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#multi-agent
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Study a substantial Three.js FPS and the multi-agent coding workflow that produced it from a single orchestration prompt. Claude of Duty is an open-source first-person shooter that runs in the browser with Three.js and WebGL2. Its main value is not only the playable demo code, but also the unusually transparent engineering record: the repository includes the original orchestration prompt, an agent ownership contract, deterministic screenshot tooling, pixel-diff gates, profiling scripts, and an honest assessment of where the result still falls short. Why it is useful Inspect a large AI-generated codebase split across rendering, physics, weapons, audio, UI, world building, and enemy AI. Learn how directory ownership and explicit subsystem contracts can coordinate multiple coding agents. Reuse ideas for deterministic visual regression tests, performance profiling, and browser-game smoke tests. Compare ambitious output claims with the author's measured limitations and adversarial review scores. What to evaluate The game is visually ambitious but the author explicitly says it does not reach modern Call of Duty quality. Reported limitations include procedural-looking materials, mannequin-like enemies, imperfect hands, approximate indirect lighting, and demanding GPU performance. The project is MIT-licensed at repository level, while package.json currently declares ISC; verify the intended licensing treatment before redistributing modified builds.
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An open-source super-agent harness for research, coding, sub-agents, memory, sandboxes, and skills. What it is DeerFlow is ByteDance's open-source super-agent harness for longer jobs that need more than a single chat turn. It combines sub-agents, memory, sandboxes, skills, and a message gateway so an agent can research, code, create artifacts, and continue work across multi-step sessions. Who should use it Use it if you are evaluating agent infrastructure for deep research, coding workflows, report generation, or multi-agent task execution. It is most relevant for builders who already understand the cost and risk of letting agents use tools, files, shells, or browser/search providers. How to evaluate it Start with the official repository and installation guide. Run it locally or in Docker before exposing it to shared users. Use make setup and make doctor to generate config and catch setup problems. Test one contained workflow first: research summary, codebase inspection, or document generation. Keep sandbox mode and provider limits tight until you understand the execution path. Limits and risks DeerFlow is powerful because it can coordinate tools, models, files, and sub-agents. That also means misconfiguration can create security risk. Review the .env, model-provider config, shell/file-write permissions, sandbox settings, and any skill code before running it on sensitive projects. The repo also notes that DeerFlow 2.0 is a ground-up rewrite, so teams using older DeerFlow material should check whether guidance applies to the current branch. Source links Primary source: https://github.com/bytedance/deer-flow Discovery/context: https://github.com/topics/ai-agents
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A community OpenClaw skill for defining agent roles, task states, handoffs, and review gates before multi-agent work gets messy. Agent Team Orchestration is a community OpenClaw skill for teams that use more than one agent on the same stream of work. It gives the orchestrator a concrete operating model: define roles, move tasks through clear states, require handoff notes, and add review gates before agent-produced work ships. What it helps with Builder and reviewer agent loops for code, docs, research, or operations work. Clear task states such as inbox, assigned, in progress, review, done, or failed. Handoff messages that include what changed, where artifacts live, how to verify them, known gaps, and the next action. Quality checks when several agents are passing work across sessions. Who should evaluate it Use this as a candidate when an OpenClaw setup already has repeated multi-agent delegation and the weak point is coordination rather than raw model capability. It is most useful for long-running workflows, parallel research, build-review loops, and agent teams that need predictable artifact paths. Skip it for simple one-off delegation or a solo assistant. The process overhead only pays off when multiple agents are producing, reviewing, or routing work across more than one task. Setup surface The ClawHub page lists the install command as openclaw skills install @arminnaimi/agent-team-orchestration. Do not install it blindly on a production Pi. Review the skill file, reference files, permissions, and any tool assumptions first, then test it in an isolated OpenClaw workspace. Risk notes This is editorial discovery, not a runner-verified recommendation. Community skills can change after publication, and orchestration skills may influence how agents spawn work, communicate, and mark tasks complete. Treat the ClawHub and index pages as source material, then perform your own review before using it with sensitive repos, credentials, or external actions. Sources Awesome OpenClaw Skills: https://github.com/VoltAgent/awesome-openclaw-skills ClawHub listing: https://clawhub.ai/arminnaimi/skills/agent-team-orchestration Skill mirror: https://clawskills.sh/skills/arminnaimi-agent-team-orchestration
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A premium field guide for evaluating and planning a multi-agent orchestration layer for Claude Code and Codex without blindly installing it.
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