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Found 36 Skills
OpenCode Multi-Agent Parallel Collaboration Configuration. Supports multiple agents working simultaneously to implement a pipeline development mode. Use when: (1) Need multiple agents to work in parallel (2) Need a master to schedule collaborative work among agents (3) Need to implement a standardized process of design → development → acceptance → testing (4) Need to configure OpenCode's multi-agent collaboration capability
This skill should be used when the user asks to "add a background job", "process async", "schedule a task", "retry failed jobs", "add email sending", "run this later", "add a cron job", "unique jobs", "batch process", or mentions Oban, Oban Pro, workflows, job queues, cascades, grafting, recorded values, job args, or troubleshooting job failures.
pg-boss expert for PostgreSQL-backed job queues with exactly-once delivery, perfect for applications already using Postgres (Supabase, Neon, etc.). Use when "pg-boss, postgres queue, postgresql job, supabase background job, neon job queue, postgres scheduling, database job queue, pg-boss, postgresql, job-queue, background-jobs, supabase, neon, exactly-once, scheduling" mentioned.
Guide for Convex actions, scheduling, cron jobs, and orchestration patterns. Use when implementing external API calls, background jobs, scheduled tasks, cron jobs, or multi-step workflows. Activates for action implementation, ctx.scheduler usage, crons.ts creation, or long-running workflow tasks.
IM scheduled reminder skill that supports one-time and recurring scheduled tasks. Wake up the Agent at the specified time via cron job, automatically detect the current IM channel, and ensure accurate message delivery.
Multi-agent orchestration workflow for deep research: Split a research objective into parallel sub-objectives, run sub-processes using Claude Code non-interactive mode (`claude -p`); prioritize installed skills for network access and data collection, followed by MCP tools; aggregate sub-results with scripts and refine them chapter by chapter, and finally deliver "finished report file path + summary of key conclusions/recommendations". Applicable scenarios: systematic web/data research, competitor/industry analysis, batch link/dataset shard retrieval, long-form writing and evidence integration, or scenarios where users mention "deep research/Deep Research/Wide Research/multi-agent parallel research/multi-process research".
Async job processing patterns for background tasks, Celery workflows, task scheduling, retry strategies, and distributed task execution. Use when implementing background job processing, task queues, or scheduled task systems.
Python DAG workflow orchestration using Apache Airflow for data pipelines, ETL processes, and scheduled task automation
Create and orchestrate multi-agent clusters to complete complex tasks. Use this skill when users need to break down complex tasks into multiple specialized agents for parallel or serial execution. Applicable scenarios: (1) Complex projects requiring multi-role collaboration (planning, research, coding, writing, design, analysis, review) (2) Need to execute multiple independent sub-tasks in parallel to improve efficiency (3) Need professional division of labor to optimize cost and quality. Keywords: multi-agent, agent cluster, task orchestration, parallel execution, agent team.
Use this for designing complex workflows, scheduled jobs, and task orchestration (Airflow, Prefect, Temporal, Cron, Celery).
Background jobs, long-running processes, and task management
自主调度技能 — 管理周期任务、去重、触发窗口与执行状态。