skills.rest
hetaoBackend
hetaoBackendCOMMUNITY
Explore all skills by this author →
GitHub

agentforge

Manage AI agent tasks via a local HTTP API and Python CLI.

8|Updated Mar 5, 2026
Search Tags:#automation#cli#task-scheduling#local-api#ai-task#agentforge#dag-pipelines
One-click install
npx skills add https://github.com/hetaoBackend/agentforge --skill agentforge
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agentforge
Source: https://github.com/hetaoBackend/agentforge/tree/main/skills/agentforge
Command: npx skills add https://github.com/hetaoBackend/agentforge --skill agentforge

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

AgentForge provides a local orchestration layer for AI coding tasks, enabling scheduling, monitoring, and DAG-driven workflows to manage automated AI work without relying on cloud infrastructure.

Core Features & Use Cases

  • Create tasks for AI agents to execute immediately, on delay, at a specific time, or on a cron schedule.
  • Query task status and view task lists to track progress across multiple projects.
  • View task execution history and streaming output for debugging and auditing.
  • Build DAG pipelines where tasks depend on upstream results and cascade failures or completions.
  • Cancel running or pending tasks to reclaim resources or stop faulty workflows.
  • Run and test locally via a REST API at http://127.0.0.1:9712 that manages scheduling and execution.

Quick Start

Install and run AgentForge locally, then use the provided CLI to create tasks, query status, and monitor execution.

Frequently Asked Questions about agentforge

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I schedule AI agent tasks locally without relying on cloud infrastructure?▼

Local AI task scheduling uses a local HTTP API at 127.0.0.1:9712 and a Python CLI to manage immediate, delayed, scheduled, and cron tasks without relying on cloud infrastructure.

Can I build DAG pipelines to manage AI task dependencies?▼

Yes, you can build DAG pipelines where AI tasks depend on upstream results, automatically cascading completions or failures to downstream tasks for structured automated workflows.

How do I monitor running AI tasks and view execution history for debugging?▼

You monitor running AI tasks by querying task status and lists via the local API, while execution history and streaming output provide detailed debugging and auditing capabilities.

What is the best way to stop faulty automated AI workflows and reclaim resources?▼

The best way to stop faulty automated AI workflows is to issue a cancellation command via the Python CLI to cancel running or pending tasks, effectively reclaiming local resources.

Does local AI task orchestration require external dependencies or cloud services?▼

Local AI task orchestration runs entirely via a local REST API and Python CLI with no external dependencies, meaning no cloud services are required to schedule and execute AI tasks.

Related Skills

Explore More Software Engineering Skills→
ilamanovilamanov

agent-bridge

Scan codebases to expose product actions as localhost API endpoints with a review/approval layer.

Community
Intermediate
miptah21miptah21
2

airflow

Manage Apache Airflow DAGs, runs, and task logs via the af CLI.

Community
Advanced
vinnie357vinnie357
24

dagu-rest-api

Manage and execute Dagu workflows via REST API endpoints.

Community
Intermediate
smith-hornsmith-horn
75

flow-nexus-swarm

Deploy and manage cloud-based AI agent swarms with Flow Nexus.

Official
Advanced