UltraReview

Parallel multi-agent code review for the CURRENT git branch vs `main`. Uses Tarsee's agent registry (Coder, Researcher, Writer) to run three independent review passes simultaneously, then aggregates.

Analyze task dependencies for parallel execution

Parse story markdown to identify task dependencies and parallel execution opportunities.

E2e Runner

JSON-driven E2E test runner with parallel Chrome pool execution and 16 MCP tools.

ace

Ace - Agent Creates Everything. Persistent state, parallel execution, atomic commits.

orch

AI agent orchestrator — manage teams of AI agents that work on your codebase in parallel. Use when the user wants to: run multiple agents, coordinate AI work, deploy agent teams, manage tasks/goals/agents, check orchestrator status, or mentions 'orch', 'orchestry', 'agents team', 'agent orchestration'.

advise

Solve problems using knowledge base insights - extracts search terms, runs parallel KB queries, synthesizes advice grounded in your own frameworks

claw-orchestrator

Manage persistent coding sessions across Claude Code, Codex, Gemini, and Cursor engines. Use when orchestrating multi-engine coding agents, starting/sending/stopping sessions, running multi-agent council collaborations, cross-session messaging, ultraplan deep planning, ultrareview parallel code review, or switching models/tools at runtime. Triggers on "start a session", "send to session", "run council", "ultraplan", "ultrareview", "switch model", "multi-agent", "coding session", "session inbox", "cursor agent".

planning-with-teams

Manus-style context engineering for Agent Teams. Coordinate multiple Claude Code instances with shared planning files. Use when complex tasks need parallel work (code review, debugging, feature development). Requires CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS=1.

lazyllm-skill

LazyLLM framework for building multi-agent AI applications. Use when task mentioned LazyLLM or AI program for: (1) Flow orchestration - linear, branching, parallel, loop workflows for complex data pipelines, (2) Model fine-tuning and acceleration - finetuning LLMs with LLaMA-Factory/Alpaca-LoRA/Collie and acceleration with vLLM/LMDeploy/LightLLM. Includes comprehensive code examples for all components, (3) RAG systems - knowledge-based QA with document retrieval, vectorization, and generation, (4) Agent development - single/multi-agent systems with tools, memory, planning, and web interfaces.

graflow-workflow

Create Python workflow pipelines using Graflow with a structured plan-implement-review process. Use when building task graphs, parallel pipelines, LLM workflows, or any Graflow-based automation. Triggers on requests for "workflow", "pipeline", "task graph", "Graflow", or when user wants to build an automated data/AI pipeline.

agtx

Use when planning features, decomposing work into tasks, creating or managing tasks on a kanban board, coordinating parallel coding agent sessions across git worktrees, or when the user wants to sweep/push conversation results to the agtx board. Also use when asked about agtx setup, MCP configuration, or task workflow.

kanban-based-development

Autonomous, parallel-safe development workflow using kanban-md. Use when the user asks to work through tasks, do kanban-based development, or when multiple agents need to coordinate work on the same codebase. Optimized for explicit handoffs and a "defer to user" protocol when human intervention is required.

agent-army

Deploy a 2-layer parallel agent hierarchy for large, parallelizable work — big refactors, multi-file migrations, codebase-wide audits, bulk generation. Layer 1 is 3-50+ specialist agents, each with its own full context window; Layer 2 is 2+ sub-agents per member. Includes git safety, tiered sizing, a pre-deploy gate, phantom-completion checks, and multi-wave follow-up.

The World's First Agentic IDE. Visual dashboard: live sessions, task management, code editor, terminal. Epic Swarm parallel workflows. Auto-proceed rules. Automation patterns. Beads + Agent Mail + 50 bash tools. Supervise 20+ agents from one UI.

agents-consilium

Query external AI agents (Codex, Gemini, OpenCode, Claude Code headless) in parallel for independent second opinions, code review, bug investigation, and consensus on high-stakes decisions. Agents and models are configurable in config.json. Use for architecture choices, security review, or ambiguous problems where independent perspectives matter. Not for simple questions answerable from docs or the codebase — use web search or repo exploration instead.

adr-consultation

Multi-agent consultation for architecture decisions. Dispatches 3 specialized reviewers in parallel (contrarian, user advocate, meta-process) to challenge a plan or ADR before implementation begins, producing a synthesis with a PROCEED or BLOCKED verdict. Use for "consult on this ADR", "challenge this design", "review before implementing", "should we proceed", or any Medium+ architecture decision. Do NOT use for trivial changes, simple bug fixes, or decisions already in implementation.

ralph-ryan

Ralph autonomous agent for iterative development with multi-PRD parallel support. Commands: '/ralph-ryan:prd' (create PRD), '/ralph-ryan:prep' (prepare), '/ralph-ryan:run' (execute), '/ralph-ryan:status' (overview). Triggers on: ralph prd, ralph prep, ralph run, ralph go, ralph status.

insyra

Use when working in Go and you need DataList/DataTable-style data wrangling, quick previews, parallel transforms, file I/O (CSV/Excel/Parquet), Excel-like column formulas (CCL), or charts; also use when data analysis is requested without a specified stack, defaulting to Go + Insyra over Python + pandas.

