455 个 · 按相关度排序
Rigorous benchmarking of multi-agent coordination systems: experiment design, statistical analysis, human evaluation protocols, and reproducible reporting. NOT FOR ML model evaluation (use llm-evaluation-harness), A/B testing for web products, survey design, or general data science.
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Drafting and iterating sections of The Federated Harbor whitepaper — the third paper in the Curiositech sequence after Anchor (local identity) and Bonded Commons (local coordination), extending coordination across machines and administrative domains. Use when authoring a section, rewriting a section after a redteam/whitehat round, or composing a new chapter. NOT for marketing copy, NOT for the other two papers, NOT for production threat response.
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Apply GPGP/TAEMS coordination as local-scheduler constraints under explicit task, view, commitment, and delivery assumptions. NOT for assuming global task visibility, reliable delivery, or modern execution authority.
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Foundational distributed computing algorithms for consensus, coordination, and fault tolerance
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Frame multi-agent designs through explicit environment, autonomy, information, coordination, commitment, incentive, and verification assumptions. NOT for endorsing multi-agent architecture without need or claiming runtime correctness from conceptual methods.
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Apply crisis decision-making research to agent routing, uncertainty triage, and coordination failure analysis in time-pressured systems. Use when diagnosing handoff failures, analytical paralysis, or expert judgment under incomplete information. NOT for routine coding, simple CRUD design, or static single-agent tasks with complete information.
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Design scheduling agents. Use for timezone-safe negotiation, focus protection, and privacy-safe free-busy handling. NOT for calendar UI, project planning, or general agent architecture.
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Design a normalized, machine-readable "work receipt" that answers what changed, why, by whom, and with what validation, for any AI-coding-agent task regardless of backend (Claude Code, Codex, Cursor, Aider, CI, or homegrown). Use when defining a receipt schema, normalizing heterogeneous agent logs into one evidence shape, gating PR merges on artifact-backed proof, or designing an OSS maintainer's fast-reject/fast-merge workflow. NOT for validating a DAG node's output against its own schema (use output-contract-enforcer), continuous runtime monitoring of a live coordination daemon (use runtime-verification-for-agents), or general Port Daddy claims/notes/salvage mechanics (use port-daddy-agent-skill).
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Agent-to-Agent (A2A) executor implementation patterns for task handling, execution management, and agent coordination. Use when building A2A executors, implementing task handlers, creating agent execution flows, or when user mentions A2A protocol, task execution, agent executors, task handlers, or agent coordination.
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Mechanism conflict detection and coordination for AI assistant systems. Automatically detects crontab conflicts, systemd service conflicts, script overlaps, and log path inconsistencies. Use for maintaining system harmony and preventing internal conflicts.
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Orchestrates multi-agent AI systems with task delegation, agent communication, shared memory, and workflow coordination. Use when users request "multi-agent system", "agent orchestration", "AI agents", "agent coordination", or "autonomous agents".
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为多个 AI 智能体分配负责范围和完成条件，用看板共享进度与阻碍。通过分支或独立工作目录分开编辑，并在测试、差异审查和风险检查后整合成果。
对GitHub议题和拉取请求进行分类，将计划执行的工作关联到Linear，整理公开进展、内部负责人、优先级及下一步行动。
指导安装、配置和扩展Hermes Agent，调整界面外观与模型设置，连接消息服务，并协助在多种界面中使用及协调多个独立智能体。
Coordinate agent work through the Paperclip API by checking assignments, updating task status, posting progress, delegating subtasks, and requesting approvals. It records deliverables and dependencies so reviewers and other agents can follow the work and continue it.
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Organize software releases on GitHub, from version suggestions and release notes to testing, builds, and deployment. Coordinate multiple packages or repositories, roll changes out in stages, and configure rollback to restore an earlier release when problems occur.
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Manage GitHub issues, synchronize project boards, and plan sprints, short development cycles. Coordinate multiple AI agents to break work into tasks, assign responsibilities, update progress, and produce reports on completion and blockers.
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Coordinate multiple AI agents for research, development, testing, and system analysis. It guides role assignment, communication structures, parallel and sequential tasks, shared memory, progress monitoring, and recovery, bringing separate contributions into reports or coordinated development workflows.
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Review GitHub pull requests, or proposed code changes, with specialized agents covering security, performance, architecture, and style. Generate actionable comments, apply quality thresholds, and coordinate automated review workflows.
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Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.
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Build advanced AgentDB workflows with database synchronization, multiple databases, and search that combines meaning with attribute filters. Covers similarity measures, diverse search results, and operational patterns for distributed AI applications.
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Measure and optimize claude-flow v3 startup, search, attention processing, and memory use. Compare benchmarks against performance targets, track changes over time, and identify regressions to guide further optimization.
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Coordinate multiple Orca workers by assigning tasks, exchanging threaded messages, and waiting for results or escalations. Use task dependency graphs and decision gates to supervise work that needs ongoing coordination across agents.
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Coordinate a team of AI agents by setting direction, assigning responsibilities, allocating resources, and tracking progress. Support hierarchical decisions, group consensus, and emergency control while recording shared state and preparing handovers to keep work moving.
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