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Manage Twill Cloud Coding Agent workflows through the public v1 API. \n \n Create, list, retrieve, and manage tasks with support for custom branches, agent selection, and file attachments \n Stream job logs in real time via Server-Sent Events and cancel running jobs at any point \n Full lifecycle control: send follow-up messages, approve plans, archive tasks, and manage task state \n Schedule recurring coding tasks with cron expressions, timezone support, and pause/resume capabilities \n List re

Structured peer-review process using constrained agents to validate designs and surface hidden assumptions before implementation. \n \n Five specialized agent roles with hard scope limits: Primary Designer, Skeptic/Challenger, Constraint Guardian, User Advocate, and Integrator/Arbiter, each with explicit permissions and restrictions \n Sequential review workflow where the designer proposes, then three reviewer agents provide feedback in order, with mandatory Decision Log recording all objections

Decision framework for autonomous agent loops with quality gates, evals, and recovery patterns. \n \n Four loop modes available: continuous-pr for CI/PR workflows, rfc-dag for RFC decomposition, infinite for exploratory parallel generation, and sequential as default \n Recommended production stack combines RFC decomposition, code quality gates, eval harness, and session persistence for robust autonomous execution \n Built-in failure mode detection covers loop churn, repeated retries, merge queue

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Design patterns for building autonomous coding agents, inspired by Cline and OpenAI Codex.

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.