ai-collaboration-standards
Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
296 skills found
Prevents AI hallucination and ensures evidence-based, verifiable outputs when analyzing code, reviewing technical documents, or providing recommendations.
Break down complex development requests into sequenced, actionable tasks for multi-agent delegation in Claude Code environments.
Enforces structured self-assessment checkpoints to validate approach, mitigate risks, and ensure quality before, during, and after task execution.
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
Migrate your codebase, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to the advanced Opus 4.5 model with automated configuration adjustments.
Keep your technical specifications, test suites, and source code perfectly synchronized during AI-assisted development.
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement in AI agents.
A project-specific template skill for maintaining architectural consistency, coding standards, and deployment workflows in AI-powered full-stack applications.
Interactive UI components for Claude Code and AI agents. Create confirmations, checklists, inputs, tables, and views to handle non-blocking interactions and monitoring.
Fetches expert perspectives from OpenAI Codex and Google Gemini for architecture, code reviews, and debugging, with transparent LLM synthesis.
Automate iOS development workflows using XcodeBuildMCP: build, run, test, inspect UI, and capture logs on local simulators.
Expert guidance for designing and implementing high-quality tool schemas and descriptions for Julia's agent systems, ensuring reliable tool execution and reducing model hallucinations.