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Found 92 Skills
StartupBase platform help — community-driven startup discovery directory with free dofollow backlinks (DR39). Covers submission process (social auth, tech startups only), review queue (2-3 months free, 24-hour premium), selection criteria (custom domain, public product, never previously featured), weekly Spark newsletter, market categories, and comparison with other directories. Use when your startup needs more visibility and early adopter traffic, you want the DR39 dofollow backlink from StartupBase, your submission keeps getting rejected, or the review queue is taking too long. Do NOT use for multi-directory launch strategy (use /sales-launch-directory). Do NOT use for Product Hunt launches (use /sales-producthunt).
Notion-style team dashboard rendered as a Live Artifact. A single-page, self-contained HTML dashboard with KPIs, a 7-day sparkline, a real-time activity feed and a linked-database task table — wired to Notion via the Composio connector catalog. Refreshes on demand and when the artifact is opened. Falls back to seeded mock data when no connector is bound, so it works offline / in screenshots / in the picker preview.
Implements Syncfusion Flutter Spark Charts (SfSparkLineChart, SfSparkAreaChart, SfSparkBarChart, SfSparkWinLossChart) for compact, lightweight data visualization. Use when working with micro charts, sparklines, KPI indicators, or inline trend charts in Flutter dashboards. This skill covers chart configuration, data binding, markers, tooltips, and trackball for all four spark chart types.
This skill should be used when designing terminal user interfaces, creating TUI layouts, choosing TUI color schemes, implementing keyboard navigation, building terminal dashboards, or working with any TUI framework. Activates on mentions of TUI design, terminal UI, Ratatui layout, Ink components, Textual widgets, Bubbletea views, terminal color palette, keybinding design, panel layout, split panes, terminal dashboard, box-drawing characters, sparklines, progress bars, modal dialogs, focus management, or terminal accessibility.
Implement, review, or improve data visualizations using Swift Charts. Use when building bar, line, area, point, pie, or donut charts; when adding chart selection, scrolling, or annotations; when plotting functions with vectorized BarPlot, LinePlot, AreaPlot, or PointPlot; when customizing axes, scales, legends, or foregroundStyle grouping; or when creating specialized visualizations like heat maps, Gantt charts, stacked/grouped bars, sparklines, or threshold lines.
Community registry of agent configurations for the AIBTC platform — browse reference configs for arc0btc, spark0btc, iris0btc, loom0btc, and forge0btc, or copy the template to bootstrap a new agent.
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms. Use PROACTIVELY for data pipeline design, analytics infrastructure, or modern data stack implementation.
Use when you need multi-agent orchestration for OpenAI Codex CLI. Triggers on: omx, $plan, $ralph, $team, $autopilot, $deep-interview. v0.11.10 — 30+ agents, 35+ workflow skills, tmux team runtime, sparkshell, explore, ralplan.
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
Guides understanding and working with Apache Beam runners (Direct, Dataflow, Flink, Spark, etc.). Use when configuring pipelines for different execution environments or debugging runner-specific issues.
Databricks SQL query optimizer: analyzes a slow SQL query, rewrites it for speed using SQL-level optimizations only, validates byte-for-byte result equivalence, and benchmarks both versions with statistical significance testing. Use this skill whenever the user wants to optimize, speed up, tune, or benchmark a SQL query on Databricks. Trigger on: "/databricks-sql-autotuner", "optimize this SQL", "make this query faster", "tune my Databricks query", "benchmark SQL on Databricks", "speed up this spark SQL", "SQL performance on Databricks", "EXPLAIN this query", "why is my query slow on Databricks", "SQL query optimization Databricks", or whenever a user pastes a SQL query and mentions performance, slowness, or runtime.
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".