Orca CLI
stablyai/orca
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation.
Install Agenthub
$ npx skills add borghei/Claude-Skills --skill agenthub -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install borghei/Claude-Skills agenthub --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/engineering/agenthub .claude/skills/agenthub && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .claude/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthubType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add borghei/Claude-Skills --skill agenthub -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install borghei/Claude-Skills agenthub --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/engineering/agenthub .agents/skills/agenthub && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .agents/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill agenthub -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install borghei/Claude-Skills agenthub --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/engineering/agenthub .cursor/skills/agenthub && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .cursor/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/borghei/Claude-Skills.git --path engineering/agenthub--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add borghei/Claude-Skills --skill agenthub -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install borghei/Claude-Skills agenthub --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/engineering/agenthub .gemini/skills/agenthub && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .gemini/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install borghei/Claude-Skills agenthubInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add borghei/Claude-Skills --skill agenthub -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/engineering/agenthub .github/skills/agenthub && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .github/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add borghei/Claude-Skills --skill agenthub -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install borghei/Claude-Skills agenthub --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/borghei/Claude-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/engineering/agenthub .opencode/skills/agenthub && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "agenthub" agent skill from https://github.com/borghei/Claude-Skills/tree/main/engineering/agenthub into .opencode/skills/agenthub/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "agenthub", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
agenthub- GitHub stars
- 874
- Token cost
- ~1.7k tokens
- SKILL.md length
- 732 words
- Files
- 15 (incl. scripts, references)
- Skills in repo
- 364
- Repo updated
- First seen
- Licence
- MIT
At a glance
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation.
- A task needs multiple specialized agents
- SKILL.md covers Core Capabilities, When to Use, Clarify First and Sub-Skills, plus 4 more sections
- Runs Python scripts from its folder; calls python
- Parallelize AI work
What it does
Agenthub is an agent skill from borghei/Claude-Skills. 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.
Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 17 other files, including scripts and reference files (for example `references/multi-agent-patterns.md`, `references/operations-and-quality.md` and `references/orchestration-core.md`).
It sits in Agent Workflows, covering Multi-agent orchestration. The repository describes itself as: 385 AI skills, 77 expert agents, and 900 stdlib Python tools for every team: engineering, PM, marketing, C-level, compliance, business ops, research, and a LinkedIn toolkit… The licence is MIT.
When your agent uses it
- A task needs multiple specialized agents
- Parallelize AI work
Example prompts
- “/agenthub”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit c9a1487. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Agenthub loads about 1.7k tokens when it runs, and up to ~5.1k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 732 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from borghei/Claude-Skills at commit c9a1487, republished under its MIT licence (© borghei). 732 words, ~1,692 tokens.
Download SKILL.mdSave it as
.claude/skills/agenthub/SKILL.md (or your agent's skills folder). This skill also uses 14 other files; get the full folder from GitHub.- 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
- metadata.author
- borghei
- metadata.category
- engineering
- metadata.domain
- ai-agents
- metadata.updated
- 2026-06-17
- metadata.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_parallelscheduling 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_parallelscheduling) - 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 |
Show full SKILL.md (265 more words)Show less
References
Load the reference that matches the task — keep this file lean and pull detail on demand:
- 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 — 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 — 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 |
|---|---|---|
agent-designer | Defines individual agent capabilities that become DAG nodes | Agent specs flow in; execution results flow back for agent tuning |
self-improving-agent | Each agent can use self-improvement patterns to get better | Session feedback from orchestration feeds into agent learning loops |
prompt-engineer-toolkit | Agent task prompts benefit from prompt engineering | Optimized prompts improve individual agent quality within the DAG |
context-engine | Manages what context each agent sees | Context retrieval provides relevant inputs to each spawned agent |
observability-designer | Monitors workflow execution and agent health | Agent state transitions and timing metrics feed into dashboards |
© borghei, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 14 other files (scripts, references) in engineering/agenthub of borghei/Claude-Skills.
