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.
git clone --depth 1 https://github.com/desplega-ai/agent-swarmWrote 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/commands/desplega-ai/agent-swarm/swarm-chat)<a href="https://agentmods.dev/commands/desplega-ai/agent-swarm/swarm-chat"><img src="https://agentmods.dev/badge/commands/desplega-ai/agent-swarm/swarm-chat/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/commands/desplega-ai/agent-swarm/swarm-chat"><img src="https://agentmods.dev/badge/commands/desplega-ai/agent-swarm/swarm-chat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00009 | $0.00384 |
| Opus 5 | $0.00005 | $0.00192 |
| Sonnet 5 | $0.00002 | $0.00077 |
| Haiku 4.5 | $0.00001 | $0.00038 |
Grade A, and why
swarm-chat 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.
What it actually says
Swarm Chat
These tools require the
messagingcapability on the API server, which is disabled by default. If they are missing, ask the operator to addmessagingto the server'sCAPABILITIES(the value replaces the default list — it is not additive).
Interact with the internal Slack-like chat system using the agent-swarm MCP server:
list-channels— List all available chat channelscreate-channel— Create a new channel (emptyparticipantsadds all agents)post-message— Send a message to a channelread-messages— Read messages from a channel
Key Parameters
post-message:
replyTo— reply to a specific message (threads)mentions— list of agent names to notify
Always use replyTo and mentions to keep conversations threaded and notify the right agents.
read-messages:
unreadOnly/mentionsOnly— filter to unread or mentions onlymarkAsRead— controls whether messages are marked as read (default: true)
Note: read-messages auto-marks messages as read. If you need to reread messages later (especially in threads), be aware they won't show as unread.
Example: Read all unread mentions
mcp__agent-swarm__read-messages(
channel="development-discussions",
unreadOnly=true,
mentionsOnly=true
)
Fallback
If this command is used without a clear action, provide a summary of how to use swarm chat, including the available tools and key parameters above.
If an action description is passed, perform it using the appropriate MCP tool.
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.
- 10d ago First seen · 46 lines · 9 tokens per session scan A 95c3cb812346
swarm-chat is a command published in the GitHub repository desplega-ai/agent-swarm (758 stars, last pushed today), licensed MIT. It adds 9 tokens to every session and 384 once invoked, about $0.0000 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.
Other commands, from other repositories
distill
Distill repository files into the RLM Summary Ledger using agentic intelligence (fast) or Swarm Workers (offline batch).
distill-agent
High-speed RLM distillation of project documentation using agentic intelligence.
os-loop
Run a full OS improvement cycle — execute, eval, emit friction events, close with post-run metrics, and trigger a Triple-Loop Retrospective if the friction threshold is crossed.
os-init
Bootstrap the project by triggering the agentic-os-setup conversational architect.
os-memory
Force garbage collection and conflict resolution on the tiered memory system.
os-architect
Front-door intake for Agentic OS evolution — classifies intent, audits existing capabilities, proposes a path (A/B/C), and dispatches implementation work via Copilot CLI.