agent▌

Automate issue-to-Draft-PR pipeline by labeling issues for GitHub Copilot assignment. \n \n Label an issue with ai-copilot to trigger GitHub Actions, which auto-assigns Copilot via GraphQL and initiates code generation, branch creation, and Draft PR opening \n Requires GitHub Copilot Pro+, Business, or Enterprise; one-time setup deploys the workflow, registers a PAT secret, and creates the label \n Copilot PRs are treated as external contributions and require manual approval before CI runs; subs

Polish and refine agent prompt files against proven best practices. \n \n Requires a prompt file as input; will request one if not provided \n Preserves front matter, encoding, and markdown structure while improving clarity and organization \n Corrects spelling, grammar, and wording issues without altering the original intent \n Applies patterns from successful prompts to strengthen structure and effectiveness \n

Deterministic browser automation for AI agents with snapshot-based element references and multi-session support. \n \n Interact with web pages using stable element refs (@e1, @e2, etc.) generated from snapshots, enabling reliable automation across DOM changes \n Core commands cover navigation, form filling, clicking, waiting, screenshots, PDFs, and visual regression testing via baseline comparison \n Supports parallel isolated sessions, network-aware waits (networkidle), and selector-based targe

Provides advanced multi-agent orchestration expertise for managing complex coordination of agents across distributed systems. Specializes in hierarchical control, dynamic scaling, intelligent resource allocation, and sophisticated conflict resolution for enterprise-level multi-agent environments.

Build sophisticated game-playing agents that learn strategies, adapt to opponents, and master complex games through AI and reinforcement learning.

Security audit enforcement for AI agents. Protects your agent through automated security scans and continuous health verification.

agent-browser

$21

Browser automation and end-to-end testing using Vercel's agent-browser CLI. Uses ref-based element targeting for reliable, AI-friendly browser interaction.

Agents are autonomous subprocesses that handle complex, multi-step tasks independently. Understanding agent structure, triggering conditions, and system prompt design enables creating powerful autonomous capabilities.