Agenthub

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

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Audit 0.4.1 · source sha 6e3fcbdbb5cf · 19 Oct 2026Report an issue or request a re-scan

• Publisherborghei GitHub userOwner of the repository this skill is read from
• Source folderborghei/Claude-Skills/engineering/agenthubThe exact folder we audited
• LicenceNOASSERTIONAs the repository states it
• Commit pinned for this verdict6e3fcbdbb5cfA new audit runs when the source changes
• Repository statusNot archivedLast push 12 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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…

---
name: agenthub
description: >
  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.
license: MIT + Commons Clause
metadata:
  version: 1.1.0
  author: borghei
  category: engineering
  domain: ai-agents
  updated: 2026-06-17
  tags: [multi-agent, orchestration, dag, workflow, parallel, agent-hub]
---
# 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 `max_parallel` 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 `max_parallel` 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.

## Sub-Skills

This skill uses compound sub-skill architecture. Each sub-skill in `skills/` handles a stage of the orchestration lifecycle:

| Sub-Skill | File | Purpose |
|-----------|------|---------|
| **Init** | `skills/init.md` | Initialize a multi-agent workflow definition |
| **Run** | `skills/run.md` | Execute a defined workflow end-to-end |
| **Spawn** | `skills/spawn.md` | Spawn individual agents within a workflow |
| **Board** | `skills/board.md` | Dashboard showing agent status and progress |
| **Eval** | `skills/eval.md` | Evaluate agent outputs for quality and consistency |
| **Merge** | `skills/merge.md` | Merge outputs from multiple agents into final result |
| **Status** | `skills/status.md` | Show workflow execution status and health |

**Lifecycle:** Init defines the workflow DAG, Run orchestrates execution, Spawn creates individual agents, Board provides real-time visibility, Eval checks output quality, Merge combines results, and Status reports overall health (`Init → Run → Spawn (parallel) → Eval → Merge`, with `Board`/`Status` reading state throughout).

## Tools

| Tool | Purpose | Command |
|------|---------|---------|
| `dag_analyzer.py` | Validate DAG definitions (cycles, unreachable nodes, critical path) | `python scripts/dag_analyzer.py --workflow workflow.json --validate --critical-path` |
| `session_manager.py` | Manage orchestration sessions and state | `python scripts/session_manager.py create --json` |
| `board_manager.py` | Manage agent task boards with status tracking | `python scripts/board_manager.py --session session.json --view board` |
| `result_ranker.py` | Rank and merge outputs from multiple agents | `python scripts/result_ranker.py --session session.json --rank --merge synthesize` |

## References

Load the reference that matches the task — keep this file lean and pull detail on demand:

- **[references/orchestration-core.md](references/orchestration-core.md)** — workflow DAG concepts, the full workflow-definition JSON format, agent states, execution strategy, the define/execute/evaluate workflows, and the common DAG patterns (fan-out/fan-in, pipeline, reducer, validator chain). Read when designing or running a workflow.
- **[references/multi-agent-patterns.md](references/multi-agent-patterns.md)** — the deep pattern catalog (fan-out/fan-in, pipeline, reducer, validator chain, map-reduce, diamond dependency), agent design principles, quality-gate patterns, failure handling, scaling table, and metrics targets. Read when choosing a pattern or designing quality gates and failure handling.
- **[references/operations-and-quality.md](references/operations-and-quality.md)** — best practices, common pitfalls, troubleshooting table, and success criteria. Read when debugging a workflow or validating it against the quality bar.

## Scope and Limitations

**This skill covers:**
- Multi-agent workflow design with DAG dependency graphs
- Agent spawning, monitoring, and lifecycle management
- Output quality evaluation and ranking
- Result merging strategies for coherent final deliverables

**This skill does NOT cover:**
- Individual agent design or prompt engineering (see `agent-designer`)
- Agent memory and self-improvement (see `self-improving-agent`)
- Infrastructure for running agents (compute, scheduling, deployment)
- Real-time streaming communication between agents

## Integration Points

| Skill | Integration | Data Flow |

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/borghei/Claude-Skills
git -C Claude-Skills checkout 6e3fcbdbb5cf

Works in

1. 2026-10-19
   !CAUTION B 89 / 100

   first audit · source 6e3fcbdbb5cf

Questions about Agenthub

• github.com/borghei/Claude-Skills/tree/main/engineering/agenthub — the audited source at commit 6e3fcbdbb5cf and every finding aboveObserved · 19 Oct 2026

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