, inferred types, cardinality)","data preview (sample rows)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_3","uri":"capability://planning.reasoning.interactive.query.refinement.and.result.exploration","name":"interactive query refinement and result exploration","description":"Provides an interactive interface where users can ask follow-up questions, refine previous queries, and drill down into results without starting from scratch. The system maintains query context and conversation history, allowing users to ask relative questions like 'show me the top 5' or 'break that down by region' without re-specifying the full query. This conversational interaction pattern reduces friction for iterative data exploration.","intents":["Ask follow-up questions that build on previous query results","Drill down into specific data subsets without rewriting queries","Refine queries iteratively based on initial results"],"best_for":["Analysts performing exploratory data analysis with multiple iterations","Users discovering insights through conversational interaction","Teams collaborating on data exploration with shared context"],"limitations":["Context window is limited—very long conversation histories may lose earlier context or cause query generation errors","Relative references ('that', 'it', 'the previous result') can be ambiguous and lead to incorrect query interpretation","No explicit session management or query history export—users cannot easily save or share specific query sequences"],"requires":["Active session with uploaded CSV","Previous query results to reference"],"input_types":["natural language follow-up questions","references to previous results"],"output_types":["refined SQL query","updated result set","conversation history"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_4","uri":"capability://data.processing.analysis.data.filtering.and.aggregation.via.natural.language","name":"data filtering and aggregation via natural language","description":"Translates natural language filter and aggregation requests into SQL WHERE, GROUP BY, and aggregate function clauses. The system recognizes intent patterns like 'show me sales over $1000', 'count by region', or 'average price per category' and maps them to appropriate SQL operations. This capability handles common analytical operations without requiring users to understand SQL syntax for filtering, grouping, or calculating summaries.","intents":["Filter data by conditions without writing WHERE clauses","Group data by dimensions and calculate aggregates (sum, count, average)","Create pivot-like summaries from CSV data"],"best_for":["Business analysts performing standard aggregations","Marketing teams analyzing campaign metrics by segment","Finance teams summarizing transaction data"],"limitations":["Complex filtering logic (nested conditions, OR/AND combinations) may be misinterpreted or fail","Advanced aggregations (window functions, running totals, percentile calculations) are not supported","No support for custom expressions or calculated fields—users cannot define derived metrics"],"requires":["CSV with numeric and categorical columns","Clear column names that map to natural language terms"],"input_types":["natural language filter and aggregation requests"],"output_types":["SQL WHERE and GROUP BY clauses","aggregated result set"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_5","uri":"capability://automation.workflow.freemium.usage.tier.with.query.limits","name":"freemium usage tier with query limits","description":"Implements a freemium pricing model with free tier limits on query execution, file uploads, or storage to encourage conversion to paid plans. The system tracks usage metrics (queries per month, files uploaded, storage used) and enforces soft or hard limits that either throttle performance or require upgrade. This enables users to test core functionality without payment while monetizing power users and teams.","intents":["Test the platform with real data before committing to paid plan","Use the tool for occasional analysis without subscription cost","Evaluate whether the tool meets team needs before budget approval"],"best_for":["Individual users and small teams evaluating the platform","Budget-conscious organizations testing tools before enterprise adoption","Occasional users with infrequent analysis needs"],"limitations":["Free tier limits may be restrictive (e.g., 10 queries/month, 1 file at a time) and frustrate users mid-analysis","Upgrade friction—users may abandon the tool rather than pay if free tier is too limited","No clear communication of upgrade path or pricing may lead to user confusion"],"requires":["User account creation","Acceptance of usage tracking and analytics"],"input_types":["user account metadata","usage events (queries, uploads)"],"output_types":["usage quota enforcement","upgrade prompts"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_6","uri":"capability://safety.moderation.session.based.data.isolation.and.privacy.handling","name":"session-based data isolation and privacy handling","description":"Manages user sessions and data isolation by storing uploaded CSV files on external servers with session-scoped access