Loading...
Loading...
Found 1,211 Skills
Deploy Alibaba Cloud official tech solutions. Trigger when the user mentions an Alibaba Cloud solution, pastes a solution URL (aliyun.com/solution/tech-solution/...), or wants to deploy an official solution template. Covers both Terraform module deployment and CLI step-by-step execution paths.
Use when porting a workflow to a different AI provider, deployment environment, model tier, or organizational context.
Capable of completing the installation and deployment of Ascend NPU drivers and firmware, featuring regular expression-based installation package extraction, on-demand addition of executable permissions, dual package verification via Python+Shell, pre-check and installation of system dependencies, and compatibility with CentOS/RHEL/Ubuntu/Debian systems. It is suitable for the installation and deployment of Ascend NPU drivers and firmware.
昇腾（Ascend）推理生态开源代码仓库智能问答专家旨在为 vLLM、vLLM-Ascend、MindIE-LLM、MindIE-SD、MindIE-Motor、MindIE-Turbo 以及 msModelSlim (MindStudio-ModelSlim) 等仓库提供专家级且易于理解的解释。在处理昇腾（Ascend）推理生态相关项目的用户询问时，务必触发此技能（Skill），可解答使用方法、部署流程、支持模型、支持特性、系统架构、配置管理、调试、测试、故障排查、性能优化、定制开发、源码解析以及其他技术问题。支持中英文双语回复，并可借助 deepwiki MCP 工具检索仓库知识库，生成具备上下文感知且基于证据的回答。Ascend inference ecosystem open-source code repository intelligent question-and-answer (Q&A) expert. Provide expert-level yet comprehensible explanations for repositories such as vLLM, vLLM-Ascend, MindIE-LLM, MindIE-SD, MindIE-Motor, MindIE-Turbo, and msModelSlim (MindStudio-ModelSlim). Use this skill when addressing user inquiries related to these Ascend inference ecosystem projects, including topics such as usage, deployment process, supported models, supported features, system architecture, configuration management, debugging, testing, troubleshooting, performance optimization, custom development, source code analysis, and any other technical issues about these projects. Support responses in both Chinese and English. Use deepwiki MCP tools to query repository knowledge bases and generate context-aware, evidence-based responses.
Early rug-risk triage for token launches and small DeFi deployments from public data—liquidity lock and pool events, dev and sniper wallet clustering, contract authority and transfer-risk checks, coordinated exits, and evidence-backed risk scores. Use when the user asks for rug pull detection, pump-and-dump signals, launch red flags, LP removal forensics, or cross-chain profit exit tracing—not for front-running trades, harassing teams, or certifying scams without on-chain proof.
Reusable better-chatbot patterns for custom deployments. Use for server action validators, tool abstraction, multi-AI providers, or encountering auth validation, FormData parsing, workflow execution errors.
Orchestrates Android development tasks including project creation, deployment, SDK management, and environment diagnostics using the `android` command-line tool.
Performs automated static analysis of Android applications using Mobile Security Framework (MobSF) to identify hardcoded secrets, insecure permissions, vulnerable components, weak cryptography, and code-level security flaws without executing the application. Use when assessing Android APK/AAB files for security vulnerabilities before deployment, during penetration testing, or as part of CI/CD security gates. Activates for requests involving Android static analysis, MobSF scanning, APK security assessment, or mobile application code review.
Install and configure LLMem for an agent harness. Handles CLI install, plugin deployment, skill registration, and provider setup. Triggers on: "install llmem", "set up memory", "configure memory", "add llmem to harness", "memory setup".
Navigate the Hermes Agent ecosystem — skills, tools, integrations, deployment, and multi-agent orchestration resources
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs. Preserves explicit latency / balanced / throughput objectives. Excludes disaggregated, multi-node, and non-MTP speculative configs.
Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters. Use when the user asks "check job status", "is my job done", "monitor my evaluation", "what's the status of the PTQ", "check on job <slurm_job_id>", or after any skill submits a long-running job. Also triggers on "nel status", "squeue", or any request to check progress of a previously submitted job.