Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add bestagentkits/agency-skills --skill agenthubgit clone --depth 1 https://github.com/bestagentkits/agency-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/bestagentkits/agency-skills/agenthub)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/agenthub"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/agenthub/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/agenthub"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/agenthub.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00081 | $0.01936 |
| Opus 5 | $0.00041 | $0.00968 |
| Sonnet 5 | $0.00016 | $0.00387 |
| Haiku 4.5 | $0.00008 | $0.00194 |
Grade A, and why
agenthub scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to agent-hub — 55 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentHub — Multi-Agent Collaboration
Spawn N parallel AI agents that compete on the same task. Each agent works in an isolated git worktree. The coordinator evaluates results and merges the winner.
Slash Commands
| Command | Description |
|---|---|
/hub:init |
Create a new collaboration session — task, agent count, eval criteria |
/hub:spawn |
Launch N parallel subagents in isolated worktrees |
/hub:status |
Show DAG state, agent progress, branch status |
/hub:eval |
Rank agent results by metric or LLM judge |
/hub:merge |
Merge winning branch, archive losers |
/hub:board |
Read/write the agent message board |
/hub:run |
One-shot lifecycle: init → baseline → spawn → eval → merge |
Agent Templates
When spawning with --template, agents follow a predefined iteration pattern:
| Template | Pattern | Use Case |
|---|---|---|
optimizer |
Edit → eval → keep/discard → repeat x10 | Performance, latency, size |
refactorer |
Restructure → test → iterate until green | Code quality, tech debt |
test-writer |
Write tests → measure coverage → repeat | Test coverage gaps |
bug-fixer |
Reproduce → diagnose → fix → verify | Bug fix approaches |
Templates are defined in references/agent-templates.md.
When This Skill Activates
Trigger phrases:
- "try multiple approaches"
- "have agents compete"
- "parallel optimization"
- "spawn N agents"
- "compare different solutions"
- "fan-out" or "tournament"
- "generate content variations"
- "compare different drafts"
- "A/B test copy"
- "explore multiple strategies"
Coordinator Protocol
The main Claude Code session is the coordinator. It follows this lifecycle:
INIT → DISPATCH → MONITOR → EVALUATE → MERGE
1. Init
Run /hub:init to create a session. This generates:
.agenthub/sessions/{session-id}/config.yaml— task config.agenthub/sessions/{session-id}/state.json— state machine.agenthub/board/— message board channels
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 182 B
- references/agent-templates.md 7.6 KB
- references/coordination-strategies.md 5.8 KB
- references/dag-patterns.md 4.0 KB
- scripts/board_manager.py 8.4 KB runs code
- scripts/dag_analyzer.py 8.7 KB runs code
- scripts/dry_run.py 11 KB runs code
- scripts/hub_init.py 8.3 KB runs code
- scripts/result_ranker.py 10 KB runs code
- scripts/session_manager.py 9.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 258 lines · 81 tokens per session scan A 6ba7f22e2649
agenthub is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 1,936 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to agent-hub, differing in 55 lines, and is treated as a copy.
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