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 agentmods add skills/codeinfinity1/stram/bot-loop-protectionnpx skills add CodeInfinity1/Stram --skill bot-loop-protectiongit clone --depth 1 https://github.com/CodeInfinity1/StramWhat 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 | $0.00045 | $0.00546 |
| Opus 5 | $0.00023 | $0.00273 |
| Sonnet 5 | $0.00009 | $0.00109 |
| Haiku 4.5 | $0.00005 | $0.00055 |
Grade A, and why
bot-loop-protection 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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bot Loop Protection
Purpose
Keep assistants from endlessly replying to each other. This skill adapts external reference bot-loop protection concepts to Stram's native channel metadata and policy gates.
When To Use
Use for Slack, Discord, Telegram, Teams, WebChat, SMS, or any channel where inbound messages may be bot-authored or automation-authored.
Inputs And Evidence
- Inbound metadata:
bot_authored, author type, sender ID, message ID, conversation ID, and channel ID. - Channel manifest loop-protection support.
- Recent messages and outbox entries.
- Group/DM policy.
Tool Map
channel_manifestchannel_catalogchannel_outboxactivity_ingestcognitive_interaction_reviewchannel_message_prepare
Workflow
- Inspect inbound metadata before considering message content.
- If the message is bot-authored and the channel does not allow bot-authored inbound handling, suppress response.
- For supported bot-authored handling, check pair/session policy and recent outbox to avoid ping-pong.
- Convert suppressed messages into passive context only when useful and safe.
- Let model-led cognition review ambiguous cases; do not build hardcoded phrase triggers.
- Record suppression reason for diagnostics.
Safety
- Never let two bot accounts repeatedly call each other's message tools.
- Do not bypass bot-loop protection because the text appears urgent.
- Do not import external reference bot-loop code directly.
Native Implementation Boundaries
- Use native channel metadata, outbox history, and cognitive review to distinguish bot-authored context from user intent.
- Use
activity_ingestfor safe passive context andchannel_message_prepareonly when policy allows a response. - Treat missing bot metadata from an adapter as a channel implementation gap; do not infer bot identity from vague text patterns.
Verification
- Suppressed events should show a reason such as bot-authored or pair suppression.
- Prepared replies should not target bot-authored messages unless policy explicitly allows it.
- Outbox should not show repeated alternating bot replies.
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.
- yesterday First seen · 69 lines · 45 tokens per session scan A 510c35e9b728
bot-loop-protection is a skill published in the GitHub repository CodeInfinity1/Stram (10 stars, last pushed 22d ago), licensed MIT. It adds 45 tokens to every session and 546 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 2026-08-31.
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