{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_skills-ai","slug":"skills-ai","name":"Skills.ai","type":"product","url":"https://skills.ai","page_url":"https://unfragile.ai/skills-ai","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_skills-ai__cap_0","uri":"capability://data.processing.analysis.natural.language.to.sql.query.translation","name":"natural-language-to-sql query translation","description":"Converts free-form natural language questions into executable SQL queries through a conversational interface, using LLM-based semantic understanding to map user intent to database schema. The system likely maintains schema awareness and context from previous queries to improve translation accuracy and handle follow-up questions that reference earlier results.","intents":["I want to ask questions about my data without learning SQL syntax","I need to quickly explore a dataset by asking conversational questions","I want to refine my data questions iteratively through follow-up clarifications"],"best_for":["non-technical business analysts and executives","teams without dedicated data engineers","organizations doing ad-hoc exploratory analysis"],"limitations":["Struggles with complex multi-step queries involving nested subqueries or window functions","May produce inconsistent SQL for semantically similar questions due to LLM variance","Limited ability to handle domain-specific jargon or ambiguous business terminology without explicit schema mapping","No explicit error recovery — failed queries may require manual SQL intervention"],"requires":["Connected data source (database, data warehouse, or CSV upload)","Schema metadata accessible to the LLM context","API key or authentication credentials for the underlying data store"],"input_types":["natural language text","conversational follow-up questions"],"output_types":["SQL query","query results (tabular data)","natural language summary of results"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_1","uri":"capability://memory.knowledge.conversational.data.exploration.with.context.retention","name":"conversational data exploration with context retention","description":"Maintains conversational state across multiple turns, tracking previous queries, results, and user intent to enable follow-up questions that reference earlier analysis. The system builds an implicit context window that allows users to ask 'show me the top 5' after a broader query without re-specifying the dataset or filters.","intents":["I want to drill down into results from my previous question","I want to ask follow-up questions that build on earlier analysis","I want the system to remember what dataset I'm analyzing across multiple questions"],"best_for":["exploratory data analysts conducting iterative investigations","business users doing root-cause analysis through progressive questioning","teams collaborating on shared data exploration sessions"],"limitations":["Context window is limited — very long conversation histories may lose earlier context or become confused","No explicit session persistence — context is lost if conversation is closed or user disconnects","Ambiguous pronouns or references in follow-up questions may be misinterpreted if context is unclear","No built-in conflict resolution if user asks contradictory follow-up questions"],"requires":["Active session or conversation thread","Sufficient LLM context window to retain prior queries and results","Stateful backend to track conversation history"],"input_types":["natural language follow-up questions","implicit references to prior results"],"output_types":["refined query results","contextual natural language explanations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_2","uri":"capability://data.processing.analysis.schema.aware.data.source.integration","name":"schema-aware data source integration","description":"Automatically introspects connected data sources (databases, data warehouses, CSV uploads) to extract and maintain schema metadata (table names, column names, data types, relationships), making this metadata available to the LLM for accurate query generation. The system likely caches schema information and updates it on-demand to ensure the LLM has current understanding of available data.","intents":["I want to connect my database and immediately start asking questions without manual schema setup","I want the system to understand my data structure automatically","I want to add new tables or columns and have them immediately available for querying"],"best_for":["organizations with multiple data sources requiring unified access","teams that frequently add or modify database schemas","users who want zero-configuration data exploration"],"limitations":["Schema introspection may be slow for very large databases with thousands of tables or columns","No explicit handling of schema ambiguity (e.g., multiple tables with similar names or columns)","Limited support for complex data types (JSON, arrays, nested structures) — may fall back to treating them as opaque strings","Schema changes may not be reflected immediately if caching is used"],"requires":["Direct database connection with read permissions","Supported data source (PostgreSQL, MySQL, Snowflake, BigQuery, etc. — specific list