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-gengit 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-gen)<a href="https://agentmods.dev/skills/volcengine/ark-cli/arkcli-gen"><img src="https://agentmods.dev/badge/skills/volcengine/ark-cli/arkcli-gen/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-gen"><img src="https://agentmods.dev/badge/skills/volcengine/ark-cli/arkcli-gen.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 | $0.00051 | $0.07722 |
| Opus 5.5 | $0.00020 | $0.03089 |
| Sonnet 5.5 | $0.00010 | $0.01544 |
| Haiku 4.5 | $0.00005 | $0.00772 |
Grade A, and why
arkcli-gen scanned grade A 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 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| **视频** | **异步**:立即返回 `task_id` + `status: queued` | `queued` **不是失败**。用 `arkcli gen get <task_id> --open` 轮询到 `succeeded`——**这次 `gen get` 会顺手把产物下载到本地并回带 `local_path`**(默认 CWD,`<task-id>.mp4`),`--open` 让成品直接在用户桌面弹出(你是 a How it starts
The opening of the file, as written. The whole thing — 292 lines — stays where its author put it; the contents beside it link to each section on GitHub.
arkcli 生成工作流(+gen)
CRITICAL — 开始前 MUST 先用 Read 工具读取 ../arkcli-shared/SKILL.md(认证闸门、模型查找回退、共享安全规则)。
CRITICAL — 真实生成是工作流,不是猜一条命令:按 Step 1 → Step 2 → 必要时 Step 2.5 → Step 3 执行。用户只要求 --dry-run 时例外:全程本地,不先跑在线 Resources / Models / Usage 准入;在线未知项保留 unresolved。执行前务必读 references/arkcli-gen.md。
CRITICAL — 用户显式给出 API Key / Base URL / Endpoint 时,MUST 先读 ../arkcli-shared/references/execution-context.md。显式 Endpoint 的权威元数据优先于当前 profile。
火山额外约束:不要因为 active profile 是 Agent/Coding Plan 就把用户给出的 Endpoint 当套餐模型调用。
Agent 快速执行顺序
先把用户要求拆成可验收项,再按资源、能力、整批预算、执行、成品检查推进。执行前读取
references/intent-and-validation.md:它约定不完整意图的默认值、
参数组合、凭证错误分流和成品验收。参数表是候选能力,不代表每个模型都支持。
用户已明确要图片或视频时,生成命令显式带 --modality image|video;不要让名称解析覆盖用户意图。
只使用用户本轮提供或明确授权复用的素材;不得从历史目录、旧任务或相似文件名擅自加入额外参考图/视频。
成品验收前:宿主视觉输入准入
生成模型能出图,不代表驱动当前 Agent 的宿主模型能看图。Read 工具描述说支持图片, 也不是当前宿主模型的视觉能力证明。 当前会话没有可靠的视觉能力声明或已验证的兼容性时, 按“未准入”处理:不要原生 Read 图片、视频或抽出的帧,也不要用成品试探能否读取。 本 Skill、参考说明和 JSON 等文本仍可正常 Read。
- 已有兼容且已授权的视觉工具,或同一主体下已准入、收费路径不变的视觉模型时, 按对应 Chat/Understand Skill 分析实际媒体,只把文字结果返回宿主;不自动换宿主、Profile 或 Key。
- 没有这样的入口时,完成文件/解码/尺寸/时长等结构检查并交付已有成品,明确“视觉语义未验”; 不声称看过或完全符合。不要为补验收擅自增加未授权的收费调用。
- 生成成功后宿主读图报错,是验收/交付失败;保留成品,不重新提交生成或轮转业务 Key。
反唤起信号
- 描述、分析现有图像而不是生成/编辑 →
arkcli-chat或有固定产出形态的arkcli-understand。 - 只查资源、能力、用量 → 对应只读 Skill,不提交生成任务。
为什么是工作流(核心,先理解再执行)
用户说"生成一个视频/一张图",本质是至少三件独立的事,必须按序;批量或多阶段任务还要先确认整批可完成:
① 本次资源从哪里来 ── 用户显式 Endpoint 优先;否则看当前 profile
② 该模型支持哪些参数 ── 不查就传参 = 瞎猜 = 被校验拒/被后端拒
②.5 多候选/多阶段额度与任务数 ── 先算完整批次,额度已耗尽就不启动半批任务
③ 按可用参数真去生成 ── 每个请求只提交一次并立即保存 task_id
把这三步压成"直接 +gen 猜一条命令",正是失败之源:模型名形态不对会 404,参数模型不支持会被拒。
模态解析硬契约
+gen 的生产调用按以下固定优先级解析能力:
explicit --modality > output_modalities > task types > unknown
- 直接传版本化模型 ID 时,读取 ArkModels 返回的
output_modalities;缺失时再读取 FoundationModel 的task_types/filter_task_types。 - 传
ep-*时,先读取 Endpoint 的ModelReference.FoundationModel(name, version),再精确匹配同版本模型的上述结构化元数据。 - 模型名与 DisplayName 只用于定位模型,不参与模态判断。不要从
seedream、seedance或任何国内/海外品牌前缀推断 image/video。 - 结构化元数据缺失或互相冲突时返回
unknown,提示用户显式传--modality image|video;禁止静默猜测。 +gen --dry-run是纯本地 Client Preview:不读取 Endpoint/模型元数据、不调用 生成 API,也不下载或打开文件。显式--modality最可靠;已知seedream/seedance模型名可本地判断,其他模型或 Endpoint 必须显式传--modality image|video。在线才能补齐的执行上下文会以unresolved和fidelity=partial明示。
What ships with it
5 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 · +16 lines 15f5cf68c5a6
- 23d ago Changed 2c8fa70ce575
- 1mo ago Changed · +57 lines b464c680a975
- 1mo ago Changed · +10 lines 41f58da78688
- 1mo ago First seen · 209 lines · 51 tokens per session scan A ea5126425d9e
arkcli-gen is a skill published in the GitHub repository volcengine/ark-cli (143 stars, last pushed 8d ago), licensed Apache-2.0. It adds 51 tokens to every session and 7,722 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
plugin-system
Generic plugin system for Python applications. Auto-discovery, validation, fault tolerance. Zero dependencies (Python stdlib only).
batch-file-ops
Batch-Dateioperationen (Löschen, Verschieben, Kopieren, Auflisten) mit Glob-Mustern. CLI-Werkzeug für effiziente Dateisystem-Operationen. Ohne Abhängigkeiten.
datamol
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters…
clinicaltrials-database
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
openalex-database
Query and analyze scholarly literature using the OpenAlex database. This skill should be used when searching for academic papers, analyzing research trends, finding works by authors or institutions, tracking citations, discovering open access publications, or conducting bibliometric analysis across 240M+ scholarly…
esm
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel…