773 skills · sorted by relevance
Review documents before signing or prepare a signing packet with checked recipients, fields, and signing order. Keep sending and signing separately authorized, and verify whether the document is drafted, sent, opened, or fully signed.
Find Ticketmaster events and compare seat options by price, quality, quantity, and budget. Get current ticket prices, group totals, and checkout links for specific seats, with purchase completion left to the user.
Coordinate a task from a verifiable goal through completion. Handle work directly or split independent tasks across separate agent sessions, track progress, verify results, and arrange pull request merging when included in the goal.
Manage Google Tasks through conversation: review lists, add tasks with notes and due dates, edit details, and mark work complete or reopen it. Organize tasks with subtasks and ordering, or review what is due.
Monitor a specific Vercel deployment in Codex and report when it becomes ready or fails. The skill identifies the exact deployment, checks its recorded state, and includes dashboard and preview links in the notification.
Organize GitHub issues using parent and child relationships and links showing which tasks must finish first. Use gh CLI to create, change, remove, and inspect relationships, including links across repositories.
Automate Joplin note management through its terminal application. Create and organize notes and notebooks, update to-dos, manage tags and attachments, and run synchronization or import and export workflows with structured results.
Design agents that operate desktop or browser software through screenshots, mouse actions, and keyboard input. Compare ways to locate interface elements, diagnose coordinate errors, handle changing screens, and evaluate task completion, operating speed, and safety boundaries.
Design the operating layer around a coding agent so it can read and edit code, run checks, and correct errors. Define completion criteria, permission boundaries, feedback, and recovery methods, and assess which tasks suit autonomous execution.
Break complex requests into research, calculation, coding, and writing stages. Clarify goals, dependencies, and completion criteria, then coordinate the next specialist skill while keeping the conversation history available across transitions.
Help implement Azure OpenAI features in C# and .NET applications using SDK examples. Cover authentication, chat, document-backed answers, image generation, and audio processing, including transcription and speech generation.
Compare prompt revisions on a fixed set of test cases to identify regressions, meaning behavior that gets worse after a change. Record model settings, case-level evidence, missing results, costs, and evaluation limits for a reviewable decision.
Create consistent records for evaluating AI agents, including task success, invalid tool calls, token use, and actual costs. Produce CSV tables, database definitions, and JSON validation schemas to support reproducible comparisons and checks for performance declines.
Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.
Review Git changes with the ocr command-line tool and a configured AI model. Get line-level findings grouped by severity for working changes, commits, or branch comparisons, with code fixes applied when explicitly requested.
Review AI-generated code for problems hidden behind successful runs. Check failure handling, resource cleanup, unusual inputs, nonexistent dependencies, and departures from project conventions to identify missing safeguards before merging or accepting the work as complete.
Organize complex work for a team of AI agents by splitting tasks, defining dependencies, and documenting ownership and completion criteria. Provides strategies for tracking progress, spotting blockers, and redistributing work when workloads become uneven.
Design vocational training around a real work task, from preparation and equipment checks to operation, verification, and handover. Build interactive practice with tools, ordered steps, consequences for mistakes, and checkpoints where learners decide whether to proceed, recheck, or stop.
Build and troubleshoot C#/.NET MCP servers that expose tools and information to AI agents. Guidance targets the official 2.x SDK and covers transport setup, authentication, interactive features, basic clients, and migration issues caused by changed defaults.
Turn feature requirements and technical plans into a structured GitHub project plan. Break work into linked issues, define acceptance criteria, dependencies, priorities, and estimates, and outline board settings and automated progress tracking.
Create a feature PRD, a product requirements document, from a parent Epic. Turns a feature idea into user stories, explicit requirements, acceptance criteria, and scope boundaries that engineering teams can use to prepare a technical specification.
Design an AI agent around its requirements, or audit an existing agent for architectural problems. Deliver justified decisions, verification plans, and a PDF report with a diagram, covering budgets, permissions, stopping rules, and failure handling.
Turn support feedback or chat logs into a structured requirements document. Use numbered questions to clarify problems, goals, users, constraints, and open questions, then guide human review before handing approved requirements to development.
Configure OMC notifications for Telegram, Discord, or Slack through guided questions. Choose events such as session completion or requests for input, set destinations and mentions where supported, and optionally send a test message to check delivery.