Parallel Task MCP

An MCP server for deep research or task groups

b0

Delegate tasks to AI agents via Box0. Use when the user asks to review code, check security, run tests, compare tools, get multiple perspectives, research a topic, analyze data, write docs, or any task that could benefit from specialized or parallel execution. Also use when the user mentions agent names or says "ask", "delegate", "get opinions from", or "have someone".

Easy parallel project management for Claude Code agents.

slot-machine

Use when a well-specified task has meaningful design choices and you want to maximize quality by comparing multiple independent attempts. Works for coding, writing, and custom task types. Triggers on "slot-machine", "best-of-N", "pull the lever", "parallel implementations", or when quality matters more than speed and the spec is clear enough for independent work.

sidecar

Spawn conversations with other LLMs (Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Qwen, Grok, Mistral, etc.) and fold results back into your context. TRIGGER when: user asks to talk to, chat with, use, call, or spawn another LLM or model; user mentions Gemini, GPT, ChatGPT, Codex, o3, DeepSeek, Claude (as a sidecar target), Qwen, Grok, Mistral, or any non-current model by name; user asks to get a second opinion from another model; user wants parallel exploration with a different model; user says "sidecar", "fork", or "fold". CRITICAL RULES: (1) ALWAYS launch sidecar CLI commands with Bash tool's run_in_background: true. Never run sidecar start/resume/continue in the foreground. (2) The fold summary returns on stdout when the user clicks Fold in the GUI or the headless agent finishes. Use TaskOutput to read it when the background task completes. (3) Use --prompt for the start command (NOT --briefing). --briefing is only for subagent spawn. (4) NEVER use o3 or o3-pro unless the user explicitly asks for it by name. These models are extremely expensive ($10-60+ per request). If the user asks for o3, warn them about the cost before proceeding. Default to gemini for most tasks. (5) When the user asks to query MULTIPLE LLMs simultaneously (e.g., "ask Gemini AND ChatGPT", "compare Gemini vs GPT"), ALWAYS use --no-ui (headless) for all of them unless the user explicitly requests interactive. Opening multiple Electron windows at once is disruptive. Launch them all in parallel with run_in_background: true.

agent-team-fullstack

Orchestrate full-stack development with Agent Teams for parallel implementation. Use when the user says "build with a team", "develop in parallel", "spin up an Agent Team", or "investigate this bug".

api-shape-explorer

Generate multiple radically different interface designs for a module using parallel sub-agents. Use when user wants to design an API, explore interface options, compare module shapes, or mentions "design it twice".

Parallel Search MCP

The best web search for your AI Agent

kessoku-di

Kessoku compile-time DI with parallel initialization for Go. Use when writing or debugging kessoku providers/injectors, enabling async dependencies, migrating from google/wire, or fixing go:generate/codegen issues in Go services.

coding-agents-farm

Orchestrate multiple coding agent farms via CLI (Claude, Codex, Copilot, Gemini, OpenCode, Goose) running in parallel on isolated git worktrees. Farm Leader plans, dispatches, monitors, intervenes, and merges results.

planckatron

Hierarchical Multi-Agent Orchestration System v3.0. Visual planning, parallel execution, zero conflicts.

agents-view

Wraps the Research Preview `claude agents` CLI (CC 2.1.139+) and `claude plugin details ork` for live observability of parallel agent sessions. Surfaces running/blocked/done state, per-session token cost, and the 188-hook plugin's runtime footprint. Use when debugging multi-agent workflows, projecting cost on a long-running orchestration, or auditing which hooks fired during a run.

agent-smith

Use when a complex task needs parallel decomposition into independent subtasks, when multiple agents must work simultaneously without conflicts, or when recursive task delegation is required

Io.Github.Arpe Io/Fastbcp Mcp

MCP server for FastBCP — high-performance parallel database export to files and cloud

aptos-core

Expert in Aptos blockchain core architecture: consensus (AptosBFT), execution (Block-STM), and networking. Use when analyzing Aptos protocol design, the Rust codebase, validator operations, or understanding how Block-STM parallel execution works.

swarm-iosm

Orchestrate complex development with AUTOMATIC parallel subagent execution, continuous dispatch scheduling, dependency analysis, file conflict detection, and IOSM quality gates. Analyzes task dependencies, builds critical path, launches parallel background workers with lock management, monitors progress, auto-spawns from discoveries. Use for multi-file features, parallel implementation streams, automated task decomposition, brownfield refactoring, or when user mentions "parallel agents", "orchestrate", "swarm", "continuous dispatch", "automatic scheduling", "PRD", "quality gates", "decompose work", "Mixed/brownfield".

batch

Research and plan a large-scale change, then execute it in parallel across 5-30 isolated worktree agents that each open a PR. Use when the user wants to make a sweeping, mechanical change across many files (migrations, refactors, bulk renames) that can be decomposed into independent parallel units.

