Agi Super Team Codex

Agi Super Team Codex from aaaaqwq/agi-super-team.

by @aaaaqwq 6 skills

Skills in this plugin

6
  1. C Suite Team · aaaaqwq bundle
    Run an outcome-oriented C-suite team in Codex with the parent acting as CEO, bounded specialist leaves, a Governor review gate, and honest manual or sequential fallbacks. Use when the user asks for an executive team, starter kit, CEO-led swarm, or cross-functional plan and delivery.
    1 repo stars
  2. Project Memory · aaaaqwq bundle
    Save, recall, list, and archive concise project decisions and handoffs in an explicit local Codex memory store without hooks or background capture. Use only when the user asks to remember, save context, resume prior work, record a durable decision, create a handoff, list stored project memory, or forget/archive a saved memory.
    1 repo stars
  3. Agi Super Team Sync · aaaaqwq bundle
    Preview, install, or update AGI Super Team in Codex. Use when the user asks to inject the Musk CEO into the global main agent, list or install a C-suite team, or sync bundled specialist agents.
    1 repo stars
  4. Context Engineering · aaaaqwq
    Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
    1 repo stars
  5. Iterative Retrieval · aaaaqwq
    Progressively refine codebase and evidence retrieval for agents using dispatch, evaluation, query refinement, and bounded iteration. Use when a task spans unfamiliar modules, initial context is incomplete or noisy, an agent reports missing context, or parallel workers need compact evidence packets.
    1 repo stars
  6. Native Agent Swarms · aaaaqwq bundle
    Coordinate small teams of specialized Codex agents with bounded parallelism, explicit ownership, native messaging, and evidence-based synthesis. Use when two or more independent investigations, reviews, or implementation streams can run concurrently without sharing writable files, or when the user asks for swarm, team, parallel agent, or multi-reviewer execution.
    1 repo stars