RFC 8805 및 IP 지리적 위치 피드(Geofeed) 생성, 조정, 검증 및 게시를 지원하는 스킬입니다. 네트워크 운영자, ISP, 모바일 통신사, 클라우드 제공업체, 호스팅 회사, IXP 또는 위성 제공업체가 IP 위치 정확도 향상 및 Geofeed 작성 모범 사례를 문의할 때 사용하세요.
이 스킬은 'Geofeed'(RFC 8805 형식의 IP 지리적 위치 피드) 생성, 조정, 검증 및 게시 과정을 지원합니다. 주 사용자는 네트워크 운영자, ISP, 통신사, 클라우드 제공업체 등으로, 이들이 자체적으로 CSV 형식의 위치 정보를 게시할 때 필요한 규칙과 권장 사항을 제공합니다. RFC 8805 표준 준수뿐만 아니라, 실제 네트워크 운영 경험에 기반한 주관적인 모범 사례(예: 불필요한 세분화 방지, 일관된 시간대 사용, 위치 데이터 검증 규칙 등)를 함께 제안합니다. 개인 또는 내부 IP 주소 관리에는 사용하지 말고, 인터넷상에서 라우팅 가능한 공용 IP 주소에만 적용됩니다. 주요 기능으로는 CSV 피드 구조 검증, 누락 또는 잘못된 필드 감지, 위치 정확도 향상을 위한 수정 제안, 게시 전 최종 검사(예: WHOIS 대조, 라우팅 테이블 확인 권장) 등이 포함됩니다. AI 어시스턴트(Claude Code, Cursor 등)와 함께 사용하면 대화형으로 Geofeed를 설계하고 개선할 수 있어, 수동 작업 대비 효율성과 정확성을 크게 높일 수 있습니다.
npx skills add https://github.com/github/awesome-copilot/tree/main/skills/geofeed-tunerInstalled? Explore more 코딩 & 개발 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
사용자가 IP 지리적 위치 피드, RFC 8805, Geofeed, 또는 CSV 형식의 자체 게시 IP 지리적 위치 피드 생성, 조정, 검증, 게시에 대한 도움을 요청할 때 이 스킬을 사용하세요. 대상 사용자는 네트워크 운영자, ISP, 모바일 통신사, 클라우드 제공업체, 호스팅 회사, IXP 또는 위성 제공업체로, IP 위치 정확도 또는 Geofeed 작성 모범 사례를 문의하는 경우입니다. RFC 8805 준수를 넘어, 주관적인 권장 사항을 포함하여 CSV 형식 IP 위치 피드를 생성, 개선 및 향상시키는 데 도움을 줍니다. 개인 또는 내부 IP 주소 관리에는 사용하지 마십시오. 공개적으로 라우팅 가능한 IP 주소에만 적용됩니다.
This repo contains 20 individual skills — each has its own dedicated page.
Use this skill when the user explicitly asks to map, document, or onboard into an existing codebase. Trigger for prompts like "map this codebase", "document this architecture", "onboard me to this repo", or "create codebase docs". Do not trigger for routine feature implementation, bug fixes, or narrow code edits unless the user asks for repository-level discovery.
Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.
Patterns and techniques for adding governance, safety, and trust controls to AI agent systems. Use this skill when: - Building AI agents that call external tools (APIs, databases, file systems) - Implementing policy-based access controls for agent tool usage - Adding semantic intent classification to detect dangerous prompts - Creating trust scoring systems for multi-agent workflows - Building audit trails for agent actions and decisions - Enforcing rate limits, content filters, or tool restrictions on agents - Working with any agent framework (PydanticAI, CrewAI, OpenAI Agents, LangChain, AutoGen)
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality
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"
Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.
Instrument a webapp to send useful telemetry data to Azure App Insights
Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.
Comprehensive project architecture blueprint generator that analyzes codebases to create detailed architectural documentation. Automatically detects technology stacks and architectural patterns, generates visual diagrams, documents implementation patterns, and provides extensible blueprints for maintaining architectural consistency and guiding new development.
Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when the user asks to create, run, debug, configure, deploy, or troubleshoot an Aspire distributed application.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.