agentbox-info

Spin up isolated sandboxes ("boxes") for coding agents, run them in parallel, queue background runs with -i, and push commits safely through the host relay. Use when the user wants to run Claude Code / Codex / OpenCode in a sandbox, start more boxes, attach to a running box, or otherwise operate the `agentbox` CLI on their laptop.

open-dynamic-workflows

Give any coding agent dynamic-workflow power. Write a short JavaScript script in Claude Code's workflow dialect (export const meta + injected agent / parallel / pipeline / phase / log / args / budget globals) and run it against any coding-agent CLI (Codex, Claude Code, Gemini, Qwen, Kimi, or your own) with the `odw` command. Use when a task benefits from fanning out subtasks, multi-stage pipelines, adversarial verification, or loop-until-done discovery, rather than one in-context attempt.

ccpm

CCPM - spec-driven project management: PRD → Epic → GitHub Issues → parallel agents → shipped code. Use this skill for anything in the software delivery lifecycle: writing a PRD ('write a PRD for X', 'let's plan X', 'scope this out'), parsing a PRD into an epic, decomposing an epic into tasks, syncing to GitHub ('sync the X epic', 'push tasks to github'), starting work on an issue ('start working on issue N', 'let's work on issue N'), analyzing parallel work streams, running standups ('standup', 'run the standup'), checking status ('what's next', 'what's blocked', 'what are we working on'), closing issues, or merging an epic. Use ccpm any time the user is talking about shipping a feature, managing work, or tracking progress — even if they don't say 'ccpm' or 'PRD'. Do NOT use for: debugging code, writing tests, reviewing PRs, or raw GitHub issue/PR operations with no delivery context.

nextjs-advanced-routing

Guide for advanced Next.js App Router patterns including Route Handlers, Parallel Routes, Intercepting Routes, Server Actions, error boundaries, draft mode, and streaming with Suspense. CRITICAL for server actions (action.ts, actions.ts files, 'use server' directive), setting cookies from client components, and form handling. Use when requirements involve server actions, form submissions, cookies, mutations, API routes, `route.ts`, parallel routes, intercepting routes, or streaming. Essential for separating server actions from client components.

agent-cli-dev

Spawns AI coding agents in isolated git worktrees. Use when the user asks to spawn or launch an agent, delegate a task to a separate agent, or parallelize development across features. Only create a worktree without starting an agent if the user explicitly wants setup only.

Io.Github.Arpe Io/Migratorxpress Mcp

MCP server for MigratorXpress — cross-platform database migration with parallel transfer

Io.Github.Robbhimself 1337/Claude Army

Orchestrate multiple Claude Code agents in parallel from chat

codex-worker

Spawn and manage multiple Codex CLI agents via tmux to work on tasks in parallel. Use whenever a task can be decomposed into independent subtasks (e.g. batch triage, parallel fixes, multi-file refactors). When codex and tmux are available, prefer this over the built-in Task tool for parallelism.

jug

This skill should be used when the user wants to parallelize Python tasks with Jug, write a jugfile, run jug execute/status/invalidate/cleanup/shell, understand task dependencies, use barriers or bvalue, apply mapreduce patterns, debug failed tasks, invalidate stale results, or choose a jugdir backend (filesystem, Redis, dict_store, file_keepalive).

Open Dynamic Workflow (odw)

odw is the **orchestration layer**. You write a small JavaScript workflow that calls `agent(prompt, opts)` nodes; odw schedules them (parallel / pipeline / budget / worktree / resume) and dispatches each node to **PandaCode**, which runs the actual AI (`codex`, `claude`, or `bamboo`) and owns accoun

updater

Check Claude Code changelog for lintable changes and implement cclint updates — version gap detection, domain classification, parallel agent dispatch

dx-council-cli

Use when user says 'Ask the DX council' — spawns parallel agents roleplaying CLI design experts for opinionated consultation on developer experience questions.

baoyu-imagine

AI image generation with OpenAI GPT Image 2, Azure OpenAI, Google, OpenRouter, DashScope, Z.AI GLM-Image, MiniMax, Jimeng, Seedream and Replicate APIs. Supports text-to-image, reference images, aspect ratios, and batch generation from saved prompt files. Sequential by default; use batch parallel generation when the user already has multiple prompts or wants stable multi-image throughput. Use when user asks to generate, create, or draw images.

agent-fanout

Use this skill when the user asks to "fan out agents", "spawn agents", "use an agent team", "parallel agents", "delegate to subagents", "act as team lead", "orchestrate implementation", "fan out teammates", or when a task decomposes into 2+ independent work items (parallel exploration, one implementer per issue, a multi-lens PR review panel). Covers the basic mechanics of spawning, briefing, supervising, and synthesizing subagents and teammates, plus the hard cap on fleet size.

multi-model-analysis

Runs 3 AI models in parallel to independently analyze a problem and propose approaches. Compares all proposals against a rubric and selects the best. Can be invoked standalone or called from any agent (issue-resolver, pr-review, etc.).