所有 Skills
找到 8842 个 Skills
Skills 列表
arize-trace
Downloads, exports, and inspects existing Arize traces and spans to understand what an LLM app is doing or debug runtime issues. Covers exporting traces by ID, spans by ID, sessions by ID, and root-cause investigation using the ax CLI. Use when the user wants to look at existing trace data, see what their LLM app is doing, export traces, download spans, investigate errors, or analyze behavior regressions.
githublsp-setup
Enable code intelligence (go-to-definition, find-references, hover, type info) for any programming language by installing and configuring an LSP server for Copilot CLI. Detects the OS, installs the right server, and generates the JSON configuration (user-level or repo-level). Use when you need deeper code understanding and no LSP server is configured, or when the user asks to set up, install, or configure an LSP server.
githubarize-link
Generates deep links to the Arize UI for traces, spans, sessions, datasets, labeling queues, evaluators, and annotation configs. Produces clickable URLs for sharing Arize resources with team members. Use when the user wants to link to or open a trace, span, session, dataset, evaluator, or annotation config in the Arize UI.
githubgithub-actions-efficiency
Audit GitHub Actions workflow efficiency and recommend fixes to reduce CI minutes and costs.
githubarize-dataset
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
githubarize-annotation
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
githubarize-instrumentation
Adds Arize AX tracing to an LLM application for the first time. Follows a two-phase agent-assisted flow to analyze the codebase then implement instrumentation after user confirmation. Use when the user wants to instrument their app, add tracing from scratch, set up LLM observability, integrate OpenTelemetry or openinference, or get started with Arize tracing.
githubintegrate-context-matic
Discovers and integrates third-party APIs using the context-matic MCP server. Uses `fetch_api` to find available API SDKs, `ask` for integration guidance, `model_search` and `endpoint_search` for SDK details. Use when the user asks to integrate a third-party API, add an API client, implement features with an external API, or work with any third-party API or SDK.
githubarize-experiment
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
githubfreecad-scripts
Expert skill for writing FreeCAD Python scripts, macros, and automation. Use when asked to create FreeCAD models, parametric objects, Part/Mesh/Sketcher scripts, workbench tools, GUI dialogs with PySide, Coin3D scenegraph manipulation, or any FreeCAD Python API task. Covers FreeCAD scripting basics, geometry creation, FeaturePython objects, interface tools, and macro development.
githubarize-evaluator
Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.
githubarize-ai-provider-integration
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
githubarize-prompt-optimization
Optimizes, improves, and debugs LLM prompts using production trace data, evaluations, and annotations. Extracts prompts from spans, gathers performance signal, and runs a data-driven optimization loop using the ax CLI. Use when the user mentions optimize prompt, improve prompt, make AI respond better, improve output quality, prompt engineering, prompt tuning, or system prompt improvement.
githubreact19-test-patterns
Provides before/after patterns for migrating test files to React 19 compatibility, including act() imports, Simulate removal, and StrictMode call count changes.
github
impeccable
当用户想要设计、重新设计、塑造、评审、审计、打磨、澄清、提炼、加固、优化、适配、添加动画、着色、提取或以其他方式改进前端界面时使用。涵盖网站、落地页、仪表盘、产品UI、应用外壳、组件、表单、设置、引导流程和空状态。处理UX评审、视觉层次、信息架构、认知负荷、可访问性、性能、响应式行为、主题化、反模式、排版、字体、间距、布局、对齐、颜色、动效、微交互、UX文案、错误状态、边界情况、国际化以及可复用的设计系统或令牌。也适用于需要更大胆或更令人愉悦的平淡设计、需要更安静的大声设计、对UI元素进行实时浏览器迭代，或追求技术上非凡的视觉效果。不适用于纯后端或非UI任务。
pbakausroundup-setup
Interactive onboarding that learns your communication style, audiences, and data sources to configure personalized status briefings. Paste in examples of updates you already write, answer a few questions, and roundup calibrates itself to your workflow.
