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/alphabravo-oss/guildWrote 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/alphabravo-oss/guild/stop)<a href="https://agentmods.dev/commands/alphabravo-oss/guild/stop"><img src="https://agentmods.dev/badge/commands/alphabravo-oss/guild/stop/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/alphabravo-oss/guild/stop"><img src="https://agentmods.dev/badge/commands/alphabravo-oss/guild/stop.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 | $0.00008 | $0.00391 |
| Opus 5.5 | $0.00003 | $0.00156 |
| Sonnet 5.5 | $0.00002 | $0.00078 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
stop 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 1mo 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
Foundry Stop Command
Gracefully stop the active foundry run.
STEP 1: CHECK FOR ACTIVE RUN
Call Foundry-Context to find the active run.
If no active run:
No active foundry run. Nothing to stop. Then STOP.
STEP 2: CONFIRM
Use AskUserQuestion:
"Stop foundry run '{run-name}' at phase {phase}, cycle {cycle}?"
- "Stop after current task" — Let running teammates finish, then stop
- "Stop immediately" — Kill all teammates now
- "Cancel" — Don't stop
STEP 3: EXECUTE STOP
Stop after current task:
- Send "All work complete, stop working." to each teammate in ONE parallel SendMessage batch (no broadcast — structured messages reject
to='*') TeamDelete+Foundry-Team-Downimmediately — do NOT wait for shutdown_response/ack or idle confirmations. Idle/terminated panes are the signal;TeamDeletecleans zombies.- Save state (run can be resumed later)
Stop immediately:
TeamDelete+Foundry-Team-Down(kills tmux panes)- Cancel all pending tasks
- Save state
Tell the user:
Foundry run '{name}' stopped at phase {phase}, cycle {cycle}. Resume with:
/foundry:resume
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.
- 1mo ago Changed · +95 lines · +6 tokens per session 6bc41ef3b648
- 1mo ago First seen · 42 lines · 8 tokens per session scan A 8c31125eebf9
stop is a command published in the GitHub repository alphabravo-oss/guild (2 stars, last pushed 11d ago), licensed MIT. It adds 8 tokens to every session and 391 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-09-04.
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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.