{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_op","slug":"op","name":"Op","type":"product","url":"https://opapp.io","page_url":"https://unfragile.ai/op","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_op__cap_0","uri":"capability://code.generation.editing.ai.assisted.sql.query.generation.from.natural.language","name":"ai-assisted sql query generation from natural language","description":"Converts natural language questions into executable SQL queries using an LLM backbone, likely with few-shot prompting or fine-tuning on database schema context. The system infers table structure and relationships from the active dataset, then generates syntactically valid queries that execute directly against the underlying data store. This eliminates manual query writing for users unfamiliar with SQL syntax while maintaining full query transparency and editability.","intents":["I want to ask a question about my data in plain English without writing SQL","I need to quickly explore a dataset without memorizing table schemas","I want to generate a starting query that I can then refine manually"],"best_for":["Technical analysts with data questions but limited SQL fluency","Developers prototyping data pipelines who want faster iteration","Teams migrating from pure spreadsheet analysis to structured queries"],"limitations":["Query generation accuracy depends on schema clarity and LLM context window — complex multi-table joins may require manual refinement","No explicit query optimization or cost estimation for large datasets","LLM hallucination risk for non-standard or ambiguous column names"],"requires":["Connected data source (spreadsheet, database, or CSV upload)","Schema metadata accessible to the LLM (column names, types, relationships)","API key for underlying LLM provider (OpenAI, Anthropic, or internal)"],"input_types":["natural language question","database schema metadata"],"output_types":["SQL query string","query execution results (tabular)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_1","uri":"capability://code.generation.editing.python.code.execution.within.spreadsheet.cells","name":"python code execution within spreadsheet cells","description":"Allows users to write and execute Python code directly in spreadsheet cells, with results rendered inline as cell values or multi-row outputs. The execution environment likely uses a sandboxed Python runtime (e.g., Pyodide, Deno, or a containerized backend) with access to common data libraries (pandas, numpy, matplotlib). Cell outputs automatically propagate to dependent cells, creating a reactive computation graph similar to spreadsheet formulas but with full Python expressiveness.","intents":["I want to run Python transformations on my data without leaving the spreadsheet","I need to apply custom logic (regex, statistical functions, ML models) to columns","I want to visualize data inline without exporting to a separate tool"],"best_for":["Data scientists and engineers comfortable with Python who want faster iteration","Teams combining exploratory analysis with reproducible code","Developers building data pipelines that need both interactivity and automation"],"limitations":["Execution latency for large datasets or complex computations may exceed spreadsheet formula performance","Sandboxing overhead and memory constraints limit model size and library availability","Debugging experience is less mature than IDE-based Python development","No built-in version control or cell dependency tracking beyond implicit formula graphs"],"requires":["Python 3.8+ syntax support","Access to standard libraries (pandas, numpy, matplotlib, sklearn, etc.)","Sufficient runtime memory for dataset size (typically <1GB per execution)"],"input_types":["Python code string","cell references (A1:C10 notation)","imported data (CSV, JSON, database query results)"],"output_types":["scalar values (numbers, strings, booleans)","tabular data (DataFrames rendered as cell ranges)","visualizations (matplotlib/plotly charts embedded inline)"],"categories":["code-generation-editing","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_10","uri":"capability://automation.workflow.export.and.report.generation","name":"export and report generation","description":"Allows users to export workbooks or selected cells to multiple formats (CSV, JSON, PDF, HTML) and generate formatted reports with charts, tables, and narrative text. The system can template reports with placeholders for dynamic data, enabling users to create reusable report formats that update automatically when underlying data changes. Exports preserve formatting, visualizations, and cell comments.","intents":["I want to share my analysis with non-technical stakeholders in a readable format","I need to generate a monthly report that updates automatically when new data arrives","I want to export my data to a format that other tools can consume"],"best_for":["Analysts sharing results with business stakeholders","Teams generating recurring reports from shared workbooks","Organizations integrating Op data into downstream systems"],"limitations":["PDF export may lose interactivity (charts become static images)","Report