- SKILL.md
- references/multi-agent-patterns.md
- references/operations-and-quality.md
- references/orchestration-core.md
- scripts/board_manager.py
- scripts/dag_analyzer.py
- scripts/result_ranker.py
- scripts/session_manager.py
- skills/board.md
- skills/eval.md
- skills/init.md
- skills/merge.md
- skills/run.md
- skills/spawn.md
- skills/status.md
Open the folder on GitHubat commit c9a1487
Compare with similar skills
Agenthub next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Agenthub this skillborghei/Claude-Skills | 874 | — | ~1.7k | Automated safety check: Pass | MIT | |
| Orca CLIstablyai/orca | 87k | 2 repos | ~593 | Automated safety check: Pass | MIT | |
| Paseo Advisor Second Opiniongetpaseo/paseo | 20k | 1 repos | ~756 | Automated safety check: Pass | Custom licence | |
| O2 Review Loopopenobserve/openobserve | 22k | — | ~3.7k | Automated safety check: Pass | AGPL-3.0 | |
| Paseo Committeegetpaseo/paseo | 20k | 1 repos | ~496 | Automated safety check: Pass | Custom licence | |
| Mission Control Agent APIbuilderz-labs/mission-control | 6.3k | — | ~2.1k | Automated safety check: Pass | MIT |
Similar skills
Paseo Advisor Second Opinion
getpaseo/paseo
Launches one separate agent through Paseo to give a second opinion on the current task, with a self-contained briefing and no permission to edit files.
O2 Review Loop
openobserve/openobserve
Splits a change into planner, coder and independent reviewer roles: you confirm a spec, a subagent implements it, and a separate reviewer checks each round's local WIP commit.
Paseo Committee
getpaseo/paseo
Forms a two-agent committee with contrasting profiles to analyze a stuck problem in parallel, reconcile their views and return a consensus plan without editing files.
Mission Control Agent API
builderz-labs/mission-control
Teaches an agent to use the Mission Control dashboard API: register, send heartbeats, fetch assigned tasks, report progress and disconnect, with API key auth.
Paseo Agent Handoff
getpaseo/paseo
Hands off the current task, including context, decisions and failed attempts, to a fresh agent through Paseo by writing a self-contained briefing prompt and launching that agent.
More from borghei/Claude-Skills
All 364 skills in this repoAgents In The Team
borghei/Claude-Skills
Run delivery when AI coding and ops agents take tickets. An agent skill from borghei/Claude-Skills.
AI Content Disclosure
borghei/Claude-Skills
Check AI-generated marketing content and reviews for required disclosures under the EU AI Act, FTC rules and platform AI-label policies.
AI Prototyping
borghei/Claude-Skills
Idea to AI-generated prototype to customer validation to engineering handoff.
Analytics Engineer
borghei/Claude-Skills
Analytics engineering across data modeling, dbt, transformation, and semantic layers.
Ansoff Matrix
borghei/Claude-Skills
Ansoff Matrix — 4-quadrant framework for growth options: market penetration, market/product development, and diversification.
Brainstorm Okrs
borghei/Claude-Skills
OKR brainstorming and validation using the Radical Focus framework — outcome objectives, measurable key results, counter-metrics.
Related
Categories
Questions about Agenthub
What does Agenthub do?
Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Agenthub is an agent skill from borghei/Claude-Skills. Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation.
When should I use Agenthub?
Agenthub fits situations like: A task needs multiple specialized agents; parallelize AI work.
How do I install Agenthub in Claude Code?
Run `npx skills add borghei/Claude-Skills --skill agenthub -a claude-code`. Or copy the skill folder (engineering/agenthub in borghei/Claude-Skills) into .claude/skills/agenthub in your project. Claude Code loads it when a task matches its description.
How do I install Agenthub in Codex?
Run `npx skills add borghei/Claude-Skills --skill agenthub -a codex`. Or copy the skill folder (engineering/agenthub in borghei/Claude-Skills) into .agents/skills/agenthub in your project. Codex loads it when a task matches its description.
Can I use Agenthub in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add borghei/Claude-Skills --skill agenthub -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agenthub, .gemini/skills/agenthub, .github/skills/agenthub and .opencode/skills/agenthub in your project.
What does Agenthub need to run?
Going by SKILL.md and its folder, Agenthub needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.
Does Agenthub access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Agenthub safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Agenthub use?
Agenthub is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Agenthub use?
About 1.7k tokens (SKILL.md is roughly 6.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 3.4k tokens, read only when the agent opens those files.
What are the alternatives to Agenthub?
Skills that share tags, products or a category with Agenthub: Orca CLI (stablyai/orca, 87k stars), Paseo Advisor Second Opinion (getpaseo/paseo, 20k stars), O2 Review Loop (openobserve/openobserve, 22k stars) and Paseo Committee (getpaseo/paseo, 20k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Agenthub?
borghei (a GitHub user) maintains it in borghei/Claude-Skills, which has 874 GitHub stars. The repository holds 364 skills in this directory. The repository was last updated on October 7, 2026.
Source: borghei/Claude-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.