githubemail-drafter
起草和审阅符合您个人写作风格的专业电子邮件。通过 WorkIQ 分析您已发送的电子邮件中的语气、问候语、结构和签名模式，然后为任何收件人生成上下文感知的草稿。用途：起草电子邮件、撰写电子邮件、编写电子邮件、回复电子邮件、跟进电子邮件、分析电子邮件语气、电子邮件风格。
githubthreat-model-analyst
对仓库和系统进行完整的STRIDE-A威胁模型分析和增量更新技能。支持两种模式：（1）单一分析——对仓库进行完整的STRIDE-A威胁模型分析，生成架构概览、DFD图、STRIDE-A分析、优先级发现结果和管理层评估。（2）增量分析——以之前的威胁模型报告为基线，比较最新（或指定提交）的代码库，并生成带有变更跟踪（新增、已解决、仍存在的威胁）、STRIDE热图、发现差异和嵌入式HTML比较的更新报告。仅在用户明确请求威胁模型分析、增量更新或直接调用/threat-model-analyst时激活。
githubgeofeed-tuner
Use this skill whenever the user mentions IP geolocation feeds, RFC 8805, geofeeds, or wants help creating, tuning, validating, or publishing a self-published IP geolocation feed in CSV format. Intended user audience is a network operator, ISP, mobile carrier, cloud provider, hosting company, IXP, or satellite provider asking about IP geolocation accuracy, or geofeed authoring best practices. Helps create, refine, and improve CSV-format IP geolocation feeds with opinionated recommendations beyond RFC 8805 compliance. Do NOT use for private or internal IP address management — applies only to publicly routable IP addresses.
githubdaily-prep
Prepare for tomorrow''s meetings and tasks. Pulls calendar from Outlook via WorkIQ, cross-references open tasks and workspace context, classifies meetings, detects conflicts and day-fit issues, finds learning and deep-work slots, and generates a structured HTML prep file with productivity recommendations.
githubroundup
按需生成个性化状态简报。从您配置的数据源（GitHub、电子邮件、Teams、Slack 等）拉取信息，进行综合，并以您自己的沟通风格为任何您定义的受众起草更新。
github
deep-research
通用深度研究 Agent 团队。由 13 个 Agent 构成的流水线，可针对任意主题开展严谨的学术研究。提供 8 种模式：全流程深度研究、快速简报、论文评审、文献综述、事实查核、三段式文献扫描、苏格拉底式引导研究对话，以及可选 meta 分析的系统性综述。覆盖研究问题提炼、导师级苏格拉底式引导、研究方法设计、系统性文献检索、文献来源验证、跨来源综合合成、偏倚风险评估、meta 分析、APA 7.0 格式报告撰写、主编评审、魔鬼代言人质询、伦理审查以及研究后文献动态监测。触发词包括：research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘.
imbad0202academic-pipeline
全流程学术研究管线编排器：研究 -> 写作 -> 学术诚信检查 -> 审稿 -> 修改 -> 复审 -> 二次修改 -> 终极学术诚信检查 -> 终稿定稿。高效协同 deep-research、academic-paper 和 academic-paper-reviewer，打造包含强制性学术诚信校验、两轮同行评审与可复现质量关卡的无缝 10 阶段工作流。触发词：academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow, 연구부터 논문까지, 연구 주제 설정부터 논문 완성까지, 논문 전체 워크플로。
imbad0202agent-owasp-compliance
Check any AI agent codebase against the OWASP Agentic Security Initiative (ASI) Top 10 risks. Use this skill when: - Evaluating an agent system's security posture before production deployment - Running a compliance check against OWASP ASI 2026 standards - Mapping existing security controls to the 10 agentic risks - Generating a compliance report for security review or audit - Comparing agent framework security features against the standard - Any request like "is my agent OWASP compliant?", "check ASI compliance", or "agentic security audit"
github