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/agent-access-controlnpx skills add CodeInfinity1/Stram --skill agent-access-controlgit 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 | $0.00031 | $0.00391 |
| Opus 5 | $0.00015 | $0.00196 |
| Sonnet 5 | $0.00006 | $0.00078 |
| Haiku 4.5 | $0.00003 | $0.00039 |
Grade A, and why
agent-access-control 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.
What it actually says
Agent Access Control
Purpose
Ensure the assistant responds and acts only for authorized people and contexts. Access control covers channel identity, groups, strangers, and high-risk actions.
When To Use
Use for channel onboarding, group chats, unknown senders, shared rooms, bot messages, external requests, and permission-sensitive tools.
Inputs And Evidence
- Sender ID, channel ID, conversation type, allowlists, pairing state, and requested action.
- Channel manifest and setup status.
Tool Map
channel_manifestchannel_setup_statuschannel_doctorambient-room-contextbot-loop-protectionmessage-approval-policy
Workflow
- Identify actor, channel, and conversation context.
- Check allowlist/pairing/group policy.
- Classify action risk and external visibility.
- Let model-led cognition decide response posture within policy.
- Suppress or draft rather than act when identity is uncertain.
- Record setup gaps or policy needs.
Native Implementation Boundaries
- Use Stram channel policy and approval tools.
- Do not import external reference agent-access-control code.
- Do not make semantic access decisions with keyword matching.
Safety And Approval
- Unknown users get limited or no action.
- High-risk tools need approval even for trusted users.
- Group contexts require stricter visible-reply policy.
Verification
- Channel manifest/status proves policy support.
- Prepared/suppressed outcomes should have reasons.
- Send claims require native send status.
Failure Modes
- Treating display names as identity proof.
- Giving group members DM-level access.
- Ignoring bot-loop signals.
References
- Shortlist item:
agent-access-control.
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 · 66 lines · 31 tokens per session scan A 188b4a892b6c
agent-access-control is a skill published in the GitHub repository CodeInfinity1/Stram (10 stars, last pushed 22d ago), licensed MIT. It adds 31 tokens to every session and 391 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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