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 volcengine/ark-cli --skill arkcli-train-finetunegit clone --depth 1 https://github.com/volcengine/ark-cliWrote 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/volcengine/ark-cli/arkcli-train-finetune)<a href="https://agentmods.dev/skills/volcengine/ark-cli/arkcli-train-finetune"><img src="https://agentmods.dev/badge/skills/volcengine/ark-cli/arkcli-train-finetune/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/volcengine/ark-cli/arkcli-train-finetune"><img src="https://agentmods.dev/badge/skills/volcengine/ark-cli/arkcli-train-finetune.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 | $0.00151 | $0.02735 |
| Opus 5.5 | $0.00060 | $0.01094 |
| Sonnet 5.5 | $0.00030 | $0.00547 |
| Haiku 4.5 | $0.00015 | $0.00274 |
Grade A, and why
arkcli-train-finetune 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 17d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ArkCLI 精调训练
先读取 ../arkcli-shared/SKILL.md，遵循认证、输出、安全和二次确认规则。
适用场景与能力边界
- 校验训练/验证数据、创建精调任务：读取
references/create.md - 列出或筛选任务：读取
references/list.md - 查询、观察或操作一个指定任务：读取
references/manage.md - 根据指标选择 step、导出产物并部署：读取
references/export-deploy.md - 精调任务创建或预检所需的本地/TOS 训练、验证数据校验留在本 skill，直接调用
arkcli dataset validate；不要因为命令路径属于dataset就切换 skill。 - 独立 Dataset 的创建、查询、更新、删除、版本、下载，或不涉及精调任务的独立数据校验，转
../arkcli-datasets/SKILL.md。创建精调任务时可以直接消费本地文件、TOS URL、ds-*/dsv-*引用和模型支持的 preset。 - 普通训练 Dataset 默认使用
--train-dataset（Multiplier=1）；需要重复引用、倍率或采样数时改用可重复的--train-path。每项最多设置multiplier或sample_count之一，均不设置时仍默认Multiplier=1。preset 必须在inject_multiplier与inject_sample_count中二选一。 - 训练产物的指标分析和 artifact export 由本 skill 编排；custom model 详情、可部署版本准备和 Endpoint 创建必须按模型仓库及部署 skill 执行。
- 不把 Raw API 或精调 SDK 当默认入口。
只加载当前任务需要的 reference。不要为了熟悉全部命令一次性读取所有文件。
反唤起信号
- 只查询公共基础模型目录 → 使用
../arkcli-models/SKILL.md。 - 只管理已有推理 Endpoint → 使用
../arkcli-infer-endpoint/SKILL.md。 - 纯登录或 profile/config 排障 → 分别使用
../arkcli-auth/SKILL.md或../arkcli-config/SKILL.md。 - 不要把 Raw API 或精调 SDK 当作默认入口；只有产品命令无法表达任务且用户确认 fallback 后才进入扩展流程。
指定任务的精确范围诊断
- 用户给出
mcj-*并询问任务状态、查不到原因、日志或 trajectory 时，必须加载本 skill 并读取references/manage.md。 - “这个任务怎么查不到”首先在当前 active profile / project / region 对原始 ID 执行
arkcli train finetune get <mcj-id>，再按该权威 API 的原始结果解释。 mcj-*是不透明资源 ID。不得根据日期片段、后缀单词或臆测的哈希格式断言 ID 无效，也不得改写用户给出的 ID。- 用户要求不切环境或只查指定任务时，禁止执行
train finetune list、扫描其他任务、切换 profile/project/region，或查询其他账号。目标get失败时保留错误 code、message 和 request ID；只有用户另行授权后才能扩大范围。 - 用户要求把指定 MCJ 的日志保存到本地路径时，第一条业务命令就是
arkcli train finetune logs <mcj-id> --output <path>。不得先 list 全部任务或用脚本遍历；目标命令失败时原样报告，不建议切环境。 - 用户要求指定 MCJ 的完整 rollout trajectory 时，直接执行
arkcli train finetune trajectory list <mcj-id> --full。不存在arkcli train trajectory路径；无轨迹或未开启记录时保留原错误，不探索 profile、MCP 或其他任务。 logs --follow仅在任务活跃且可能继续产生日志时持续轮询；任务已终态时输出当前快照后自动退出，轮询中发现终态且无新日志也会退出。不要再用外部 timeout 作为正常终止机制。pause与resume是明确的可逆关系：pause将运行任务置为Paused，resume用于恢复Paused；后端允许时也可用resume重试Failed/Terminated，以当前 API 结果为准。
What ships with it
8 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.
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.
- 17d ago Changed · +6 lines 6f6fe6a764e7
- 1mo ago Changed · -4 lines · +37 tokens per session 89821540df8e
- 1mo ago First seen · 106 lines · 114 tokens per session scan A 0ddf08929b51
arkcli-train-finetune is a skill published in the GitHub repository volcengine/ark-cli (143 stars, last pushed 8d ago), licensed Apache-2.0. It adds 151 tokens to every session and 2,735 once invoked, about $0.0006 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 2026-08-30.
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