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Found 502 Skills
Docker Compose 编排
Prepare a research artifact package for conference artifact evaluation, reproducibility review, badges, supplementary material, or post-acceptance artifact release. Use this skill whenever the user needs install instructions, reviewer-facing reproduction commands, Docker or environment checks, data/checkpoint packaging, hardware/runtime estimates, anonymized or public artifact metadata, artifact evaluation forms, or a claim-to-artifact reproducibility audit for ML/AI venues.
Remediate OS and base-image CVEs in Docker-hosted applications. Use for base image candidate discovery, Docker Scout based comparison, Dockerfile updates, and OS remediation reporting.
Especialista em infraestrutura e entrega contínua no SynkOS. Use esta skill quando o usuário pedir para configurar um pipeline de CI/CD, dockerizar um serviço, preparar ou executar um deploy, configurar monitoramento e alertas, auditar infraestrutura, gerenciar secrets e variáveis de ambiente, ou fazer perguntas como "configure o CI para o projeto X", "crie o Dockerfile para Y", "o que verificar antes do deploy?", "como configurar logs e alertas?", "audite a infraestrutura", "valide o ambiente de produção". Ative também para criar documentação de rollback, validar saúde pós-deploy, e garantir que toda mudança de ambiente está versionada como código.
Specialized in Container diagrams (Level 2) with Infrastructure mapping. Use this skill when the user requests decomposing systems into separately deployable units, identifying the tech stack, and mapping infrastructure components (Docker, K8s).
Deploy Slidev presentations to the web. Use this skill for GitHub Pages, Netlify, Vercel, and Docker deployments.
Zero Script QA - Testing methodology without test scripts. Uses structured JSON logging and real-time Docker monitoring for verification. Use proactively when user needs to verify features through log analysis instead of test scripts. Triggers: zero script qa, log-based testing, docker logs, 제로 스크립트 QA, ゼロスクリプトQA, 零脚本QA, QA sin scripts, pruebas basadas en logs, registros de docker, QA sans script, tests basés sur les logs, journaux docker, skriptloses QA, log-basiertes Testen, Docker-Logs, QA senza script, test basati sui log, log docker Do NOT use for: unit testing, static analysis, or projects without Docker setup.
This skill should be used when users want to run any workload on Hugging Face Jobs infrastructure. Covers UV scripts, Docker-based jobs, hardware selection, cost estimation, authentication with tokens, secrets management, timeout configuration, and result persistence. Designed for general-purpose compute workloads including data processing, inference, experiments, batch jobs, and any Python-based tasks. Should be invoked for tasks involving cloud compute, GPU workloads, or when users mention running jobs on Hugging Face infrastructure without local setup.
SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.
Local pentest sandbox for a full black-box engagement. Triggers on "kage", "pentest", "security audit on", "audit the security of". Runs recon, deep testing, exploit verification, and judging inside a per-engagement Kali Docker container. Each host working directory gets its own isolated sandbox. Produces `./results/<target>/audit-report.md`.
Verifies a Taubyte Go function locally via the `taubyte/go-wasi` Docker recipe (preferred over `tau build`, with tmpfs+bind-mount-ro to avoid root-owned artifacts in the source tree), and verifies a function actually serves on Dream by curling the gateway with the right `Host:` header (plus `/etc/hosts` mapping for `*.localtau`). Use when locally compiling a Go function to WASM, when smoke-testing a function before pushing, or when probing a Dream-hosted HTTP function from the laptop.
Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.