fabric-swarm

fabric-swarm is a skill for Claude Code from monotykamary/pi-fabric. It costs 40 tokens per session (651 once invoked), scanned A, original, MIT.

A method for organizing persistent AI workers into a team that shares messages, task history, and safely claimed work. It uses version checks so two workers do not accidentally take the same task.

In plain words
What is it for?
Use it to create multi-agent work runs, assign dependent tasks, track progress, coordinate questions, and record completed results.
Why use it?
It provides coordination for long-running delegated work, including task dependencies, progress updates, leases, and blockers.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Good fit Use it to create multi-agent work runs, assign dependent tasks, track progress, coordinate questions, and record completed results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/monotykamary/pi-fabric/fabric-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 monotykamary/pi-fabric --skill fabric-swarm
Clone the repo
git clone --depth 1 https://github.com/monotykamary/pi-fabric

Made for: Claude Code.

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 fabric-swarm

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/monotykamary/pi-fabric/fabric-swarm"><img src="https://agentmods.dev/badge/skills/monotykamary/pi-fabric/fabric-swarm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 651 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00040 $0.00651
Opus 5 $0.00020 $0.00326
Sonnet 5 $0.00008 $0.00130
Haiku 4.5 $0.00004 $0.00065

Measured 5d ago against content hash f7caabc9b675, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

fabric-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 5d 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.

skillsets/python/fabric-swarm/SKILL.md · 41 lines

What it actually says

Fabric Swarm — Python

Build from persistent actors and durable mesh primitives, not an external swarm extension. Pass payloads.run, JSON payloads.tasks (id, title, detail, optional dependencies), and JSON payloads.roles (name, instructions). Choose a fresh run key; seed with ifVersion=0.

Actor instructions must require: verify dependencies are complete; claim only ready tasks with the observed version; stop after a failed claim; publish progress; update blocked/completed state using each successful operation's returned version; CAS-unblock dependents only when all dependencies complete; direct questions via mesh.publish(topic=..., to=...); respect path ownership; emit directives only for blockers/final results.

import json
run = π.run
topic = "team." + run
tasks = json.loads(π.tasks)
roles = json.loads(π.roles)
seeded = []
actors = []
dispatched = []
try:
    for task in tasks:
        value = dict(task)
        value.update({"dependencies": task.get("dependencies", []), "status": "blocked" if task.get("dependencies") else "ready", "owner": None, "progress": [], "result": None})
        await mesh.put(key="runs/" + run + "/tasks/" + task["id"], value=value, ifVersion=0)
        seeded.append(task["id"])
    for role in roles:
        actor = await agents.create(name=role["name"], runner="pi", instructions=role["instructions"], topics=[topic], responseMode="directive", delivery="mailbox", coalesce=False)
        actors.append({"id": actor["id"], "name": actor["name"]})
    for actor in actors:
        await agents.tell(id=actor["id"], message="Join " + topic + ". Inspect ready tasks under runs/" + run + "/tasks/ and atomically claim one matching your role.")
        dispatched.append(actor["id"])
    await mesh.publish(topic=topic, kind="run.started", data={"run": run, "actors": actors})
    return {"status": "success", "run": run, "topic": topic, "actors": actors, "taskPrefix": "runs/" + run + "/tasks/"}
except Exception as error:
    return {"status": "partial" if seeded or actors else "failed", "run": run, "topic": topic, "seeded": seeded, "actors": actors, "dispatched": dispatched, "error": str(error)}

Seeding, actor creation, and dispatch are sequential so failures retain exact completed identities. On partial setup, inspect state/mailboxes before retrying; never automatically replay successful creation or messages. Keep coordination pull-based at decision points, not continuous polling. Persistent actors receive tell/ask; agents.steer redirects running one-shot workers. Do not return transcripts or task bodies unnecessarily.

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. 5d ago Changed · -40 lines f7caabc9b675
  2. 13d ago First seen · 81 lines · 40 tokens per session scan A f91a253ac0a4

Subscribe to this mod's changes

fabric-swarm is a skill published in the GitHub repository monotykamary/pi-fabric (213 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 651 once invoked, about $0.0002 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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