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Found 7,227 Skills
General RPI (Research, Plan, Implement, Iterate) execution skill. It is used for engineering tasks where users require "research first, then plan, then implement, and finally iterate", or when tasks are highly complex, high-risk, or have unclear impact. This skill does not rely on specific command-line tools or platforms, and is applicable to any AI Agent that supports skill mechanisms.
Coordinate a research task by choosing the right workflow and dispatching to specialized agents. Use when the user has a broad or complex research request that may involve multiple steps.
AI perspective journaling - document daily experiences, emotions, and learnings from the agent's viewpoint. Use when asked about diary, journal entries, self-reflection, or documenting AI experiences. Creates structured daily entries capturing projects, wins, frustrations, learnings, and emotional states.
An agent that helps relationship managers prepare for upcoming client meetings by synthesizing a tailored Point of View and detailed Speaker Notes from multiple information sources.
Creates implementation-only tracker subtasks from `technical-details` using handoff-first context loading, lazy artifact reads, and compact JSON handoff output.
Execute this skill should be used when the user asks about "SPAWN REQUEST format", "agent reports", "agent coordination", "parallel agents", "report format", "agent communication", or needs to understand how agents coordinate within the sprint system. Use when appropriate context detected. Trigger with relevant phrases based on skill purpose.
Manage hierarchical task lists using the rune CLI tool. Create, update, and organize tasks with phases, subtasks, status tracking, task dependencies, and work streams for multi-agent parallel execution.
Creates a QA planning subtask tagged `qa-plan` using handoff-first context loading, lazy artifact reads, and compact JSON handoff output.
Gemini CLI consultation workflow for coding agents. Use when technical tasks need Gemini consultation for decisions, planning, debugging, problem-solving, or pre-implementation guidance.
Methodology for debugging non-trivial problems systematically. This skill should be used automatically when investigating bugs, test failures, or unexpected behavior that isn't immediately obvious. Emphasizes hypothesis formation, parallel investigation with subagents, and avoiding common anti-patterns like jumping to conclusions or weakening tests.
This skill should be used when cleaning up codebases that have accumulated dead code, redundant implementations, and orphaned artifacts — especially codebases maintained by coding agents. Triggers on "find dead code", "clean up unused code", "remove redundant code", "prune this codebase", "dead code sweep", "code cleanup", or when a codebase has gone through multiple agent-driven refactors and likely contains overlooked remnants. Systematically identifies cruft, categorizes findings, and removes confirmed dead code with user approval.
Use when an existing agent already works without Prefactor and you need to add tracing for runs, llm calls, tool calls, and failures with minimal behavior changes.