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Choose LLM settings for theorem proving, code generation, and creative exploration. It provides examples for output limits and response variety, plus proof planning prompts and a method for generating multiple proof attempts and selecting the best candidate.

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• Choosing LLM settings for theorem proving
• Adjusting output settings for code generation
• Increasing response variety for creative exploration

Guide agents to index files as soon as they are created, making them searchable for subsequent tasks. The skill describes write-triggered indexing and single-file updates instead of waiting for session-end batches or scheduled jobs.

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• Making handoff documents searchable immediately
• Triggering indexing when a file is written
• Reviewing indexing delays in downstream workflows

Coordinate background agents through saved files, keeping lengthy work transcripts out of the main conversation. It defines how to pass results between agents, monitor progress, and verify completed work with tests.

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• Keeping long agent transcripts out of the main conversation
• Passing research and plans between agents through files
• Checking for stalled background agents

Implement one task from a larger development plan using test-driven development, where tests precede code. Record changes, decisions, test results, and blockers in a handoff document so the next agent can continue with the necessary context.

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• Implementing one task from a larger development plan
• Adding functionality by writing failing tests first
• Passing implementation details to the next agent

Carry out approved technical plans in phases, checking success criteria and recording progress as work proceeds. Larger plans can use separate agents with saved handoff notes, while manual verification keeps users involved at phase boundaries.

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• Implementing an approved technical plan
• Checking results at each implementation phase
• Resuming work from unfinished plan items

Carry out technical plans in verified stages, with automated checks followed by human review. The skill defines task delegation, progress tracking, and persistent handoff records so work can resume with the context needed for the next task.

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• Implementing a technical plan in verified stages
• Assigning separate agents to sequential tasks
• Resuming work from saved handoff records

Design fallback and repair paths to be idempotent, meaning repeated runs leave the same result. The skill outlines checks before writing, atomic file updates, and reconciliation practices to avoid overwrite conflicts and repeated repair loops.

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• Adding fallback paths that write only when values are missing
• Reviewing repair steps that may run repeatedly
• Preventing file overwrite conflicts between writers

Guide Claude Code hook development with shell wrappers and TypeScript handlers. It covers event timing, JSON input and output, hook registration, and manual testing, including handlers that can block tool execution.

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• Add a check before tool execution
• Process events when a session starts
• Standardize hook registration and manual testing

Provides a reference for building Claude Code hooks, scripts or model checks triggered by events. Covers input and output formats, registration, testing, and debugging for tasks such as blocking tool calls, adding context, and checking tests before stopping.

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• Create a hook that blocks edits to protected files
• Format files automatically after edits
• Add Git context when submitting a prompt

Discover the tools, workflows, specialist agents, and hooks available in a configured workspace. Interactive guidance and topic-specific commands help you choose relevant capabilities, inspect usage examples, and understand customization options.

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• Find workspace capabilities that match a task
• Browse development workflows and specialist agents
• Understand active hooks and their roles

Design software that keeps working with reduced features when an optional service is unavailable. The skill provides patterns for early availability checks, cached results, and messages explaining what is missing, which features are affected, and how to restore them.

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• Keeping available features running during an optional service outage
• Writing messages with clear service setup steps
• Caching availability checks to avoid repeated requests

Search GitHub code, repositories, issues, and pull requests through MCP, a connection to external tools. Use GitHub search syntax and owner or repository filters to find implementation examples, bug reports, and project information.

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• Find implementation examples across repositories
• Find critical bug reports
• Find related pull requests

Route requests to commit, push, or save Git changes through a dedicated /commit skill. The workflow removes Claude attribution, records attempted approaches in reasoning.md, clears build attempts, and pushes changes when requested.

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• Saving changes through the designated commit workflow
• Preserving attempted approaches alongside a commit
• Removing Claude attribution from commit messages

Coordinate investigation, fixes, tests, and commits for bugs, Claude Code hooks, dependency errors, and pull request feedback. Route work to specialized skills and agents, with human review checkpoints, diagnosis-only options, and a handoff record.

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• Investigate and fix a bug
• Diagnose a Claude Code hook that does not run
• Resolve package or import errors

Connect Agentica SDK agents to Claude Code CLI through a proxy, an intermediary server. Use setup and debugging guidance to address file permissions, streamed responses, result formatting, and agents reporting success without performing the requested operations.

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• Connecting Agentica agents to Claude Code tools
• Investigating file write and edit permission errors
• Debugging streaming and REPL response formats

Delegate implementation, file reading, and testing to separate agents, then bring concise summaries back to the main conversation. The rules explain when delegation helps and when a small edit is better handled directly.

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• Implementing a feature across multiple files
• Completing a specific phase of an implementation plan
• Delegating feature development and testing

Retrieve web page content from a URL or search query through Firecrawl MCP. Export it as Markdown, HTML, or plain text, with support for extracting structured data from pages.

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• Collecting content from a specific web page
• Searching the web and retrieving page content
• Extracting structured data from web pages

A playful, arcade-inspired interface for games — built on the VT323 pixel typeface, hard-edged 0px corners, chunky pill buttons that physically press into solid offset blocks

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Retro arcade-inspired design with pixel fonts, dotted borders, playful high-contrast colors, and 8-bit game aesthetics.

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Entrypoint for building, upgrading, and finishing Three.js browser games. Routes work across the sibling threejs-* skills for gameplay, graphics, UI, 3D/image/audio asset generation, debugging, and release. Use for any request to build, upgrade, polish, or ship a Three.js game as a whole, at any scope from a small arcade prototype to a premium release.

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Expert blueprint for sports games (FIFA, NBA 2K, Rocket League, Tony Hawk) covering physics-based ball interaction, team AI formations, contextual input, and match umpire/score authority. Broadcast framing routes to godot-camera-systems. Use when building soccer, basketball, hockey, racing sports, or arcade sports games. Keywords ball physics, magnus effect, formation AI, team tactics, contextual controls, steering behaviors.

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Expert patterns for racing mechanics, checkpoint tracking, and ghost recording/playback in Godot 4. Use when building racing games, speed-run platformers, or arcade trials.

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Expert blueprint for racing games including vehicle physics (VehicleBody3D, suspension, friction), checkpoint systems (prevent shortcuts), rubber-banding AI (keep races competitive), drifting mechanics (reduce friction, boost on exit), camera feel (FOV increase with speed, motion blur), and UI (speedometer, lap timer, minimap). Use for arcade racers, kart racing, or realistic sims. Trigger keywords: racing_game, vehicle_physics, checkpoint_system, rubber_banding, drifting_mechanics, camera_feel.

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Expert blueprint for sports games (FIFA, NBA 2K, Rocket League, Tony Hawk) covering physics-based ball interaction, team AI formations, contextual input, and match umpire/score authority. Broadcast framing routes to godot-camera-systems. Use when building soccer, basketball, hockey, racing sports, or arcade sports games. Keywords ball physics, magnus effect, formation AI, team tactics, contextual controls, steering behaviors.

中文简介正在准备中。当前显示原文说明。