LoopX is a provider-neutral control plane that gives long-running AI-agent work persistent state, decisions, governance, recovery, and handoffs across different agent harnesses. It is for managing and reviewing durable work performed by Codex, Claude Code, Cursor, dsh, or custom agent systems.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/huangruiteng/loopxnpx agentmods add skills/huangruiteng/loopx/tb4-five-armWrote 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/huangruiteng/loopx/tb4-five-arm)<a href="https://agentmods.dev/skills/huangruiteng/loopx/tb4-five-arm"><img src="https://agentmods.dev/badge/skills/huangruiteng/loopx/tb4-five-arm/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/huangruiteng/loopx/tb4-five-arm"><img src="https://agentmods.dev/badge/skills/huangruiteng/loopx/tb4-five-arm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00100 | $0.02947 |
| Opus 5.5 | $0.00040 | $0.01179 |
| Sonnet 5.5 | $0.00020 | $0.00589 |
| Haiku 4.5 | $0.00010 | $0.00295 |
Grade A, and why
tb4-five-arm 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 1mo 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 — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Terminal-Bench 4.0 五臂对照
五臂定义、LoopX profile 装配、agent 类、监控工具全部与 [[swe-marathon-five-arm]] 相同,先读那一份。这里只写 TB4 特有的部分。
对照维度仍是 harness 不是模型:五臂的模型、effort、工具面、沙箱、容器一致。
切换方式
export WEN_BENCH=tb4 # 必须 export,见下面「最毒的一个坑」
source env.sh
./scripts/prebuild_images.sh music-harmony # 应建 2 个镜像,不是 1 个
./scripts/verify_envs.sh music-harmony # install-only,不烧 token
./scripts/canary_timeout.sh # 五臂 × 短死线
./scripts/marathon_all.sh --dry # 63 任务 × 5 臂 = 315 trial
benchmark 相关的量全在 scripts/bench/tb4.sh,驱动脚本里没有任何 TB4 常量。
WEN_BENCH 不设时默认 swe-marathon,行为与引入 bench 层之前逐字一致
(已用 bash -x 逐参数 diff 验证过)。
数据来源
terminal-bench/ 是 harbor-framework/terminal-bench 的 tag v4.0.0
(commit 452bf305c6),66 个任务,见 terminal-bench/VERSION。
- 不能用
harbor run -d [email protected]:harbor 的注册表(Supabasedataset表)里只有[email protected],实测查 4.0 返回null。 - 不要跟 main:上游 README 明说 "will be continuously updated", 跟 main 跑出来的结果不可复现。
tasks/dataset.toml里每个任务有 sha256 digest,task.toml与tests/一个字节都不能改。要注入环境变量走 harbor 的--ve/--ae。
TB4 vs SWE-Marathon:四处结构性差异
| 项 | SWE-Marathon | TB4.0 |
|---|---|---|
agent.timeout_sec |
3600–36000 各异 | 全部 28800(统一 8h) |
verifier.environment_mode |
无(shared) | 全部 separate |
network_mode |
三态,逐任务 | 一个都没声明 → harbor 默认 public |
| 每任务镜像数 | 1(environment/) |
2(environment/ + tests/) |
| 连续分 | metrics.json 有 |
没有,只有二值 reward |
排除 3 个要 H100 的任务(fp8-rmsnorm-gemm / jax-speedrun-gpu /
math-eval-grader),本机是 4090D。剩 63 个,其中 11 个是多容器
(environment/docker-compose.yaml),1 个(medical-claims-processing)
还要 MCP server(playwright,sse http://playwright-mcp:3080/sse)。
坑(都会静默通过)
一、最毒的一个:WEN_BENCH 用前缀赋值
WEN_BENCH=tb4 source env.sh # ✗ 错
export WEN_BENCH=tb4; source env.sh # ✓ 对
bash 在 source 返回后会把前缀赋值的变量还原成未设置,但 WEN_TASKS_DIR
是 export 的、留了下来。于是后续任何脚本自己 source env.sh 时:WEN_BENCH 空 →
回退 swe-marathon profile,却继承着 TB4 的任务目录 —— 拿 TB4 的任务、套
marathon 的网络策略(from-task 找不到 network_mode 就断网)、
开着判官注入、写进 marathon-full/。退出码 0,全程无警告。
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.
- 1mo ago First seen · 192 lines · 100 tokens per session scan A 1b76d5cebae3
tb4-five-arm is a skill published in the GitHub repository huangruiteng/loopx (6,233 stars, last pushed 5d ago), licensed Apache-2.0. It adds 100 tokens to every session and 2,947 once invoked, about $0.0004 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 2026-09-03.
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