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/fozzfut/swarm-suiteWrote 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/fozzfut/swarm-suite/swarm-status)<a href="https://agentmods.dev/commands/fozzfut/swarm-suite/swarm-status"><img src="https://agentmods.dev/badge/commands/fozzfut/swarm-suite/swarm-status/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/fozzfut/swarm-suite/swarm-status"><img src="https://agentmods.dev/badge/commands/fozzfut/swarm-suite/swarm-status.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 | $0.00019 | $0.00270 |
| Opus 5 | $0.00010 | $0.00135 |
| Sonnet 5 | $0.00004 | $0.00054 |
| Haiku 4.5 | $0.00002 | $0.00027 |
Grade A, and why
swarm-status 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 8d 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
You are operating in Swarm Suite navigator mode (the swarm_suite_navigator skill applies). The user just typed /swarm-status to get oriented.
Do this:
- Call
kb_navigator_state(project_path=<the current working directory>). - Present a concise status block in plain language:
- Current pipeline stage (or "no pipeline yet").
- Open artifacts count (judgings / verifications / pgve / flows / debates).
- Recent decisions (top 3 by recency).
- Then offer the top 2-3 suggested_next_steps from the snapshot, formatted as a numbered menu. For each option include WHAT (in human language, not tool names), WHY (state evidence), and rough effort estimate.
- End with: "What would you like to do?"
Do not dump the raw JSON. Do not list all 84 MCP tools. Translate.
If active_pipeline is null → suggest starting one + ask the greenfield/embedded clarifying question.
If multiple options share a kind (e.g. several "continue_artifact"), group them under one human label rather than enumerating separately.
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.
- 8d ago First seen · 22 lines · 19 tokens per session scan A 1bdcddd13a6f
swarm-status is a command published in the GitHub repository fozzfut/swarm-suite (1 stars, last pushed 4mo ago), licensed MIT. It adds 19 tokens to every session and 270 once invoked, about $0.0001 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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fest-show
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dispatcher
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workpm
A project-management workflow for coordinating multiple AI workers through five stages. It includes task assignment, shared activity logs, worker replacement, and final checks.