unknown)","Network access from Skills.ai infrastructure to the data source"],"input_types":["database connection credentials","data source configuration"],"output_types":["schema metadata (tables, columns, types)","relationship mappings"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_3","uri":"capability://text.generation.language.natural.language.result.summarization.and.insight.extraction","name":"natural language result summarization and insight extraction","description":"Automatically generates human-readable summaries and highlights key insights from query results using LLM-based text generation, translating raw tabular data into narrative explanations of trends, anomalies, or patterns. The system likely applies heuristics to identify statistically significant findings and present them in business-friendly language.","intents":["I want the system to tell me what the data means, not just show me numbers","I want to quickly understand key trends or anomalies in my results","I want insights presented in plain English rather than raw tables"],"best_for":["executives and non-technical stakeholders who need quick insights","analysts who want to accelerate the interpretation phase of analysis","teams creating reports or presentations from data"],"limitations":["Summaries may miss domain-specific context or business rules that affect interpretation","LLM-generated insights can be inaccurate or misleading if the model misunderstands the data context","No statistical rigor — insights are narrative rather than statistically validated","Summaries may be verbose or miss subtle patterns that a domain expert would catch"],"requires":["Query results in tabular format","LLM with sufficient context window to process result sets"],"input_types":["tabular query results","optional context about business domain or analysis goals"],"output_types":["natural language summary","highlighted key findings","narrative explanation of trends or anomalies"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_4","uri":"capability://planning.reasoning.multi.turn.conversational.refinement.with.clarification","name":"multi-turn conversational refinement with clarification","description":"Handles ambiguous or incomplete user questions by asking clarifying questions in natural language, then refining the query based on user responses. The system uses LLM-based intent detection to identify when a question is ambiguous and generates targeted clarification prompts rather than failing silently or returning unexpected results.","intents":["I asked a vague question and want the system to ask me to clarify","I want to iteratively narrow down what I'm looking for through conversation","I want the system to catch misunderstandings before executing a query"],"best_for":["users exploring unfamiliar datasets who may ask imprecise questions","teams collaborating on analysis where intent may not be immediately clear","organizations using Skills.ai as a training tool for data literacy"],"limitations":["Clarification logic is heuristic-based and may ask unnecessary questions or miss genuine ambiguities","Users may find excessive clarification prompts frustrating in rapid-fire exploration workflows","No explicit handling of domain-specific ambiguity (e.g., 'revenue' could mean gross or net)","Clarification questions are generated by LLM and may be poorly phrased or confusing"],"requires":["Active conversational session","LLM capable of intent detection and clarification generation"],"input_types":["ambiguous natural language questions"],"output_types":["clarification prompts","refined queries based on user responses"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_5","uri":"capability://automation.workflow.freemium.access.with.usage.based.tier.progression","name":"freemium access with usage-based tier progression","description":"Implements a freemium pricing model where users can access core natural language querying capabilities at no cost, with paid tiers unlocking higher query volumes, advanced features, or premium data sources. The system tracks usage metrics (queries executed, data scanned, results returned) and presents upgrade prompts when users approach tier limits.","intents":["I want to try the tool without committing to a paid plan","I want to understand pricing before deciding to upgrade","I want to scale my usage as my team grows"],"best_for":["startups and small teams evaluating data tools","individual contributors testing the tool before recommending to their organization","organizations with variable data analysis needs"],"limitations":["Free tier may have restrictive query limits that frustrate power users","Unclear pricing structure or hidden costs may deter upgrade decisions","No explicit information on data retention or privacy policies for free users","Freemium model may incentivize feature limitations that reduce free tier utility"],"requires":["User account creation","Email or authentication credentials"],"input_types":["user registration information"],"output_types":["account tier assignment","usage tracking and billing information"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_6","uri":"capability://data.processing.analysis.data.source.agnostic.query.execution","name":"data source agnostic