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Run remaining GSD project phases through discussion, planning, and execution, while recording progress and phase documents. You can limit the phase range or keep decisions interactive; after all phases finish, the workflow audits and closes the milestone.
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Define a consistent process for unattended AI research using public sources. Limit clarification questions, show a search plan, distinguish facts from interpretations and assumptions, and keep reports structured so repeated investigations can be compared.
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Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.
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Create and manage prediction markets on Solana, where participants trade YES or NO outcome tokens. Support includes direct peer bets, social media metrics, custom result authorities, market settlement, and redemption of winning positions.
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Advance one development task by managing an existing pull request, fixing a build failure or bug, or implementing a queued idea. Record the work and integrate changes into auto-dev only after automated checks pass.
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Keep implementation work moving by turning routine uncertainty into explicit assumptions. The agent follows existing project patterns, makes small reversible changes, validates results, and reports its decisions, completed changes, checks, and remaining risks.
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Draft clear instructions for an agent to keep working toward a measurable goal. It defines the objective, constraints, validation commands, and stopping conditions, and guides setup, monitoring, and troubleshooting for the agent's supported runtime.
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Manage long or multi-part agent tasks by defining observable completion criteria before work begins. Break work into deliverables, run approved checks, and verify recorded evidence again before reporting which requirements are met or still need attention.
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Run all remaining phases autonomously — discuss→plan→execute per phase
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Autonomously engage with complex coding tasks — multi-step planning, tool use, self-correction, and solution verification without human intervention.
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Autonomous research engineering — web search orchestration, source synthesis, hypothesis formation, and structured research report generation.
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Use when the user says run the rest autonomously, overnight, or hands-off through the remaining phases. Runs discuss, plan, execute per phase, pausing only for user decisions, then milestone audit, complete, and cleanup.
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Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.
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Autonomous orchestrator for manifest work items through the development lifecycle. Use when running egregore to process a backlog automatically.
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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.
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Design patterns for building autonomous coding agents, inspired by [Cline](https://github.com/cline/cline) and [OpenAI Codex](https://github.com/openai/codex).
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Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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Manage persistent coding sessions across Claude Code, Codex, Antigravity (agy), Cursor, and OpenCode engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, autoloop autonomous workspace iteration, ultraapp building deployable web apps from a structured Q&A interview, switching models/tools at runtime, exposing the orchestrator's 69 tools as an MCP server to Hermes Agent / Claude Desktop / Cursor / Cline / Continue / Zed / Windsurf / Goose, or running as an Agent Client Protocol (ACP) agent that Zed / JetBrains / Neovim / Emacs / VS Code / dsh can drive directly. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "autoloop", "ultraapp", "Forge tab", "build a web app", "one-click app", "AppSpec", "autonomous iteration", "iterate until goal", "deep paper review", "auto research", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent", "opencode", "mcp server", "clawo-mcp", "hermes mcp", "model context protocol", "ultracode", "dynamic workflow", "fanout", "fan-out", "best-of-N", "steer turn", "interrupt turn", "fork thread", "rollback turns", "acp", "agent client protocol", "clawo acp", "zed agent", "jetbrains agent", "external agent", "dsh subagent", "deepseek harness".
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Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.
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Let AKA autonomously add, run, inspect, and revise intra-kernel timeline probes for standalone CUDA/inline PTX or CuTe DSL when ordinary benchmark, NSYS, or NCU evidence cannot answer a specific kernel-internal timing question.
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You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.
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AI Agents Architect workflow skill. Use this skill when the user needs Expert in designing and building autonomous AI agents. Masters tool and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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AI Agent Development Workflow workflow skill. Use this skill when the user needs AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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