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 monotykamary/pi-fabric --skill fabric-swarmgit clone --depth 1 https://github.com/monotykamary/pi-fabricWrote 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/monotykamary/pi-fabric/fabric-swarm)<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.
<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>- 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 | $0.00040 | $0.00651 |
| Opus 5 | $0.00020 | $0.00326 |
| Sonnet 5 | $0.00008 | $0.00130 |
| Haiku 4.5 | $0.00004 | $0.00065 |
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.
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.
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.
- 5d ago Changed · -40 lines f7caabc9b675
- 13d ago First seen · 81 lines · 40 tokens per session scan A f91a253ac0a4
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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