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 skills add MLOps-Courses/agentops-open-course --skill agent-least-privilegegit clone --depth 1 https://github.com/MLOps-Courses/agentops-open-courseWrote 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/skills/mlops-courses/agentops-open-course/agent-least-privilege)<a href="https://agentmods.dev/skills/mlops-courses/agentops-open-course/agent-least-privilege"><img src="https://agentmods.dev/badge/skills/mlops-courses/agentops-open-course/agent-least-privilege/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/skills/mlops-courses/agentops-open-course/agent-least-privilege"><img src="https://agentmods.dev/badge/skills/mlops-courses/agentops-open-course/agent-least-privilege.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 | $0.00073 | $0.00551 |
| Opus 5.5 | $0.00029 | $0.00220 |
| Sonnet 5.5 | $0.00015 | $0.00110 |
| Haiku 4.5 | $0.00007 | $0.00055 |
Grade A, and why
agent-least-privilege 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 29d 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.
How it starts
The opening of the file, as written. The whole thing — 37 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agent Least Privilege
Split work by authority, not because a diagram looks tidier. A boundary a model could talk itself across is prose; a boundary enforced by which tools an agent physically holds is policy.
When to use
- One agent holds both readers of attacker-influenceable content and consequential write tools.
- You want an injected instruction to have nothing to call, by construction.
- You are unsure whether to split an agent or keep it single (see "When one agent is better").
Steps
- Separate reading from acting. Give the agent that reads untrusted content (logs, web, tool output) no write tools; give the agent that can act no raw-content readers. An injection reaching the reader then has nothing to invoke.
- Route by description, and keep policy in the coordinator. A coordinator delegates to specialists whose
descriptionfields the model matches the task against — write them precisely. Keep the policy (diagnose before remediate) in the coordinator's instruction, not in the model's discretion. - Keep writes gated even after the split. Least privilege limits capability, not content: the injected text still circulates in shared history. So the acting agent's writes still require human approval, and you still spotlight untrusted output.
- Share one budget. Delegation multiplies model calls; carry one shared token ceiling across all sub-agents so a chain that keeps hopping still hits a single limit.
When one agent is better
Most of the time. Every delegation is an extra model call and another candidate author for a wrong answer. Split only when tool sets must differ in authority, one instruction has grown contradictory, or a sub-task needs a different model/budget. Otherwise prefer one agent with well-guarded tools.
Reference implementation
From the AgentOps Open Course (agent modules live under agents/python/src/agent/):
delegation.py— a coordinator with read-only and write-only specialists.- Course chapter
3.7. Multi-Agent.
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.
- 29d ago Changed 56500cdb5441
- 1mo ago First seen · 37 lines · 73 tokens per session scan A cd5f3ab84846
agent-least-privilege is a skill published in the GitHub repository MLOps-Courses/agentops-open-course (2 stars, last pushed yesterday), licensed MIT. It adds 73 tokens to every session and 551 once invoked, about $0.0003 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 2026-08-31.
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