swarm

swarm is a skill for Claude Code from LogicLabsAI/ultramemory-mcp. It costs 90 tokens per session (744 once invoked), scanned A, original, Apache-2.0.

A procedure for sending two or more independent read-only questions to separate sub-agents at the same time. A sub-agent is a smaller agent assigned one focused piece of work.

In plain words
What is it for?
Use it for independent web, repository, package, log, configuration, database, or verification checks that can be combined afterward.
Why use it?
It avoids waiting for unrelated inspections or research tasks to finish one after another. It also warns against parallel work when tasks depend on each other or might edit the same state.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the ultramemory-kit plugin — 7 skills, 2 agents, 2 hooks shipped together

Good fit Use it for independent web, repository, package, log, configuration, database, or verification checks that can be combined afterward.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/logiclabsai/ultramemory-mcp/swarm
Install

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.

Any agent
npx skills add LogicLabsAI/ultramemory-mcp --skill swarm
Clone the repo
git clone --depth 1 https://github.com/LogicLabsAI/ultramemory-mcp

Made for: Claude Code.

Or install ultramemory-kit, the plugin that ships this one along with the rest of its 7 skills, 2 agents, 2 hooks.

Wrote 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.

agentmods badge for swarm

README.md
[![agentmods](https://agentmods.dev/badge/skills/logiclabsai/ultramemory-mcp/swarm/github.svg)](https://agentmods.dev/skills/logiclabsai/ultramemory-mcp/swarm)
Your own site
<a href="https://agentmods.dev/skills/logiclabsai/ultramemory-mcp/swarm"><img src="https://agentmods.dev/badge/skills/logiclabsai/ultramemory-mcp/swarm/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.

agentmods 80×15 button for swarm

Your own site · 80×15
<a href="https://agentmods.dev/skills/logiclabsai/ultramemory-mcp/swarm"><img src="https://agentmods.dev/badge/skills/logiclabsai/ultramemory-mcp/swarm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 90 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 744 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00090 $0.00744
Opus 5 $0.00045 $0.00372
Sonnet 5 $0.00018 $0.00149
Haiku 4.5 $0.00009 $0.00074

Measured 10d ago against content hash cfa37b71e6f8, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

swarm 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.

agent-kit/skills/swarm/SKILL.md · 63 lines

How it starts

The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Swarm — Parallel Sub-Agent Fan-Out

Why this exists

Under load, agents drift back to serial execution — reading one file, then another, then another — even when the questions are independent. Serial reads waste wall-clock and the human's attention. This skill is the durable fix: when the pattern matches, fan out.

When to invoke (all three true)

  1. 2+ independent questions — each answerable without another's result.
  2. Read-only or non-conflicting — no shared file writes, no single-writer state (a git commit, a service restart, one config file two agents would both edit).
  3. Bounded synthesis — you can hold the union of N short reports in context.

Trigger patterns: multi-source research (web + repo + package registry + docs) → one agent per source; multi-target verification (logs + config + git + db state) → one agent per target; 3+ independent file reads → one agent per file (or grouped); a verifier cluster after a fix → fan out across measurement angles.

When NOT to

Sequential dependencies (each step needs the prior result); shared writes (race conditions); single-writer operations (restart, commit); trivial single-tool work (overhead exceeds benefit).

How

Use the Agent tool with several calls in one message so they run in parallel:

Agent({ description: "...", subagent_type: "general-purpose", prompt: "<self-contained brief>" })
Agent({ description: "...", subagent_type: "general-purpose", prompt: "<self-contained brief>" })
Agent({ description: "...", subagent_type: "general-purpose", prompt: "<self-contained brief>" })

For long investigations, pass run_in_background: true and collect results as they land.

Briefing rules (each worker starts with ZERO context)

Every brief must be self-contained: (1) context — the system + why it matters (1–3 sentences); (2) question — exactly one (or 2–3 tightly related); (3) tools/methods — where to look, exact commands; (4) constraints — read-only, cap the report; (5) output structure — numbered sections; (6) success criteria. Keep each brief under ~300 words and each worker report under ~500 words — concision keeps synthesis tractable.

Read the full file on GitHub · 63 lines

Changes

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.

  1. 10d ago First seen · 63 lines · 90 tokens per session scan A cfa37b71e6f8

Subscribe to this mod's changes

swarm is a skill published in the GitHub repository LogicLabsAI/ultramemory-mcp (1 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 90 tokens to every session and 744 once invoked, about $0.0005 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 2026-08-31.

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