templating is basic — no support for complex layouts or conditional formatting","Large exports (100MB+) may be slow or fail due to memory constraints","No built-in scheduling for automated report generation — requires manual export or external automation"],"requires":["Workbook with data and visualizations to export","Sufficient disk space for exported files"],"input_types":["workbook cells","charts and visualizations","report templates"],"output_types":["CSV files","JSON data","PDF reports","HTML pages"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_11","uri":"capability://data.processing.analysis.database.connection.and.live.query.execution","name":"database connection and live query execution","description":"Establishes persistent connections to SQL databases (PostgreSQL, MySQL, Snowflake, BigQuery, etc.) and executes queries directly against live data without importing. The system manages connection pooling, query timeouts, and result streaming for large result sets. Users can parameterize queries with cell references, enabling dynamic queries that change based on cell values (e.g., 'SELECT * FROM users WHERE age > [A1]').","intents":["I want to query my production database directly without exporting data","I need to create dynamic queries that change based on cell values","I want to work with large datasets that don't fit in memory"],"best_for":["Data engineers and analysts working with production databases","Teams building dashboards that query live data","Organizations with large datasets that can't be imported locally"],"limitations":["Query execution latency depends on database performance — slow queries block cell recalculation","No built-in query optimization or cost estimation for expensive operations","Connection credentials must be stored securely — potential security risk if not handled properly","Result streaming for large datasets may cause memory issues if not paginated"],"requires":["Database connection credentials (host, port, username, password)","Network access to the database server","Appropriate database permissions for the user account"],"input_types":["SQL query string","parameterized queries with cell references","database connection configuration"],"output_types":["query results (tabular data)","execution time and row count metadata"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_12","uri":"capability://data.processing.analysis.ai.powered.data.anomaly.detection.and.suggestions","name":"ai-powered data anomaly detection and suggestions","description":"Automatically analyzes data in cells and suggests potential issues (outliers, missing values, data quality problems) or interesting patterns (correlations, trends) using statistical methods and LLM-based analysis. The system runs in the background and surfaces suggestions as notifications or sidebar recommendations. Users can accept suggestions to apply transformations (e.g., 'remove outliers', 'fill missing values') or dismiss them.","intents":["I want to automatically detect data quality issues without manual inspection","I need suggestions for interesting patterns or correlations in my data","I want to quickly identify and fix common data problems"],"best_for":["Analysts working with unfamiliar datasets who need rapid quality assessment","Teams building data pipelines that need automated quality checks","Exploratory analysts looking for interesting patterns to investigate"],"limitations":["Anomaly detection is heuristic-based — may produce false positives or miss subtle issues","Suggestions are generic and may not match domain-specific knowledge or business rules","No support for custom anomaly definitions or thresholds","Background analysis adds latency and resource overhead, especially for large datasets"],"requires":["Data in cells with sufficient rows for statistical analysis (typically 100+)","LLM API access for pattern analysis"],"input_types":["cell data (numeric, categorical, temporal)"],"output_types":["anomaly alerts","pattern suggestions","recommended transformations"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_2","uri":"capability://code.generation.editing.ai.assisted.python.code.generation.and.completion","name":"ai-assisted python code generation and completion","description":"Provides context-aware code suggestions and auto-completion for Python cells using an LLM trained on code patterns and the current spreadsheet schema. When a user types a partial function or transformation, the system suggests completions based on available columns, imported libraries, and common data manipulation patterns. The LLM likely uses few-shot examples from the current workbook and standard pandas/numpy idioms to generate syntactically correct, runnable code.","intents":["I want code suggestions for common transformations (filtering, grouping, aggregating) without typing boilerplate","I need to apply a function to a column but don't remember the exact syntax","I want to generate a starting implementation that I can then refine"],"best_for":["Python developers who want faster iteration on data transformations","Analysts with some coding experience but imperfect library knowledge","Teams