controls. Each user session maintains isolated access to their uploaded data, and files are processed server-side for query execution. However, the system's data retention policies and encryption practices are not transparently documented, creating privacy concerns for sensitive data.","intents":["Upload and analyze CSV data without local processing","Maintain separate analysis sessions for different datasets","Ensure uploaded data is not visible to other users"],"best_for":["Users with non-sensitive CSV data (public datasets, anonymized exports)","Teams analyzing internal data with basic privacy requirements","Organizations without strict data residency or compliance requirements"],"limitations":["Data uploaded to external servers—no guarantee of encryption in transit or at rest","Data retention policies are unclear—files may be retained longer than necessary for analysis","No support for on-premise or private deployment—all data must be sent to cloud servers","Compliance risk for regulated data (HIPAA, GDPR, PCI-DSS)—unclear how the platform handles data deletion or audit trails","No end-to-end encryption option—users must trust the platform's security practices"],"requires":["User account and authentication","Internet connection for file upload","Acceptance of data being processed on external servers"],"input_types":["CSV file","user session token"],"output_types":["session-scoped data access","query results"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_askcsv__cap_7","uri":"capability://automation.workflow.query.result.caching.and.performance.optimization","name":"query result caching and performance optimization","description":"Caches query results and inferred schemas to reduce redundant computation and improve response times for repeated or similar queries. The system likely stores results in memory or a fast cache layer, enabling instant retrieval of previously executed queries and faster execution of similar queries through cache hits. This optimization is critical for interactive exploration where users may ask similar questions multiple times.","intents":["Get instant results when re-running the same query","Improve response time for iterative analysis","Reduce server load from redundant query execution"],"best_for":["Users performing iterative exploratory analysis","Teams with limited server resources","Scenarios with repeated queries across multiple users"],"limitations":["Cache invalidation strategy is unclear—users may see stale results if CSV is updated","Cache size limits may cause eviction of older results, reducing hit rates","No explicit cache control options—users cannot force fresh results or clear cache"],"requires":["Previous query execution","Identical or similar query structure"],"input_types":["query hash or similarity metric"],"output_types":["cached result set","cache hit/miss metadata"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["CSV file with properly formatted headers","Internet connection for LLM inference","Reasonable query complexity (simple filters, aggregations, basic joins only)","Query results with at least one dimension and one measure","Data types properly inferred from CSV (numeric vs categorical)","CSV file with headers in first row","File size within platform limits (likely <100MB)","Standard CSV formatting (comma-delimited, proper escaping)","Active session with uploaded CSV","Previous query results to reference"],"failure_modes":["Query complexity ceiling is low—complex window functions, CTEs, and multi-table operations often fail or require manual SQL intervention","Column naming ambiguity can cause incorrect query generation if CSV headers are unclear or contain special characters","No support for domain-specific SQL dialects or database-specific functions beyond standard SQL","Visualization recommendations are heuristic-based and may not match domain-specific best practices for specialized data types","Limited customization options—users cannot easily adjust colors, labels, or chart styling beyond basic presets","No support for advanced visualization types (Sankey diagrams, network graphs, geographic maps) common in professional BI tools","Schema inference is heuristic-based and may misclassify data types (e.g., ZIP codes as numeric, dates in non-standard formats)","Limited to CSV format only—no native support for Excel, Parquet, JSON, or database connections","File size limits likely exist (typical cloud services cap at 100MB–1GB) for performance reasons","No support for multi-file joins or relationships—each CSV is analyzed in isolation","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"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":"2026-04-05T13:23:42.552Z","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=askcsv","compare_url":"https://unfragile.ai/compare?artifact=askcsv"}},"signature":"PUldKWm6T/70eOuK9De0VEeLBFocbDQcjNuFZWYrdQUSdfos5ghMDZFaXnutQnyq4R41wzpoaTFgoCIVNE+6CQ==","signedAt":"2026-06-20T23:38:16.120Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/askcsv","artifact":"https://unfragile.ai/askcsv","verify":"https://unfragile.ai/api/v1/verify?slug=askcsv","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"}}