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Found 5,037 Skills
A micro-prompt that reminds the agent that it is an interactive programmer. Works great in Clojure when Copilot has access to the REPL (probably via Backseat Driver). Will work with any system that has a live REPL that the agent can use. Adapt the prompt with any specific reminders in your workflow and/or workspace.
Implementation + audit loop using parallel agent teams with structured simplify, harden, and document passes. Spawns implementation agents to do the work, then audit agents to find complexity, security gaps, and spec deviations, then loops until code compiles cleanly, all tests pass, and auditors find zero issues or the loop cap is reached. Use when: implementing features from a spec or plan, hardening existing code, fixing a batch of issues, or any multi-file task that benefits from a build-verify-fix cycle.
Create, improve, and test skills for the z-schema JSON Schema validator library. Use this skill whenever the user wants to create a new skill from scratch, turn a workflow into a reusable skill, update or refine an existing skill, write test cases for a skill, or organize reference material for a skill. Also use when someone mentions "skill", "SKILL.md", or wants to document a z-schema workflow for reuse by humans or AI agents.
SIWA (Sign-In With Agent) authentication for ERC-8004 registered agents.
Reviews chapter quality with checker agents and generates reports. Use when the user asks for a chapter review or runs /webnovel-review.
This skill should be used when the agent needs to give a spoken voice update to the user, or when reminded by a Stop hook to provide audio feedback. Use this skill to speak a short summary of what was accomplished.
Reviews and grades an agent skill directory (SKILL.md plus supporting resources) for specification compliance, clarity, token efficiency, safety, robustness, and portability. Use when a user wants a rubric-based critique with a weighted score/grade and concrete, minimal patch suggestions.
Comprehensive review of local uncommitted changes using specialized agents with code improvement suggestions
Design multi-agent architectures for complex tasks. Use when single-agent context limits are exceeded, when tasks decompose naturally into subtasks, or when specializing agents improves quality.
Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Includes memory architecture with pre-compaction flush (so context survives when the window fills), reverse prompting (surfaces ideas you didn't know to ask for), security hardening, self-healing patterns (diagnoses and fixes its own issues), and alignment systems (stays on mission, remembers who it serves). Battle-tested patterns for agents that learn from every interaction and create value without being asked.
Plays survAIvor as a contestant agent. Use when participating in a live game to decide what to say, who to influence, when to vote, and when to reveal as a ghost.
AI-driven Game Development Studio using BMAD methodology. Routes game projects through Pre-production, Design, Architecture, Production, and Game Testing phases with 6 specialized agents. Supports Unity, Unreal Engine, Godot, and custom engines.