standardizing on pandas idioms across a shared workbook"],"limitations":["Suggestions may not match the user's intent if context is ambiguous or incomplete","LLM-generated code can contain subtle bugs (off-by-one errors, type mismatches) that require manual review","No static type checking or linting feedback before execution","Completion latency (typically 1-3 seconds) may interrupt flow for rapid typing"],"requires":["Active Python cell with partial code","Schema metadata (column names, types) from the current dataset","LLM API access (OpenAI, Anthropic, or internal model)"],"input_types":["partial Python code string","cursor position in cell","spreadsheet schema context"],"output_types":["code completion suggestions (1-5 options)","full function implementations"],"categories":["code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_3","uri":"capability://automation.workflow.reactive.cell.dependency.tracking.and.automatic.recalculation","name":"reactive cell dependency tracking and automatic recalculation","description":"Maintains an implicit dependency graph between cells (both formula-based and code-based) and automatically recalculates downstream cells when upstream data changes. The system tracks which cells reference which data sources and columns, then propagates changes through the graph in topological order. This enables users to modify a source dataset or transformation and see all dependent analyses update in real-time without manual refresh.","intents":["I want to update a source dataset and see all my analyses automatically refresh","I need to understand which cells depend on a given column or transformation","I want to avoid manual recalculation steps when iterating on analysis logic"],"best_for":["Analysts building multi-step analysis pipelines with interdependent calculations","Teams collaborating on shared workbooks where changes propagate automatically","Developers prototyping data workflows that need reactive updates"],"limitations":["Circular dependencies are not handled gracefully — may cause infinite loops or error states","Recalculation latency scales with graph complexity; large workbooks with 100+ cells may experience noticeable delays","No explicit dependency visualization or debugging tools to diagnose recalculation issues","Performance degrades if cells contain expensive computations (ML model inference, large data joins)"],"requires":["Cells with explicit references (A1:C10 notation or column names)","Acyclic dependency graph (no circular references)","Sufficient runtime resources for recalculation of all affected cells"],"input_types":["cell formulas and code","data source updates"],"output_types":["updated cell values","propagated changes to dependent cells"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_4","uri":"capability://data.processing.analysis.schema.inference.and.column.type.detection","name":"schema inference and column type detection","description":"Automatically analyzes imported data (CSV, JSON, database query results) to infer column names, data types (string, number, date, boolean), and basic statistics (min, max, cardinality). The system likely uses heuristic sampling (first N rows) and pattern matching to detect types, then exposes this metadata to the LLM for query generation and code completion. Users can override inferred types manually if needed.","intents":["I want to quickly understand the structure of a new dataset without manual inspection","I need accurate column type information to write correct queries and transformations","I want the AI to generate code that references the correct column names and types"],"best_for":["Analysts working with unfamiliar datasets who need rapid schema understanding","Teams importing data from multiple sources with inconsistent formatting","Developers building automated data pipelines that depend on schema metadata"],"limitations":["Type inference can fail on ambiguous data (e.g., '123' could be string or number) — requires manual override","Sampling-based inference may miss rare types or edge cases in large datasets","No support for complex types (nested JSON, arrays, custom objects) — flattens to scalar types","Cardinality detection may be inaccurate for high-cardinality columns (IDs, timestamps)"],"requires":["Imported data source (CSV, JSON, database connection, or spreadsheet upload)","Sufficient sample size (typically first 1000 rows) for reliable inference"],"input_types":["raw data (CSV, JSON, SQL query results)","user-provided type hints (optional)"],"output_types":["schema metadata (column names, inferred types, statistics)","type override interface for manual correction"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_5","uri":"capability://data.processing.analysis.multi.source.data.import.and.unification","name":"multi-source data import and unification","description":"Supports importing data from multiple sources (CSV files, JSON, SQL databases, cloud storage) and merging them into a unified spreadsheet view. The system handles format conversion, column alignment, and deduplication, allowing users to combine data from heterogeneous sources without manual ETL. Imported