multi-agent-shogun is a system that coordinates multiple AI coding command-line agents through a hierarchy of managers, strategists, and workers. Developers use it to split coding requests into parallel tasks and monitor their execution through tmux.
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 yohey-w/multi-agent-shogun --skill shogun-model-listgit clone --depth 1 https://github.com/yohey-w/multi-agent-shogunWrote 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/yohey-w/multi-agent-shogun/shogun-model-list)<a href="https://agentmods.dev/skills/yohey-w/multi-agent-shogun/shogun-model-list"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-model-list/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/yohey-w/multi-agent-shogun/shogun-model-list"><img src="https://agentmods.dev/badge/skills/yohey-w/multi-agent-shogun/shogun-model-list.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00073 | $0.01584 |
| Opus 5 | $0.00036 | $0.00792 |
| Sonnet 5 | $0.00015 | $0.00317 |
| Haiku 4.5 | $0.00007 | $0.00158 |
Grade A, and why
shogun-model-list 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/shogun-model-list — Model Capability Reference
Overview
Displays a complete reference table of all AI CLI tools, models, required subscriptions,
and maximum Bloom cognitive level per model. Use this before configuring capability_tiers
in config/settings.yaml.
When to Use
- "What models can I use with my subscription?"
- "Which model handles L5 tasks?"
- "Compare Claude vs Codex model tiers"
- "Show me all models" / "モデル一覧"
- Before running
/shogun-bloom-configto understand the landscape
Instructions
Output the reference tables below directly to the user. No tool calls required.
Bloom's Taxonomy — Quick Reference
| Level | Category | Task Examples |
|---|---|---|
| L1 | Remember | File copy, template apply, data format |
| L2 | Understand | Summarize, explain, translate |
| L3 | Apply | Implement known patterns, generate boilerplate |
| L4 | Analyze | Debug, code review, root cause analysis |
| L5 | Evaluate | Architecture review, design trade-off judgment |
| L6 | Create | Novel architecture, requirements design, strategy |
Claude Code (Anthropic)
Subscription Plans
| Plan | Monthly | Opus 4.6 | Sonnet 4.6 | Haiku 4.5 | Extended Thinking |
|---|---|---|---|---|---|
| Free | $0 | ✗ | ✓ | ✓ | ✗ |
| Pro | $20 | ✓ | ✓ | ✓ | ✓ |
| Max 5x | $100 | ✓ | ✓ | ✓ | ✓ |
| Max 20x | $200 | ✓ | ✓ | ✓ | ✓ |
Pro/Max 5x/Max 20x have the same model access. The difference is usage quota (5x/20x = multiplier of Pro).
Claude Models × Bloom Capability
| Model | Bloom Max | Best For | Notes |
|---|---|---|---|
claude-haiku-4-5-20251001 |
L3 | High-volume L1-L3 tasks, fast responses | $1/$5/M; SWE-bench 73.3% (4pp below Sonnet 4.5); extended thinking available |
claude-sonnet-4-6 |
L5 | Code review, analysis, orchestration | Best balance — $3/$15/M; SWE-bench 79.6%, 1M context |
claude-opus-4-6 |
L6 | Novel design, strategy, architecture | $5/$25/M; SWE-bench 80.8% (only 1.2pp above Sonnet 4.6); use for true L6 only |
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.
- 10d ago First seen · 127 lines · 73 tokens per session scan A 6dc3dad0d358
shogun-model-list is a skill published in the GitHub repository yohey-w/multi-agent-shogun (1,420 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 1,584 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
skill-agent-mapping
This skill should be used when looking up which agents own or consume specific skills, understanding skill-agent relationships, or routing tasks based on skill ownership.
exploration-strategy
This skill should be used when exploring codebases, finding patterns, searching for code, gathering context, or understanding code structure before planning or implementation.
team-decision
This skill should be used when deciding whether to use Agent Teams for parallel execution or sequential subagent orchestration, based on task analysis, independence criteria, and cost-benefit.
agentsview-usage
Search prior session history to recall how similar work was handled before. Use when you want to leverage a past approach, check prior experience on a topic, answer "how was this handled before", or cross-verify current handling against precedent from earlier sessions.
graphify-usage
This skill should be used when querying the graphify knowledge graph for structural codebase information, choosing between graph tools and grep, or interpreting graph query results.
prompt-refinement
This skill should be used when the user provides a vague request, asks to clarify requirements, structure a task, or refine a prompt for multi-agent orchestration.