| name | orchestrating-swarms |
|---|---|
| description | Master multi-agent orchestration using Claude Code's TeammateTool and Task system. Use when coordinating multiple agents, running parallel code reviews, creating pipeline workflows with dependencies, building self-organizing task queues, or any task benefiting from divide-and-conquer patterns. |
Master multi-agent orchestration using Claude Code's TeammateTool and Task system.
| core_oam_dev_workloaddefinitions | |
| Creating cluster "kind" ... | |
| [32mâ[0m Ensuring node image (kindest/node:v1.21.1) đź | |
| [?7l | |
| â â Preparing nodes đŚ [?7h[?7l | |
| â â Preparing nodes đŚ [?7h[?7l | |
| â â ą Preparing nodes đŚ [?7h[?7l | |
| â ⥹ Preparing nodes đŚ [?7h[?7l | |
| â˘âĄą Preparing nodes đŚ [?7h[?7l |
| package main | |
| import ( | |
| "bytes" | |
| "encoding/json" | |
| "fmt" | |
| "log" | |
| "cuelang.org/go/cue" | |
| "cuelang.org/go/cue/load" |
| import numpy as np | |
| import cvxpy as cp | |
| import itertools | |
| # Problem data | |
| global_indices = list(range(4)) | |
| # 0 = TOKEN-0 | |
| # 1 = TOKEN-1 | |
| # 2 = TOKEN-2 |
Lecture 1: Introduction to Research — [📝Lecture Notebooks] [
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[