data is cached locally for fast access, with optional refresh scheduling for live data sources.","intents":["I want to combine data from a CSV file and a database query in one place","I need to import data from cloud storage (S3, Google Drive) without downloading locally","I want to set up automatic data refresh for live sources without manual re-import"],"best_for":["Analysts combining data from multiple sources for exploratory analysis","Teams building data pipelines that need to unify heterogeneous sources","Developers prototyping data products that integrate multiple APIs"],"limitations":["No built-in data validation or conflict resolution for duplicate rows across sources","Large imports (>100MB) may experience slow load times or memory constraints","Schema mismatches between sources require manual column mapping","Refresh scheduling is basic — no support for incremental updates or change data capture"],"requires":["Connection credentials for external sources (API keys, database credentials, cloud storage access)","Sufficient local storage for cached data","Network connectivity for live data sources"],"input_types":["CSV files","JSON data","SQL database connections","cloud storage URLs (S3, Google Drive, etc.)"],"output_types":["unified spreadsheet view","merged tabular data","import logs and error reports"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_6","uri":"capability://image.visual.inline.data.visualization.with.matplotlib.plotly","name":"inline data visualization with matplotlib/plotly","description":"Renders charts and plots directly in spreadsheet cells using matplotlib or plotly backends, allowing users to visualize data without exporting to external tools. Users write Python code that generates a plot (e.g., `plt.scatter(df['x'], df['y'])`), and the output is displayed as an embedded interactive chart. Visualizations update reactively when underlying data changes, maintaining the same dependency graph as other cells.","intents":["I want to create a chart from my data without leaving the spreadsheet","I need to explore data visually as part of my analysis workflow","I want my visualizations to update automatically when I change the underlying data"],"best_for":["Data analysts who want rapid visual exploration without tool-switching","Teams building interactive dashboards within a single workbook","Developers prototyping data products with embedded visualizations"],"limitations":["Rendering performance degrades for complex plots with 10,000+ data points","Limited customization compared to dedicated visualization tools (Tableau, Power BI)","No built-in interactivity (drill-down, filtering) — requires manual code for interaction","Chart size is constrained by cell dimensions, limiting readability for large datasets"],"requires":["Python code cell with matplotlib or plotly import","Data in a format compatible with plotting libraries (pandas DataFrame, numpy array, list)"],"input_types":["Python plotting code","data from cells or imported sources"],"output_types":["rendered chart (static or interactive)","SVG or HTML representation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_7","uri":"capability://automation.workflow.workbook.sharing.and.collaborative.editing","name":"workbook sharing and collaborative editing","description":"Enables multiple users to edit the same workbook simultaneously with real-time synchronization of cell changes, comments, and execution results. The system likely uses operational transformation (OT) or conflict-free replicated data types (CRDTs) to merge concurrent edits without conflicts. Users can see who is editing which cells, leave comments on specific cells or analyses, and track change history.","intents":["I want to collaborate with teammates on a shared analysis without email back-and-forth","I need to see real-time updates when a colleague modifies a cell or runs code","I want to leave feedback on specific analyses without disrupting the workflow"],"best_for":["Teams collaborating on data analysis projects in real-time","Organizations building shared data products with multiple contributors","Analysts working across time zones who need asynchronous collaboration"],"limitations":["Concurrent execution of expensive computations (ML models, large joins) may cause resource contention","Conflict resolution for simultaneous edits to the same cell is automatic but may not match user intent","No built-in access control beyond basic sharing (all collaborators have equal permissions)","Change history is limited to recent edits — no long-term audit trail for compliance"],"requires":["User account and authentication","Shared workbook link or explicit invite","Network connectivity for real-time sync"],"input_types":["cell edits","code execution","comments and annotations"],"output_types":["synchronized workbook state","change notifications","comment threads"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_8","uri":"capability://automation.workflow.query.result.caching.and.performance.optimization","name":"query result caching and performance optimization","description":"Caches query results and computation outputs to avoid redundant