Orchestrate multiple AI agents in parallel and evaluate their results.
npx skills add https://github.com/4lerman/text_evaluator --skill agenthub-4lerman
Please help me install this Agent Skill. Skill: agenthub Source: https://github.com/4lerman/text_evaluator/tree/main/.agents/skills/engineering-advanced-skills/agenthub Command: npx skills add https://github.com/4lerman/text_evaluator --skill agenthub-4lerman
The AgentHub Skill solves the problem of complex tasks that benefit from parallel, competitive approaches. It facilitates collaboration between multiple AI agents that work independently and are evaluated by a coordinator to determine the best outcome.
Use the agenthub skill to optimize the 'optimize_performance.py' script. Run /hub:init --task "Optimize script performance" --agents 3 --eval "pytest bench.py --json" --metric p50_ms --direction lower --template optimizer. Then run /hub:run to start the optimization process.
High-intent search queries and answers about installing and using this skill.
Parallel task execution uses a coordinator to spawn multiple AI agents that work independently on different strategies, evaluate their results, and merge the best outcome. This is ideal for complex engineering, research, and content generation scenarios.
You orchestrate parallel AI agents by initializing the task with a specific number of agents and an evaluation metric. Agents explore different optimization strategies simultaneously, and the coordinator selects and merges the best result based on the metric.
Yes, a git repository is required to run multi-agent task orchestration. The environment also requires Python with specified libraries to support parallel processing and competitive evaluation of the agents.
Competitive evaluation works by using metrics or a human judge to compare the independent results generated by parallel AI agents. The coordinator assesses these results against specified metrics to determine and merge the best solution.
You should use multi-agent collaboration for complex tasks that benefit from parallel, competitive approaches, such as testing multiple algorithms simultaneously. It allows independent agents to explore different strategies and evaluate results to find the optimal solution.
clonable-edenDelegate issues to an Orchestrator agent using git worktrees and WezTerm windows.
jeremy-newhousePromote a dev branch to main after CI health and divergence checks.
KuassCoordinate worker swarms for parallel task execution with Kimi K2.5 PARL patterns.
galangryandanaDispatch independent problems to dedicated agents for parallel execution.
