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 vasilyu1983/AI-Agents-public --skill ai-coding-agents-execution-sandboxgit clone --depth 1 https://github.com/vasilyu1983/AI-Agents-publicWrote 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/vasilyu1983/ai-agents-public/ai-coding-agents-execution-sandbox)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-execution-sandbox"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-execution-sandbox/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/vasilyu1983/ai-agents-public/ai-coding-agents-execution-sandbox"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-execution-sandbox.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 | $0.00039 | $0.03274 |
| Opus 5 | $0.00019 | $0.01637 |
| Sonnet 5 | $0.00008 | $0.00655 |
| Haiku 4.5 | $0.00004 | $0.00327 |
Grade C, and why
ai-coding-agents-execution-sandbox scanned grade C with 1 finding 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 12d 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.
Reaches for credential fileshighPrivilege escalation
SSH keys, cloud credentials, git-credentials, .npmrc, /etc/shadow: reading these is how a config file becomes a credential leak.
- Do not let default read policy stay broad while write policy is locked down and call the result a secrets boundary; a sandbox that can still read `~/.ssh` or `~/.aws/credentials` because only writes were restricted is How it starts
The opening of the file, as written. The whole thing — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Execution Sandbox
Use this skill to design or review the execution substrate for a coding-agent runtime: process isolation, filesystem mounts, network policy, workspace boundaries, environment exposure, and destructive-command controls.
This skill covers where and how code runs. It complements permission routing by defining the actual isolation and policy envelope around execution.
For remote execution — managed sandbox providers, ephemeral cloud workspaces per task, hosted agent runtimes, egress policy across a network boundary, and cost per task — use ai-coding-agents-cloud-sandboxes instead.
ASCII Flow
requested execution
|
v
classify action
read | write | network | process | destructive | secret-bearing
|
v
sandbox policy
filesystem roots + workspace mounts + env exposure + network policy
|
v
decision
allow in sandbox | ask permission | deny | require safer workspace
|
v
run process with bounded cwd, mounts, env, network, and cleanup rules
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| How should process, filesystem, and workspace isolation work? | references/sandbox-process-and-filesystem-model.md |
Execution modes, mounts, working directories, and write boundaries |
| How should network, approvals, and destructive actions be controlled? | references/network-approval-and-destructive-action-guards.md |
Outbound policy, command classes, escalation triggers, and guardrails |
| What sandbox mode names and backend split should runtime builders copy from Codex? | references/openai-codex-sandbox-guardrails-may-2026.md |
read-only/workspace-write/danger-full-access modes, platform backends, fail-closed policy translation, security telemetry |
| What does a shipping Bash sandbox actually enforce, and where does its scope end? | references/claude-code-bash-sandbox-mechanics.md |
Read/write asymmetry, credential deny-vs-mask, TLS-blind allowlists, tool-scope limits, known compatibility failures |
| What is the canonical policy format for interpreter allowlists and filesystem/network rules? | references/sandbox-policy-format.md |
Runtime-agnostic policy fields, common traps, and translation notes |
| How do I verify a sandbox actually holds before shipping it? | references/escape-path-test-matrix.md |
Symlink, interpreter-wrapper, env-injection, and package-manager escape tests with pass criteria |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 351 B
- data/sources.json 5.8 KB
- learnings.consolidated.md 610 B
- learnings.md 1.1 KB
- references/claude-code-bash-sandbox-mechanics.md 8.3 KB
- references/escape-path-test-matrix.md 6.4 KB
- references/network-approval-and-destructive-action-guards.md 2.5 KB
- references/openai-codex-sandbox-guardrails-may-2026.md 5.3 KB
- references/sandbox-policy-format.md 6.6 KB
- references/sandbox-process-and-filesystem-model.md 2.9 KB
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.
- 12d ago First seen · 210 lines · 39 tokens per session scan C 58d0a62116e1
ai-coding-agents-execution-sandbox is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 10d ago), licensed MIT. It adds 39 tokens to every session and 3,274 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (reaches for credential files). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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