insightpulse-superset-platform-admin
Design, deploy, upgrade, and operate the InsightPulseAI Superset-based BI platform on the user's infrastructure with secure, stable, scalable configs.
Boost efficiency with task automation and organizers.
Design, deploy, upgrade, and operate the InsightPulseAI Superset-based BI platform on the user's infrastructure with secure, stable, scalable configs.
Query and analyze Claude Code observability data (metrics, logs, traces). Use when analyzing performance, costs, errors, tool usage, sessions, conversations, or subagents.
Provides quick reference for BI Dashboard (Plotly Dash) commands and operations. Activates when user asks how to run, test, or verify the BI Dashboard. Includes startup, shutdown, verification, and troubleshooting procedures.
AGGRESSIVELY use TOON v2.0 format for biggish regular data (≥5 items, ≥60% uniform). Auto-applies to tables, logs, events, transactions, analytics, API responses, database results. Supports 3 array types (inline, tabular, expanded), 3 delimiters (comma, tab, pipe), key folding for nested objects. Triggers on structured data, arrays, repeated patterns. Use TOON by default when tokens matter - RAG pipelines, tool calls, agents, benchmarks. Keywords "data", "array", "list", "table", "log", "transaction", "metric", "analytics", "API", "database", "query", "TOON".
AGGRESSIVELY use TOON v2.0 format for biggish regular data (≥5 items, ≥60% uniform). Auto-applies to tables, logs, events, transactions, analytics, API responses, database results. Supports 3 array types (inline, tabular, expanded), 3 delimiters (comma, tab, pipe), key folding for nested objects. Triggers on structured data, arrays, repeated patterns. Use TOON by default when tokens matter - RAG pipelines, tool calls, agents, benchmarks. Keywords "data", "array", "list", "table", "log", "transaction", "metric", "analytics", "API", "database", "query", "TOON".
Create new blog posts and data analyses for the Data Blog. Use when the user wants to create a new blog post, add a new analysis, or set up a new data visualization. This includes creating the content.mdx file with proper frontmatter, setting up the directory structure (data/, results/, src/), and implementing dashboard components with charts, tables, and maps.
Comprehensive multi-dimensional skill reviews across structure, content, quality, usability, and integration. Task-based operations with automated validation, manual assessment, scoring rubrics, and improvement recommendations. Use when reviewing skills, ensuring quality, validating production readiness, identifying improvements, or conducting quality assurance.
Generate and update HTML dashboards for LLM usage (Claude, Gemini, Kiro, VS code, Cline, etc). Use when the user wants to visualize their AI coding assistant usage statistics, view metrics in a web interface, or analyze historical trends.
Process Excel files with data manipulation, formula generation, and chart creation. Use when working with spreadsheets or Excel data.
Create Architecture Decision Records (ADRs) documenting significant technical decisions for the FF Analytics platform. This skill should be used when making architectural choices, evaluating alternatives for data models or infrastructure, documenting trade-offs, or when the user asks "should we use X or Y approach?" Guides through the ADR creation workflow from context gathering to documentation.
Create, validate, test, and manage data contracts using the Open Data Contract Specification (ODCS) and the datacontract CLI. Use when working with data contracts, ODCS specifications, data quality rules, or when the user mentions datacontract CLI or data contract workflows.
Prisma ORM and PostgreSQL database management for EFT-Tracker. Handles schema design, migrations, client generation, and Neon database branches. Activates when user mentions: database, schema, migration, Prisma, model, relation, PostgreSQL, Neon, db push, db pull.
Backend patterns for Convex serverless database including schema definitions, queries, mutations, indexes, and Clerk authentication integration. Use for database operations, API endpoints, auth checks.
Automate the end-to-end process of handling a new API request, from model generation to Data Source integration.
Use for Rapidata Python SDK tasks: authenticate with RapidataClient, submit labeling orders from datasets (compare/classification/ranking/free-text/locate/draw/select-words/timestamp), apply filters/settings/selections, create and manage validation sets, monitor or delete orders, retrieve results, and run MRI benchmarking (benchmarks/leaderboards/evaluations/standings). Trigger when the user wants to send data to Rapidata, control annotator targeting, improve quality with validation or early stopping, or manage Rapidata orders/results.
Result backend configuration patterns for Celery including Redis, Database, and RPC backends with serialization, expiration policies, and performance optimization. Use when configuring result storage, troubleshooting result persistence, implementing custom serializers, migrating between backends, optimizing result expiration, or when user mentions result backends, task results, Redis backend, PostgreSQL results, result serialization, or backend migration.
Creates Robot Framework test cases for uploading files to SnapLogic SLDB. Use when the user wants to upload files (JSON, CSV, expression libraries, pipelines, JAR files, etc.), needs to know which destination path to use, or wants to see file upload test case examples.
Monitor and troubleshoot dual-pipeline data collection systems on GCP. This skill should be used when checking pipeline health, viewing logs, diagnosing failures, or monitoring long-running operations for data collection workflows. Supports Cloud Run Jobs (batch pipelines) and VM systemd services (real-time streams).
Use when working with DuckDB databases, Makefiles, or building/deploying notebooks. Triggers on DuckDB queries, database creation, Makefile editing, make targets (build, data, etl), GitHub Actions workflows, CI/CD, and creating new notebook repositories.
Creates Robot Framework test cases for uploading files to SnapLogic SLDB. Use when the user wants to upload files (JSON, CSV, expression libraries, pipelines, JAR files, etc.), needs to know which destination path to use, or wants to see file upload test case examples.
Expert on GigLedger's database schema, collections, data models, DTOs, caching strategy, and cost control. Use when designing database schemas, creating DTOs, understanding data flow from database, or optimizing queries. References docs/05_data_model_and_schema.md.