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-swarm-deployergit 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-swarm-deployer)<a href="https://agentmods.dev/skills/onewave-ai/claude-skills/agent-swarm-deployer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-swarm-deployer/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-swarm-deployer"><img src="https://agentmods.dev/badge/skills/onewave-ai/claude-skills/agent-swarm-deployer.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 | $0.00069 | $0.00930 |
| Opus 5 | $0.00034 | $0.00465 |
| Sonnet 5 | $0.00014 | $0.00186 |
| Haiku 4.5 | $0.00007 | $0.00093 |
Grade A, and why
agent-swarm-deployer 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 12d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Swarm Deployer
Deploy a swarm of parallel sub-agents to process massive, independent data tasks (documents, records, rows, items) and aggregate the results. Use this for data operations; use agent-army for code changes.
Contents
references/overview.md-- swarm vs army comparison, use cases, architecture diagramreferences/swarm-design.md-- input/output schemas, batch-size and swarm-size formulas, scaling guidelinesreferences/agent-brief.md-- agent brief template, data distribution methods, progress trackingreferences/aggregation-recovery.md-- merge logic, completeness validation, retry strategy, error-handling tablereferences/output-formats.md-- CSV/JSON/Markdown/individual-file outputs, final summary reportreferences/task-configs.md-- ready-made configs for sentiment, lead scoring, content generation, summarization
Workflow
-
Understand the task. Pin down five things before deploying anything: data source, operation per item, output format, output destination, and quality/validation requirements. If any is ambiguous, ask the user first -- a wrong spec wastes all agent compute.
-
Intake and inventory. Glob/Bash to locate and count items. Read 3-5 samples to learn structure. Estimate tokens per item and total. Report an intake summary (source, total count, item format, sample structure, token estimate).
-
Detect input schema and define output schema. Derive the input schema from samples; define the exact output schema the task requires. See
references/swarm-design.md. -
Design the swarm. Compute batch size from token budget (70% of ~200K usable context per agent) and swarm size from total items. Cap at 20 agents per wave; split into waves beyond that. Present the swarm plan and agent assignments, then get approval. See
references/swarm-design.md. -
Prepare agent briefs. Build a self-contained brief per agent: role, task, input data, output schema with example, quality rules, error protocol, and strict JSON output format. See
references/agent-brief.md.
What ships with it
6 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.
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.
- 12d ago First seen · 49 lines · 69 tokens per session scan A aa4a34392a1e
agent-swarm-deployer is a skill published in the GitHub repository OneWave-AI/claude-skills (291 stars, last pushed 1mo ago), licensed MIT. It adds 69 tokens to every session and 930 once invoked, about $0.0003 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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