Agent skill · operations · borghei
agenthub
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.
Why this skill is useful
Adds executable scripts for validating DAG definitions and managing orchestration sessions, which are not commonly available in public documentation.
What it needs
Requires python installed locally. About 4k tokens when loaded. Last updated 2026-07-21. 462 stars on the source repository.
What this skill does
AgentHub - Multi-Agent DAG Orchestration AgentHub provides patterns and tools for orchestrating multiple AI agents as a directed acyclic graph (DAG). Instead of one agent doing everything sequentially, AgentHub lets you decompose complex tasks into sub-tasks, assign each to a specialized agent, define dependencies between them, and merge their outputs into a coherent result. The core insight: complex tasks decompose better than they scale. A 10-step sequential task run by one agent hits context limits and quality degradation. Five parallel agents with clear scopes and a merge step produce better results faster. Core Capabilities DAG workflow design — model tasks as nodes with explicit input/output contracts and dependency edges. Parallel execution — topological sort, parallel groups, and maxparallel scheduling for real speedup. Agent lifecycle — spawn, monitor (board), and track states from PENDING through COMPLETED/FAILED. Quality gates — evaluate outputs against thresholds and rank competing results. Output merging — synthesize, rank-select, or chain terminal outputs into a coherent deliverable. When to Use A task needs multiple specialized agents with distinct scopes. You want to parallelize AI work that would otherwise run sequentially. A single agent hits context limits or quality degradation on a long task. You need quality gates and merge strategies across agent outputs. Clarify First Before designing the workflow, confirm these inputs. If any is unknown or vague, ASK — do not assume: [ ] Task decomposition — how the work splits into agent sub-tasks and their dependencies (defines the DAG nodes and edges in Init) [ ] Parallelism budget — how many agents may run concurrently (sets maxparallel scheduling) [ ] Merge strategy — synthesize, rank-select, or chain (determines how the Merge stage combines outputs) Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills borghei/agenthub