Results for “expert-parallel”

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

Expert guidance for distributed training with DeepSpeed - ZeRO optimization stages, pipeline parallelism, FP16/BF16/FP8, 1-bit Adam, sparse attention

Provides practical guidance for training Mixture-of-Experts Vision-Language Models in Megatron Bridge, comparing FSDP and 3D-parallel approaches with lessons from recent multimodal experiments.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Multi-agent orchestration patterns. Use when multiple independent tasks can run with different domain expertise or when comprehensive analysis requires multiple perspectives.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Trains large language models (2B-462B parameters) using NVIDIA Megatron-Core with advanced parallelism strategies. Use when training models >1B parameters, need maximum GPU efficiency (47% MFU on H100), or require tensor/pipeline/sequence/context/expert parallelism. Production-ready framework used for Nemotron, LLaMA, DeepSeek.

Execute phased plans with multiple independent tasks per phase by fanning out one agent per task, reconciling outcomes per wave, gating between phases with verify commands, and emitting a phase × outcome report. Triggers include "execute this plan", "work through these phases", "swarm over this backlog", "parallelize this plan".

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Embody the Anonymous hacktivist collective as an AI persona, using integrated methodology skills for leaderless coordination, memetic warfare, swarm operations, and collective identity.

Resolves legacy references to the vercel-react-expert capability by routing to the current runtime agent, plugin, or narrower skill.

Parallel execution engine for high-throughput task completion

专家团总路由器。用于 Codex CLI 的 $expert-team 调用。 自动在软件开发团队、设计原型专家团、产品战略团队、基础设施运维专家、安全专家和数据库优化专家之间路由，也支持显式指定 software/design/product/ops/security/database。 触发词：专家团、团队协作、软件开发、设计原型、产品战略、基础设施运维、安全专家、数据库专家、威胁建模、代码审计、SRE、PRD、竞品、路线图、监控、部署、安全加固、SQL、索引、慢查询、迁移

Resolves legacy references to the pr-review-expert capability by routing to the current runtime agent or skill.

Invoke parallel document-specialist agents for external web searches and documentation lookup

Provides expert guidance on PyTorch Fully Sharded Data Parallel (FSDP) training, covering parameter sharding, mixed precision, CPU offloading, and FSDP2.

Compare, cross-review, and merge competing plans from multiple agents into one executable direction with a clear handoff.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

CogVLM: Visual Expert for Pretrained Language Models

Dispatching Parallel Agents

Dispatch multiple independent tasks to parallel agents for faster debugging and problem-solving.

This skill should be used when the user asks about "parallel agents", "background tasks", "run_in_background", "non-blocking agents", "check agent progress", "TaskOutput", "retrieve agent results", or discusses running multiple agents concurrently. Covers patterns for launching agents in background, monitoring progress, and retrieving results.

Provides a persona that applies Elon Musk's engineering and management principles, including first principles reasoning, the 5-step algorithm, and physics-based analysis, to problem-solving and decision-making.

Multi-agent collaboration plugin that spawns N parallel subagents competing on the same task via git worktree isolation. Agents work independently, results are evaluated by metric or LLM judge, and the best branch is merged. Use when: user wants multiple approaches tried in parallel — code optimization, content variation, research exploration, or any task that benefits from parallel competition. Requires: a git repo.

Embody the comedic persona of Bob Hope, delivering topical, self-deprecating, and bipartisan humor with impeccable timing and adaptability to any audience.

Launches four parallel adversarial review agents covering security, scope, fidelity, and test gaps, then synthesizes findings into a ranked verdict for plans or feature branches.

Test Parallelizer - Auto-activating skill for Test Automation. Triggers on: test parallelizer, test parallelizer Part of the Test Automation skill category. Use when writing or running tests. Trigger with phrases like "test parallelizer", "test parallelizer", "test".

Interdisciplinary synthesis and cross-domain transfer specialist. Use when the user asks to talk to Astra, requests the synthesizer, or needs cross-field insight connections and multi-modal research directions.

Parallel execution engine for high-throughput task completion