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github/fedora-linux-triage

Fedora 문제를 dnf, systemd 및 SELinux 인식 가이드를 사용하여 분류하고 해결합니다.

fedora-linux-triage란 무엇인가요?

이 스킬은 Fedora Linux 시스템의 문제를 분석하고 해결하기 위한 전문적인 지침을 제공합니다. 주로 패키지 관리(dnf), 시스템 서비스 관리(systemd), 보안 강화 Linux(SELinux)와 관련된 문제들을 대상으로 합니다. Fedora 사용자나 관리자가 발생하는 오류 메시지, 서비스 실패, 권한 문제 등을 신속하게 진단하고 해결할 수 있도록 도와줍니다. 이 스킬은 Claude Code, Cursor, Codex 등 주요 AI 에이전트 플랫폼에서 사용할 수 있으며, SKILL.md 문서에 기반하여 각 플랫폼의 특징에 맞는 명령어와 절차를 안내합니다. 시스템 로그 분석, 패키지 의존성 확인, SELinux 컨텍스트 검사 등 다양한 도구와 명령어를 활용하여 문제의 근본 원인을 파악하고, 구체적인 해결 방안을 제시합니다. 이를 통해 사용자는 복잡한 Fedora 시스템 문제를 보다 효율적으로 해결할 수 있습니다.

지원 대상~Claude Code~Codex CLI~Cursor
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즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

fedora-linux-triage은(는) 무엇을 하나요?

Fedora 문제를 dnf, systemd 및 SELinux 인식 가이드를 사용하여 분류하고 해결합니다.

설명

이 스킬은 Fedora Linux 시스템에서 발생하는 일반적인 문제를 진단하고 해결하는 방법을 제공합니다. 여기에는 다음이 포함됩니다:

  • 패키지 관리(dnf): 패키지 설치, 업데이트, 제거 문제 및 종속성 충돌 해결.
  • 시스템 서비스(systemd): 서비스 시작/중지 실패, 부팅 문제, 로그 분석.
  • 보안(SELinux): 접근 거부 오류, 컨텍스트 문제, 정책 조정.

사용 방법

  1. 문제 증상을 설명합니다(예: "SELinux가 내 웹 서버 접근을 차단합니다").
  2. 에이전트가 관련 로그와 상태를 분석합니다.
  3. 구체적인 해결 명령어와 단계를 제공합니다.

예시 명령어

# dnf 문제 확인
sudo dnf check
sudo dnf repolist

# systemd 서비스 상태 확인
systemctl status <서비스명>
journalctl -xe

# SELinux 문제 진단
sudo sealert -a /var/log/audit/audit.log
sudo getenforce
sudo semanage boolean -l

참고

  • 이 스킬은 Fedora 36 이상을 대상으로 합니다.
  • 일부 명령어는 루트 권한이 필요할 수 있습니다.
  • 문제가 지속되면 Fedora 포럼이나 문서를 참조하세요.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

github/acquire-codebase-knowledge

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.

github/add-educational-comments

Add educational comments to the file specified, or prompt asking for file to comment if one is not provided.

github/agent-governance

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)

github/agentic-eval

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

github/agent-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/ai-prompt-engineering-safety-review

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.

github/appinsights-instrumentation

Instrument a webapp to send useful telemetry data to Azure App Insights

github/apple-appstore-reviewer

Serves as a reviewer of the codebase with instructions on looking for Apple App Store optimizations or rejection reasons.

github/architecture-blueprint-generator

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.

github/arch-linux-triage

Triage and resolve Arch Linux issues with pacman, systemd, and rolling-release best practices.

github/arize-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.

github/arize-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.

github/arize-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.

github/arize-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.

github/arize-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.

github/arize-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.

github/arize-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.

github/arize-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.

github/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.

github/aspire

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.

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