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Found 2,443 Skills
Hypothesis-driven deep research swarm. Spawns specialist sub-agents to investigate a task across codebase patterns, web sources, MCP tools, installed skills, and project dependencies — with evidence grading and adversarial challenge. Activates on: research, investigate, discover, deep research, how should I, what's the best way, explore options, analyze approaches, scout, prior art, feasibility.
Manages Atlassian Jira and Confluence via the Rovo MCP Server. Handles MCP setup, OAuth authentication, and troubleshooting. Runs agentic project management: Confluence plans, Jira Epics with child tickets, agent team coordination, and resuming interrupted work from Jira state. Supports uploading images/attachments to Confluence pages via REST API. Reads and writes Confluence page comments (footer, inline, reply threads). Creates git branches linked to Jira tickets (GitHub and Bitbucket). Use this skill whenever the user mentions Jira, Confluence, Atlassian, tickets, epics, sprints, project boards, wiki pages, or Confluence spaces. Also trigger when the user wants to plan a project, break work into tasks, track progress, resume interrupted work, upload images to wiki pages, manage comments on Confluence pages, or create git branches linked to tickets — even if they don't mention Atlassian by name.
Autonomous workflow execution pipeline with CSV wave engine. Session discovery → plan validation → IMPL-*.json → CSV conversion → wave execution via spawn_agents_on_csv → results sync. Task JSONs remain the rich data source; CSV is brief + execution state.
Requirement planning to wave-based CSV execution pipeline. Decomposes requirement into dependency-sorted CSV tasks, computes execution waves, runs wave-by-wave via spawn_agents_on_csv with cross-wave context propagation.
Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.
AIWorkflowOrchestratorの正本仕様を `references/` から検索・参照・更新するスキル。 resource-map / quick-reference / topic-map / keywords を起点に、必要最小限の文書だけを段階的に読む。 用途: 要件確認、設計/API/IPC契約確認、UI/状態管理/セキュリティ判断、task-workflow・lessons-learned・未タスク同期。 特に safeInvoke timeout、settings bypass、skill lifecycle、global nav、Skill Center / Workspace / Agent / Skill Creator の導線再編を扱う。 Anchors: • Specification-Driven Development / 適用: 正本仕様同期 / 目的: 実装-仕様整合の維持 • Progressive Disclosure / 適用: resource-map起点読込 / 目的: 必要最小限参照で漏れ防止 Trigger: 仕様確認, 仕様更新, task-workflow同期, lessons-learned同期, UI仕様反映, API/IPC契約確認, セキュリティ要件確認, safeInvoke, timeout, settings bypass, skill lifecycle, Skill Center, Workspace, Agent, Skill Creator, navContract, GlobalNavStrip, MobileNavBar, SkillManagementPanel, line budget reform, spec splitting, family split, generated index sharding
Generate AI images using Gemini or GPT APIs directly. Covers model selection (Gemini for scenes, GPT for transparent icons), the 5-part prompting framework, API calling patterns, multi-turn editing, and quality assurance. Produces photorealistic scenes, icons, illustrations, OG images, and product shots. Use when building websites that need images, creating marketing assets, or generating visual content. Triggers: 'generate image', 'ai image', 'create hero image', 'make an icon', 'generate illustration', 'create og image', 'ai art', 'image generation'.
Deep research and discovery before building something new. Explores local projects for reusable code, researches competitors, reads forums and reviews, analyses plugin ecosystems, investigates technical options, and produces a comprehensive research brief. Three depths: focused (30 min), wide (1-2 hours), deep (3-6 hours). Triggers: 'research this', 'deep research', 'discovery', 'explore the space', 'what should I build', 'competitive analysis', 'before I start building', 'research before coding'.
Track parcels and check delivery status for Australian and international couriers. Searches Gmail for dispatch/shipping emails and provides tracking links for all major Australian couriers including AusPost, StarTrack, Aramex, CouriersPlease, Sendle, Toll, Team Global Express, DHL, FedEx, TNT, Hunter Express, Border Express, Direct Freight Express, and UPS. Triggers: 'where is my parcel', 'track my order', 'has my package arrived', 'tracking status', 'check tracking', 'where is my delivery'.
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
Teaches AI to design landing pages that feel like $150k agency work. Defines exact fonts, spacing, shadows, card structures, animations, and Korean typography standards that make Supanova-generated pages feel expensive and intentional. Blocks all common defaults that make AI designs look cheap or generic.
Safe bulk editing across multiple Hugo markdown posts: find/replace, frontmatter updates, content transforms with mandatory preview before apply. Use when user needs batch text replacement, bulk frontmatter field changes, heading/link/whitespace normalization, or regex-based content transforms across posts. Use for "batch edit", "find and replace across files", "add field to all posts", "bulk update tags". Do NOT use for single-file edits, structural refactoring, or content generation.