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/hoja-solutions/agent-stdlib/sandboxing-agentic-systemsnpx skills add Hoja-Solutions/agent-stdlib --skill sandboxing-agentic-systemsgit clone --depth 1 https://github.com/Hoja-Solutions/agent-stdlibWrote 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/hoja-solutions/agent-stdlib/sandboxing-agentic-systems)<a href="https://agentmods.dev/skills/hoja-solutions/agent-stdlib/sandboxing-agentic-systems"><img src="https://agentmods.dev/badge/skills/hoja-solutions/agent-stdlib/sandboxing-agentic-systems.svg" alt="Measured on agentmods" 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 | $0.00173 | $0.00845 |
| Opus 5 | $0.00086 | $0.00423 |
| Sonnet 5 | $0.00035 | $0.00169 |
| Haiku 4.5 | $0.00017 | $0.00085 |
Grade A, and why
sandboxing-agentic-systems 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 5d 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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sandboxing agentic systems
Source: How we contain Claude and Beyond permission prompts. Single-layer skills exist (a Seatbelt-profile generator, Docker configs). None package the end-to-end, threat-model-driven layering, which is where containment actually comes from.
Contain at the environment layer first, steer at the model layer second. A prompt or a classifier is probabilistic and will miss an edge case eventually. A filesystem mount and a firewall rule are deterministic: they hold on the case you did not think of. Build the deterministic boundary first and treat model-layer guidance as a second line, never the only one.
Isolate the filesystem
Scope reads and writes to the working directories the task needs. Block parent and system paths. Two details decide whether it holds:
- Use an OS primitive that also covers spawned subprocesses. Linux bubblewrap and macOS Seatbelt confine the process tree, so a shell command the agent runs is confined too. An application-level path check does not survive the agent shelling out.
- Validate paths before symlink resolution. A symlink inside an allowed directory can point at
/etc. Resolve and check the real target, not the link.
Offer mount modes that match the task: read-only, read-write, and read-write-without-delete for work that should add but never remove.
Isolate the network
Route every outbound connection through an egress proxy with allow and deny lists, and prompt on a new destination. The non-obvious part:
Check provenance, not just the destination domain. Allowlisting a domain opens every endpoint reachable through it, including ones that exfiltrate. Validate where the request came from and what it carries, not only where it is going.
Treat incoming content as hostile
Anything a network-enabled tool returns is a possible prompt-injection payload, including a fetched web page, an API response, or a file from a shared drive. Run tool results through a lightweight classifier before they enter the model's context. Defer parsing or executing project-local config, localhost listeners, and similar local-but-untrusted inputs until the user has explicitly consented.
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.
- 5d ago First seen · 54 lines · 173 tokens per session scan A d9fc2fcdc9e7
sandboxing-agentic-systems is a skill published in the GitHub repository Hoja-Solutions/agent-stdlib (1 stars, last pushed 1mo ago), licensed MIT. It adds 173 tokens to every session and 845 once invoked, about $0.0009 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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agile-product-owner
../../../product-team/agile-product-owner/skills/agile-product-owner/SKILL.md.
agent-memory
../../../engineering/agent-memory/skills/agent-memory/SKILL.md.