execution when cells are recalculated. The system tracks which queries and code cells have been executed, stores their results in memory or local storage, and reuses cached results if inputs haven't changed. Users can manually clear cache or set cache expiration policies for live data sources.","intents":["I want to avoid re-running expensive queries when I modify unrelated cells","I need faster recalculation for large workbooks with many interdependent cells","I want to work offline with cached data from my last session"],"best_for":["Analysts working with large datasets or expensive computations","Teams iterating rapidly on analysis logic without re-fetching data","Developers building data products that need responsive UX"],"limitations":["Stale cache can lead to incorrect results if source data changes unexpectedly","Cache invalidation logic may miss dependencies, causing inconsistent state","Memory usage grows with cache size — large workbooks may exhaust available RAM","No explicit cache management UI — users must understand implicit caching behavior"],"requires":["Sufficient local storage or memory for cached results","Deterministic queries and code (same inputs always produce same outputs)"],"input_types":["query results","code execution outputs"],"output_types":["cached results","cache hit/miss indicators"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_op__cap_9","uri":"capability://automation.workflow.version.control.and.workbook.history","name":"version control and workbook history","description":"Maintains a version history of workbook changes, allowing users to view previous states, revert to earlier versions, and compare changes between versions. The system likely tracks cell-level changes with timestamps and user attribution, enabling granular rollback and audit trails. Users can create named snapshots (e.g., 'before refactoring') for easy reference.","intents":["I want to undo a mistake and revert to a previous version of my analysis","I need to see who changed what and when for accountability","I want to compare two versions of my analysis to understand what changed"],"best_for":["Teams collaborating on shared analyses who need accountability","Analysts iterating on complex analyses who want to preserve intermediate states","Organizations with compliance requirements for audit trails"],"limitations":["Version history storage grows with workbook size and edit frequency — may consume significant disk space","Reverting to old versions may break dependencies if cells reference external data that has changed","No branching or merging — version control is linear, not suitable for parallel development","Diff visualization is limited to cell-level changes, not semantic analysis of code or logic changes"],"requires":["Sufficient storage for version history","User authentication to track who made changes"],"input_types":["cell edits","code execution","data imports"],"output_types":["version snapshots","change diffs","rollback operations"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"high","permissions":["Connected data source (spreadsheet, database, or CSV upload)","Schema metadata accessible to the LLM (column names, types, relationships)","API key for underlying LLM provider (OpenAI, Anthropic, or internal)","Python 3.8+ syntax support","Access to standard libraries (pandas, numpy, matplotlib, sklearn, etc.)","Sufficient runtime memory for dataset size (typically <1GB per execution)","Workbook with data and visualizations to export","Sufficient disk space for exported files","Database connection credentials (host, port, username, password)","Network access to the database server"],"failure_modes":["Query generation accuracy depends on schema clarity and LLM context window — complex multi-table joins may require manual refinement","No explicit query optimization or cost estimation for large datasets","LLM hallucination risk for non-standard or ambiguous column names","Execution latency for large datasets or complex computations may exceed spreadsheet formula performance","Sandboxing overhead and memory constraints limit model size and library availability","Debugging experience is less mature than IDE-based Python development","No built-in version control or cell dependency tracking beyond implicit formula graphs","PDF export may lose interactivity (charts become static images)","Report templating is basic — no support for complex layouts or conditional formatting","Large exports (100MB+) may be slow or fail due to memory constraints","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"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":"2026-05-24T12:16:31.859Z","last_scraped_at":"2026-04-05T13:23:42.551Z","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=op","compare_url":"https://unfragile.ai/compare?artifact=op"}},"signature":"2wih1lowUvgtW4didd7tNrn484bS86eI4mZNdHwkC+4mRO0ttRuxFDG9DxITEKWy1TEbHXPDOidLGp7esbF8Cw==","signedAt":"2026-06-21T20:54:12.932Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/op","artifact":"https://unfragile.ai/op","verify":"https://unfragile.ai/api/v1/verify?slug=op","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"}}