Semantic Search capability uses nomic-embed-text component for creating table embeddings to improve table ranking in schema selection. Semantic Search stores table embeddings in Qdrant vector database for fast similarity search and ranking. QuestionRouter uses Semantic search capability to handle unstructured textual queries via QdrantService. DataLens performs semantic search querying Qdrant vectors. Semantic Search using nomic-embed-text relies on Qdrant vector database for vector storage and search. Question Router routes queries to Semantic Search using Qdrant vectors

Semantic search sidebar uses the Qdrant collection for retrieving relevant document chunks based on vector embeddings. DataLens includes a Semantic Search Sidebar displaying matching documents alongside results

Data Discovery feature added with semantic table matching, improving query success rate from 70% to over 95%. Implementation includes /api/v1/discovery endpoints and discovery components, currently not started. The discovery.py service implements semantic table matching through TableIndex.

Session 56 with 3 messages (text, thinking, thinking) corresponds to no entries in the agent_findings table for project 14, indicating no findings created yet. Session 56, which includes messages, is related to the absence of data in the agent_findings table, indicating query execution paths stopping before findings are created.

Validated extraction pipeline on 8 files, confirming successful data load with ongoing issues in summaries and API endpoints.

Validation of 5 uploaded files across extraction, summaries, and feature workflows, confirming readiness of the platform with ongoing blocker fixes.

The command for spawning sessions uses the 'sonnet' model, requiring specific parameters and following strict protocols.

The DiscoveryFlow component is built with the shadcn stack, enabling modern UI features like gradients and animations.

The Frontend uses shadcn-svelte UI components. The Frontend uses shadcn-svelte as a third-party component. The npm dev dependency shadcn-svelte depends on svelte in the frontend project dependencies.

The DataLens platform backend incorporates shadcn-ui as part of its user interface skills. The DataLens platform backend includes the shadcn-ui skill. The Frontend uses shadcn-svelte components The DataLens platform backend integrates the shadcn-ui skill.

The ops user owns and runs the Skill API on the agent server, handling ringfenced SQL execution isolated from the OpenClaw agent service.

Requirements of DataLens Agent Mode are validated by Skill Unit Tests that verify skill correctness on fixture data.

LocalAgentClient uses SkillExecutor to process messages locally with an async generator. IronClawClient.send_message() depends on SkillExecutor or corresponding executor to process messages and yield responses asynchronously. SkillExecutor uses SkillResult as the result from skill execution. SkillExecutor orchestrates agent's ReAct loop and produces SkillResult. LocalAgentClient.send_message uses SkillExecutor to execute and stream query results asynchronously. LocalAgentClient instantiates SkillExecutor internally for processing agent messages. AgentWarmingService assembles warm context that is used by SkillExecutor in agent sessions. RingfencedSkills replace raw SQL skills with constrained operations used by SkillExecutor when executing agent skills. LocalAgentClient directly uses SkillExecutor to run agent logic without IronClaw service dependency.

SkillExecutor uses SkillResult as the result from skill execution. ExploreSchemaSkill produces SkillResult when executing to discover and profile project data schema. QueryDataSkill produces SkillResult when executing natural language queries via Text-to-SQL. DiscoverInsightsSkill uses SkillResult when performing statistical profiling and anomaly detection. VisualizeSkill uses SkillResult when generating Plotly chart specifications. PrepareDataSkill produces SkillResult during data cleaning and transformation executions. GenerateReportSkill uses SkillResult to compile findings into structured reports. SkillExecutor orchestrates agent's ReAct loop and produces SkillResult. ExportService converts SkillResult data into CSV, Excel, or JSON formats.

Contains skills related to data extraction and processing, details not specified.

DataLens follows a mandatory workflow for features which includes referring to the skills/brainstorming/SKILL.md design element for brainstorming steps. DataLens agent mandates following the workflow defined in skills/brainstorming/SKILL.md for new features: Brainstorm, Design, Review, Build. DataLens mandates following the Brainstorm-Design-Review-Build workflow described in skills/brainstorming/SKILL.md for new features and significant changes.

The PPTX extractor implements slide-based chunking, with potential sub-slide splits for dense content.

The PPTX extractor design includes slide layout detection such as title, bullets, and blank layouts to improve chunk semantic understanding.

The smart auto-processing pipeline uses Qdrant for vector search functionality within DataLens. The smart processing capability involves Phase B, which is a smart auto-processing pipeline with Qdrant. The smart processing capability involves Phase B, which is a smart auto-processing pipeline with Qdrant.

The batch upload pipeline is part of the smart processing UX model for DataLens. Phase B involving the smart auto-processing pipeline with Qdrant is part of the smart processing UX model. Phase C, the unified question interface, is part of the smart processing UX model for DataLens. The smart processing capability involves Phase B, which is a smart auto-processing pipeline with Qdrant. The batch upload pipeline uses smart processing to handle uploaded data effectively. DataLens implements smart processing as part of its data handling capabilities.