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-remote-runtimegit 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-remote-runtime)<a href="https://agentmods.dev/skills/vasilyu1983/ai-agents-public/ai-coding-agents-remote-runtime"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-remote-runtime/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-remote-runtime"><img src="https://agentmods.dev/badge/skills/vasilyu1983/ai-agents-public/ai-coding-agents-remote-runtime.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Output Handling · line 168 Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
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 | $0.00041 | $0.04904 |
| Opus 5 | $0.00020 | $0.02452 |
| Sonnet 5 | $0.00008 | $0.00981 |
| Haiku 4.5 | $0.00004 | $0.00490 |
Grade A, and why
ai-coding-agents-remote-runtime 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 9d 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 — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Coding Agents Remote Runtime
Use this skill to design or review coding-agent runtimes where execution happens remotely but the user still interacts through a local CLI or terminal UI.
This skill covers remote sessions, bridge transports, viewer-only clients, SSH-style local-UI remote-tool flows, and remote approval routing.
ASCII Flow
local client / terminal UI
|
v
bridge transport
WebSocket | SSE | HTTP POST | ACP stdio | SSH-like tunnel
|
v
remote runtime
agent loop + tools + sandbox + session store
|
+--> permission request -> bridge -> local owner decision -> remote execution
+--> stream events -> bridge -> local rendering
+--> reconnect -> sequence resume or replay from checkpoint
Quick Reference
| Question | Read | Outcome |
|---|---|---|
| What should the remote runtime model look like? | references/local-ui-remote-execution-model.md |
Local UI, remote agent loop, viewer modes, and mode boundaries |
| How should bridge transport and approval work? | references/bridge-transport-and-permission-bridging.md |
WebSocket control flow, permission bridging, reconnect, and message adaptation |
| Which transport should I use (WebSocket vs SSE vs HTTP POST vs ACP stdio)? | references/transport-selection.md |
Decision tree, criteria, hybrid patterns, and anti-patterns |
| How do I build reconnect with sequence-number resume? | references/recipe-reconnect-with-sequence.md |
Step-by-step resumable stream recipe with ring buffer and client reconnect loop |
| How does OpenAI Codex structure app-server and remote-control daemon lifecycle? | references/openai-codex-app-server-remote-control.md |
Daemon lifecycle, JSON control output, remote host bootstrap, long-running work |
| How does OpenAI Codex keep app-server protocol clients in sync? | references/openai-codex-app-server-protocol-codegen.md |
Schema-driven TypeScript/JSON artifacts, experimental filtering, fixture tests, and explicit update workflow |
How does codex mcp-server work and how does it differ from the app-server-daemon? |
references/openai-codex-as-mcp-server.md |
McpServer subcommand, codex-rs/mcp-server crate, approval bridging over MCP, contrast with HTTP daemon |
What ships with it
11 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 321 B
- data/sources.json 7.4 KB
- learnings.consolidated.md 607 B
- learnings.md 1.4 KB
- references/bridge-transport-and-permission-bridging.md 3.7 KB
- references/local-ui-remote-execution-model.md 5.2 KB
- references/openai-codex-app-server-protocol-codegen.md 3.1 KB
- references/openai-codex-app-server-remote-control.md 4.7 KB
- references/openai-codex-as-mcp-server.md 5.4 KB
- references/recipe-reconnect-with-sequence.md 8.3 KB
- references/transport-selection.md 7.9 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.
- 9d ago First seen · 249 lines · 41 tokens per session scan A 086af1e3c643
ai-coding-agents-remote-runtime is a skill published in the GitHub repository vasilyu1983/AI-Agents-public (87 stars, last pushed 7d ago), licensed MIT. It adds 41 tokens to every session and 4,904 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 2026-08-30.
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