agent-teams
claudecodexOrchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
6 skills · 7 commands · 4 agents
Token-efficient code generation pipeline using hosted LLM (Cerebras) — requires CEREBRAS_API_KEY from https://cloud.cerebras.ai
Not in a marketplace yet, so it loads for one session with --plugin-dir.
Plugin speed-run in 2389-research/speed-run. Commands read from the repo's manifest — 1 month ago.
This plugin runs code on your machine — its hooks fire automatically on events like tool use and it starts local MCP servers. The exact commands are listed below; read them before installing.
any-percentRuns 2-5 parallel agents in isolated git worktrees, each implementing a distinct architectural approach via Cerebras hosted LLM, then judges all survivors to select the winner (~60% token savings vs Claude direct). Use when the best architecture is unclear and you want real competing implementations
judgeScores parallel implementations with a 5-criteria worksheet (fitness, justified complexity, readability, robustness, maintainability; 25 points max), hard gates on score=1 or Fitness delta>=2, and Speed-Run efficiency tiebreakers. Invoked automatically by speed-run:showdown and speed-run:any-percent
showdownRuns 2-5 parallel runners against a shared design doc in isolated git worktrees, each independently planning and generating code via Cerebras hosted LLM, then invokes speed-run:judge to score all implementations and pick the winner (~60% token savings vs Claude direct). Use when you have a finalized
speed-runToken-efficient code generation pipeline via Cerebras hosted LLM; requires CEREBRAS_API_KEY. Checks API key then routes to speed-run:turbo (direct codegen), speed-run:showdown (parallel competition), or speed-run:any-percent (parallel exploration). Use when the user says \"speed-run\", \"fast build\
turboGenerates code via Cerebras hosted LLM from a structured contract prompt, writes files directly to disk, then applies surgical Claude fixes for test failures (~60% token savings vs Claude direct). Use when you have a single task — algorithmic code, boilerplate, data transformations, or multi-file sc
speed-runruns: node ${CLAUDE_PLUGIN_ROOT}/mcp/dist/index.js
bash ${CLAUDE_PLUGIN_ROOT}/hooks/check-api-key.sh
Orchestrate multi-agent teams for parallel code review, hypothesis-driven debugging, and coordinated feature development using Claude Code's Agent Teams
6 skills · 7 commands · 4 agents
Multi-agent collaboration — spawn N parallel subagents that compete on code optimization, content drafts, research approaches, or any task that benefits from diverse solutions. 7 slash commands (/hub-init, /agenthub:spawn, /hub-status, /agenthub:eval, /agenthub:merge, /agenthub:board, /hub-run), agent templates, DAG-based orchestration, LLM judge mode, message board coordination.
8 skills · 1 agent · hooks
Comprehensive context engineering and harness engineering skills for production-grade AI agent systems: fundamentals, degradation patterns, compression, optimization, self-managed context (model-controlled live context editing), multi-agent coordination, long-horizon prompting (pseudo-formal task briefs for long-running and parallel agents), memory systems, tool design, filesystem context, hosted agents, evaluation, autonomous harnesses, self-improvement loops (RSI, meta-harnesses, evolutionary scaffold search), latent briefing (KV cache sharing between agents), project development, and cognitive architecture. Ships with a researcher operating system (rubrics, mechanism registry, claim provenance, run state machine, adversarial benchmarks, continuous loop).
19 skills
Worktrunk is a CLI for Git worktree management, designed for parallel AI agent workflows. This plugin provides configuration guidance (LLM commit messages, project hooks, worktree paths), automatic activity tracking (🤖/💬 indicators in `wt list` showing active Claude sessions), and a `/wt-diff` pane showing each worktree's changes.
2 skills · hooks
Write, run, and debug Ginkgo/Gomega test suites in Go: the tree-construction mental model, the full node and decorator surface, tables and generated specs, parallelism, filtering, timeouts and async, CI configuration, and JSON-first failure diagnosis.
13 skills
Describe your API. Get a production CLI. Generates Go CLI tools from OpenAPI specs or natural language descriptions.
20 skills
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