query execution","description":"Abstracts away data source-specific SQL dialects and query patterns, allowing the same natural language question to be executed against different databases (PostgreSQL, MySQL, Snowflake, BigQuery, etc.) without user intervention. The system translates the generated SQL into the appropriate dialect for each data source and handles source-specific optimizations or limitations.","intents":["I want to ask the same question across multiple databases without rewriting queries","I want to migrate my data source without changing how I ask questions","I want to query data from multiple sources in a single question"],"best_for":["organizations with heterogeneous data infrastructure","teams migrating between data platforms","enterprises with data spread across multiple systems"],"limitations":["SQL dialect translation may introduce subtle bugs or performance issues for complex queries","Not all SQL features are portable across databases — some queries may fail on certain sources","Cross-database joins or federated queries are not explicitly mentioned — likely unsupported","Performance optimization is database-specific and may not be applied consistently"],"requires":["Supported data source (specific list unknown)","Connection credentials for each data source","Network access from Skills.ai to all data sources"],"input_types":["natural language questions"],"output_types":["database-specific SQL","query results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_skills-ai__cap_7","uri":"capability://data.processing.analysis.csv.and.file.based.data.upload.with.inline.analysis","name":"csv and file-based data upload with inline analysis","description":"Allows users to upload CSV, Excel, or other tabular files directly into Skills.ai for immediate natural language querying, without requiring a database connection. The system likely creates a temporary or persistent table from the uploaded file and makes it immediately queryable through the same conversational interface.","intents":["I have a CSV file and want to ask questions about it without setting up a database","I want to quickly analyze an exported dataset without importing it into a database","I want to share a data file with a colleague and have them query it immediately"],"best_for":["analysts working with ad-hoc datasets or exports","non-technical users who don't have database access","teams doing quick exploratory analysis on shared files"],"limitations":["File size limits are likely restrictive — very large CSVs may not upload or may be slow to query","No explicit handling of data quality issues (missing values, inconsistent types, duplicates)","Uploaded files may not persist across sessions — data may be lost if user logs out","No built-in data validation or schema inference — malformed CSVs may produce unexpected results"],"requires":["CSV, Excel, or other supported tabular file format","File size within platform limits (unknown)"],"input_types":["CSV files","Excel files","other tabular formats"],"output_types":["queryable dataset","query results"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Connected data source (database, data warehouse, or CSV upload)","Schema metadata accessible to the LLM context","API key or authentication credentials for the underlying data store","Active session or conversation thread","Sufficient LLM context window to retain prior queries and results","Stateful backend to track conversation history","Direct database connection with read permissions","Supported data source (PostgreSQL, MySQL, Snowflake, BigQuery, etc. — specific list unknown)","Network access from Skills.ai infrastructure to the data source","Query results in tabular format"],"failure_modes":["Struggles with complex multi-step queries involving nested subqueries or window functions","May produce inconsistent SQL for semantically similar questions due to LLM variance","Limited ability to handle domain-specific jargon or ambiguous business terminology without explicit schema mapping","No explicit error recovery — failed queries may require manual SQL intervention","Context window is limited — very long conversation histories may lose earlier context or become confused","No explicit session persistence — context is lost if conversation is closed or user disconnects","Ambiguous pronouns or references in follow-up questions may be misinterpreted if context is unclear","No built-in conflict resolution if user asks contradictory follow-up questions","Schema introspection may be slow for very large databases with thousands of tables or columns","No explicit handling of schema ambiguity (e.g., multiple tables with similar names or columns)","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":"2026-05-24T12:16:33.096Z","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=skills-ai","compare_url":"https://unfragile.ai/compare?artifact=skills-ai"}},"signature":"ww8JStfQi3hPPvGTUxED0v30Rm/gv8em6y8jyGD2keZuLWSWvMc6CmVhF36nLT2n1fQu2td9oZENXI0krE9jCA==","signedAt":"2026-06-20T08:27:05.948Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/skills-ai","artifact":"https://unfragile.ai/skills-ai","verify":"https://unfragile.ai/api/v1/verify?slug=skills-ai","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"}}