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 huaweicloud/huaweicloud-skills --skill huawei-cloud-cloudrobo-traingit clone --depth 1 https://github.com/huaweicloud/huaweicloud-skillsWrote 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/huaweicloud/huaweicloud-skills/huawei-cloud-cloudrobo-train)<a href="https://agentmods.dev/skills/huaweicloud/huaweicloud-skills/huawei-cloud-cloudrobo-train"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-cloudrobo-train/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/huaweicloud/huaweicloud-skills/huawei-cloud-cloudrobo-train"><img src="https://agentmods.dev/badge/skills/huaweicloud/huaweicloud-skills/huawei-cloud-cloudrobo-train.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 | $0.00264 | $0.12796 |
| Opus 5 | $0.00132 | $0.06398 |
| Sonnet 5 | $0.00053 | $0.02559 |
| Haiku 4.5 | $0.00026 | $0.01280 |
Grade A, and why
huawei-cloud-cloudrobo-train 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 2d 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 — 742 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Windows / PowerShell: Examples use bash syntax. To run on Windows PowerShell:
- Flatten
\line continuations to a single line, or end lines with a backtick.- Set env vars with
$env:NAME="value"instead ofexport NAME="value".- Single-quoted JSON
'{"a":"b"}'works as-is.
Overview
Manages the full lifecycle of CloudRobo model training tasks and SimRL tasks. Two training modes (MODEL_TUNING / TRAIN_FROM_SCRATCH), five methods (FFT/SFT/LORA/QLORA/DEEPSPEED). Covers creation, monitoring, diagnosis, drafts, stats, resume, and pipeline orchestration.
Two task surfaces (switchable via --sim-rl):
| Surface | API prefix | CLI switch | SDK methods | Resume |
|---|---|---|---|---|
| Regular training | /v1/training/train-tasks |
(default) | 19 train_* |
Yes |
| SimRL | /v1/training/rl-tasks/simulation |
--sim-rl |
16 sim_rl_* |
No |
Scenarios: Fine-tuning, pretraining, simulation RL, draft save/resubmit, monitoring (stages/ resource/events), failure diagnosis, stats. Training runs for hours/days; poll at 30-60s intervals.
Agent → CLI (`cloudrobo train <command> [--sim-rl]`) or SDK (`TrainClient`)
→ cloudrobo-service (REST API)
All operations target cloudrobo-service and require a workspace_id. Model/dataset discovery
calls cloudrobo-asset-manager via cloudrobo asset.
Prerequisites
See references/cli-installation-guide.md for CLI installation, AK/SK authentication, and
workspace configuration. All training operations require a valid workspace_id.
Workspace auto-resolution: The SDK and CLI automatically resolve workspace_id in this order:
- Explicit
--workspace-idparameter (if provided) - Configured default workspace (via
cloudrobo workspace use --workspace-id <id>) - Auto-query: if no workspace is configured, the SDK queries
cloudrobo workspace list, picks the first available workspace, saves it as the default, and uses it
If auto-query fails (no workspaces found), the error message guides the user to create a workspace.
To manually set the default workspace: cloudrobo workspace use --workspace-id <id>.
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.
- references/acceptance-criteria.md 6.7 KB
- references/api-paths.md 12 KB
- references/cli-installation-guide.md 4.6 KB
- references/dataflow-diagram.md 6.4 KB
- references/iam-policies.md 5.1 KB
- references/sdk-quickstart.md 21 KB
- references/task-config-catalog.md 27 KB
- references/verification-method.md 8.4 KB
- scripts/test-cli-commands.sh 7.9 KB runs code
- templates/test-vars.json 11 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.
- 2d ago First seen · 742 lines · 264 tokens per session scan A cb8217a0e269
huawei-cloud-cloudrobo-train is a skill published in the GitHub repository huaweicloud/huaweicloud-skills (49 stars, last pushed yesterday), licensed MIT. It adds 264 tokens to every session and 12,796 once invoked, about $0.0013 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 2026-09-10.
Other skills, from other repositories
nemotron-retrieval-recipes
Use when planning, debugging, tuning, evaluating, exporting, or deploying public Nemotron embed/rerank retrieval recipes.
nemotron-nano3
Reference desk for Nemotron 3 Nano / Llama-Nemotron Nano 3 — architecture, training data, recipes, evaluation, quantization, deployment. Use when the user asks facts about the model rather than building a pipeline.
nemotron-customize
Plan, configure, and chain repo-native Nemotron customization steps into single-step or multi-step pipelines: curation, translation, SFT/PEFT (AutoModel or Megatron-Bridge), pretraining/CPT, RL alignment (DPO/RLVR/GRPO/RLHF), BYOB/MCQ benchmarks, checkpoint conversion, ModelOpt optimization, env profiles, and…
nemotron-super3
Reference desk for NVIDIA Nemotron 3 Super — architecture, training data, recipes (pretrain/SFT/RL/eval/quantization), and deployment notes. Use when the user asks facts about Super3 rather than building a pipeline.
nemotron-add-model
Onboard a new model family (Nemotron or third-party) into skills/ — paper chunks, recipe summaries, context packs, and model card. Use when a contributor wants downstream skills like /nemotron-customize to be able to route to a new model.
nemotron-ultra
Reference desk for NVIDIA Nemotron 3 Ultra (550B-A55B) — architecture, NVFP4 pretraining, SFT, MOPD (multi-teacher on-policy distillation), MTP boosting, quantization, inference. Use when the user asks facts about Ultra rather than building a pipeline.