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 ascend-ai-coding/awesome-ascend-skills --skill verl-feature-deploygit clone --depth 1 https://github.com/ascend-ai-coding/awesome-ascend-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/ascend-ai-coding/awesome-ascend-skills/verl-feature-deploy)<a href="https://agentmods.dev/skills/ascend-ai-coding/awesome-ascend-skills/verl-feature-deploy"><img src="https://agentmods.dev/badge/skills/ascend-ai-coding/awesome-ascend-skills/verl-feature-deploy/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/ascend-ai-coding/awesome-ascend-skills/verl-feature-deploy"><img src="https://agentmods.dev/badge/skills/ascend-ai-coding/awesome-ascend-skills/verl-feature-deploy.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 | $0.00123 | $0.03474 |
| Opus 5 | $0.00062 | $0.01737 |
| Sonnet 5 | $0.00025 | $0.00695 |
| Haiku 4.5 | $0.00012 | $0.00347 |
Grade A, and why
external-gitcode-ascend-verl-feature-deploy 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 7d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
9 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.
- assets/start_template.sh 5.7 KB runs code
- assets/training_template.sh 9.9 KB runs code
- references/feature-guide.md 3.1 KB
- references/ops-commands.md 1.3 KB
- references/troubleshooting.md 3.0 KB
- scripts/feature_mask.sh 8.9 KB runs code
- scripts/generate_training.sh 10 KB runs code
- scripts/pre_check.sh 2.0 KB runs code
- scripts/verl_docker_run.sh 3.0 KB runs code
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.
- 7d ago First seen · 374 lines · 123 tokens per session scan A 42903977fd4e
external-gitcode-ascend-verl-feature-deploy is a skill published in the GitHub repository ascend-ai-coding/awesome-ascend-skills (168 stars, last pushed yesterday), with no licence file. It adds 123 tokens to every session and 3,474 once invoked, about $0.0006 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 2026-09-05.
Other skills, from other repositories
etl-integration-nifi
Apache NiFi specialist for flow-based data integration, routing, and provenance tracking. Deep expertise in processors, FlowFiles, connections, process groups, clustering, record-oriented processing, and NiFi 2.x modernization. WHEN: \"Apache NiFi\", \"NiFi\", \"NiFi processor\", \"FlowFile\", \"process group\"…
companion-clis
Companion CLIs for Runpod workflows — HuggingFace, GitHub, Docker, and AWS.
eks-genai
Use whenever someone is building, training, fine-tuning, or serving a generative AI / LLM workload on Amazon EKS — phrased as "GPU vs Trainium/Inferentia", "vLLM on EKS", "Ray Serve / KubeRay", "distributed training on EKS", "FSx for Lustre for ML", "Karpenter for GPU", "EFA / NCCL multi-node", "DCGM / Neuron…
agentic-eks-bootstrap
Bootstrap an AWS EKS cluster optimized for Agentic AI workloads — Karpenter v1.2+ GPU node pools, EKS Auto Mode, Kubernetes 1.32+ with DRA 1.35 GA, VPC CNI, GPU Operator, and baseline observability. Use when starting a new EKS cluster that will host vLLM, Inference Gateway, Langfuse, or Kagent.
alterlab-modal
Part of the AlterLab Academic Skills suite. Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).