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 OneWave-AI/claude-skills --skill agent-armygit clone --depth 1 https://github.com/OneWave-AI/claude-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/onewave-ai/claude-skills/agent-army)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/agent-army"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-army/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/onewave-ai/claude-skills/agent-army"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-army.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 | $0.00142 | $0.03835 |
| Opus 5 | $0.00071 | $0.01917 |
| Sonnet 5 | $0.00028 | $0.00767 |
| Haiku 4.5 | $0.00014 | $0.00383 |
Grade A, and why
agent-army 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.
How it starts
The opening of the file, as written. The whole thing — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Army
A 2-layer parallel execution framework. The Commander is the top-tier model — Fable by default, or Opus if that's the session model — and does the thinking: recon, composition, briefing, verification. It orchestrates swarms of subordinate models whose tiers the user picks at deploy time (see Power Level). Each Layer 1 agent has its own full context window (not a slice). Each spawns Layer 2 sub-agents under it. The result is many independent brains running at once — not one brain divided.
Commander (you — Fable or Opus, the session model)
|
|-- Layer 1: Team (3 to 50+, each = own 1M context, L1 tier from power level)
| |-- Agent A (1M) -- Sub-agent A1, A2, ... (L2 tier)
| |-- Agent B (1M) -- Sub-agent B1, B2, ... (L2 tier)
| |-- Agent C (1M) -- Sub-agent C1, C2, ... (L2 tier)
| |-- ... (no cap)
Swarm vs. army: A swarm splits one context window across sub-agents — one brain, divided. An army gives each Layer 1 member its own window. That difference is the whole point of this skill.
When to use
- Large refactors spanning many files
- Multi-file color / style / naming / API migrations
- Broad codebase audits (security, a11y, performance, dead code)
- Bulk content generation or transformation
- Any task with 6+ independent units of work that can run simultaneously
When NOT to use
- Fewer than 6 independent units of work — just do it directly
- Heavy sequential dependencies where each step needs the last one's output
- Single-file changes
- Tasks needing one coherent authorial voice across all output (parallel agents drift)
If the task doesn't clearly fit, say so and propose doing it inline instead of spinning up an army.
- EVERY Layer 1 agent MUST spawn 2+ sub-agents. No exceptions. If you're about to deploy an L1 agent with no sub-agents, STOP and restructure.
- NEVER silently shrink the army. Match the user's chosen tier. If you must deviate, say so out loud and why.
2b. NEVER silently downgrade models. Pass
model:explicitly on every Agent call at the user's chosen power level. Omitting it inherits the session model — that's a silent Max Power bill. - Sub-agent deployment instructions go INSIDE the Layer 1 brief. If they're missing, the sub-agents will never be created.
- Report as agents complete:
[Agent N/M complete] name: X files modified, Y flags. - Show the army plan and pass the Deployment Gate before deploying (Full Mode). Quick Mode composes the plan internally but still passes the Gate.
- After every wave: run the build AND the phantom-completion check (see Verify). A green report from an agent is a claim, not proof.
- "Keep going" / "don't stop" = continuous mode: launch a new agent the moment one completes. Don't wait, don't re-ask.
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 · 272 lines · 142 tokens per session scan A fa93512ef879
agent-army is a skill published in the GitHub repository OneWave-AI/claude-skills (288 stars, last pushed 1mo ago), licensed MIT. It adds 142 tokens to every session and 3,835 once invoked, about $0.0007 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.
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