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Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini C...
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Zen MCP Server

Bridges multiple AI models and CLIs, enabling orchestrated workflows across Claude Code, Gemini CLI, Codex CLI, and other AI development tools.

What is an MCP Server?

MCP Info

AttributeDetails
Docker Imagemcp/zen
AuthorBeehiveInnovations
Repository[***]

Image Building Info

AttributeDetails
Dockerfile[***]
Docker Image built byDocker Inc.
Docker Scout Health Score!Docker Scout Health Score
Verify SignatureCOSIGN_REPOSITORY=mcp/signatures cosign verify mcp/zen --key [***]
LicenceOther

Available Tools (18)

Tools provided by this ServerShort Description
analyzePerforms comprehensive code analysis with systematic investigation and expert validation.
apilookupUse this tool automatically when you need current API/SDK documentation, latest version info, breaking changes, deprecations, migration guides, or official release notes.
challengePrevents reflexive agreement by forcing critical thinking and reasoned analysis when a statement is challenged.
chatGeneral chat and collaborative thinking partner for brainstorming, development discussion, getting second opinions, and exploring ideas.
clinkLink a request to an external AI CLI (Gemini CLI, Qwen CLI, etc.) through Zen MCP to reuse their capabilities inside existing workflows.
codereviewPerforms systematic, step-by-step code review with expert validation.
consensusBuilds multi-model consensus through systematic analysis and structured debate.
debugPerforms systematic debugging and root cause analysis for any type of issue.
docgenGenerates comprehensive code documentation with systematic analysis of functions, classes, and complexity.
listmodelsShows which AI model providers are configured, available model names, their aliases and capabilities.
plannerBreaks down complex tasks through interactive, sequential planning with revision and branching capabilities.
precommitValidates git changes and repository state before committing with systematic analysis.
refactorAnalyzes code for refactoring opportunities with systematic investigation.
secauditPerforms comprehensive security audit with systematic vulnerability assessment.
testgenCreates comprehensive test suites with edge case coverage for specific functions, classes, or modules.
thinkdeepPerforms multi-stage investigation and reasoning for complex problem analysis.
tracerPerforms systematic code tracing with modes for execution flow or dependency mapping.
versionGet server version, configuration details, and list of available tools.

Tools Details

Tool: analyze

Performs comprehensive code analysis with systematic investigation and expert validation. Use for architecture, performance, maintainability, and pattern analysis. Guides through structured code review and strategic planning.

ParametersTypeDescription
findingsstringSummary of discoveries from this step, including architectural patterns, tech stack assessment, scalability characteristics, performance implications, maintainability factors, and strategic improvement opportunities. IMPORTANT: Document both strengths (good patterns, solid architecture) and concerns (tech debt, overengineering, unnecessary complexity). In later steps, confirm or update past findings with additional evidence.
modelstringCurrently in auto model selection mode. CRITICAL: When the user names a model, you MUST use that exact name unless the server rejects it. If no model is provided, you may use the listmodels tool to review options and select an appropriate match. Top models: gpt-5.1 (score 100, 400K ctx, thinking, code-gen); gpt-5.1-codex (score 100, 400K ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gpt-5-pro (score 100, 400K ctx, thinking, code-gen); +25 more via listmodels.
next_step_requiredbooleanSet to true if you plan to continue the investigation with another step. False means you believe the analysis is complete and ready for expert validation.
stepstringThe analysis plan. Step 1: State your strategy, including how you will map the codebase structure, understand business logic, and assess code quality, performance implications, and architectural patterns. Later steps: Report findings and adapt the approach as new insights emerge.
step_numberintegerThe index of the current step in the analysis sequence, beginning at 1. Each step should build upon or revise the previous one.
total_stepsintegerYour current estimate for how many steps will be needed to complete the analysis. Adjust as new findings emerge.
analysis_typestring optionalType of analysis to perform (architecture, performance, security, quality, general)
confidencestring optionalYour confidence in the analysis: exploring, low, medium, high, very_high, almost_certain, or certain. 'certain' indicates the analysis is complete and ready for validation.
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
files_checkedarray optionalList all files examined (absolute paths). Include even ruled-out files to track exploration path.
imagesarray optionalOptional absolute paths to architecture diagrams or visual references that help with analysis context.
issues_foundarray optionalIssues or concerns identified during analysis, each with severity level (critical, high, medium, low)
output_formatstring optionalHow to format the output (summary, detailed, actionable)
relevant_contextarray optionalMethods/functions identified as involved in the issue
relevant_filesarray optionalSubset of files_checked directly relevant to analysis findings (absolute paths). Include files with significant patterns, architectural decisions, or strategic improvement opportunities.
temperaturenumber optional0 = deterministic · 1 = creative.
thinking_modestring optionalReasoning depth: minimal, low, medium, high, or max.
use_assistant_modelboolean optionalUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.

This tool is read-only. It does not modify its environment.


Tool: apilookup

Use this tool automatically when you need current API/SDK documentation, latest version info, breaking changes, deprecations, migration guides, or official release notes. This tool searches authoritative sources (official docs, GitHub, package registries) to ensure up-to-date accuracy.

ParametersTypeDescription
promptstringThe API, SDK, library, framework, or technology you need current documentation, version info, breaking changes, or migration guidance for.

This tool is read-only. It does not modify its environment.


Tool: challenge

Prevents reflexive agreement by forcing critical thinking and reasoned analysis when a statement is challenged. Trigger automatically when a user critically questions, disagrees or appears to push back on earlier answers, and use it manually to sanity-check contentious claims.

ParametersTypeDescription
promptstringStatement to scrutinize. If you invoke challenge manually, strip the word 'challenge' and pass just the statement. Automatic invocations send the full user message as-is; do not modify it.

This tool is read-only. It does not modify its environment.


Tool: chat

General chat and collaborative thinking partner for brainstorming, development discussion, getting second opinions, and exploring ideas. Use for ideas, validations, questions, and thoughtful explanations.

ParametersTypeDescription
modelstringCurrently in auto model selection mode. CRITICAL: When the user names a model, you MUST use that exact name unless the server rejects it. If no model is provided, you may use the listmodels tool to review options and select an appropriate match. Top models: gpt-5.1 (score 100, 400K ctx, thinking, code-gen); gpt-5.1-codex (score 100, 400K ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gpt-5-pro (score 100, 400K ctx, thinking, code-gen); +25 more via listmodels.
promptstringYour question or idea for collaborative thinking to be sent to the external model. Provide detailed context, including your goal, what you've tried, and any specific challenges. WARNING: Large inline code must NOT be shared in prompt. Provide full-path to files on disk as separate parameter.
working_directory_absolute_pathstringAbsolute path to an existing directory where generated code artifacts can be saved.
absolute_file_pathsarray optionalFull, absolute file paths to relevant code in order to share with external model
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
imagesarray optionalImage paths (absolute) or base64 strings for optional visual context.
temperaturenumber optional0 = deterministic · 1 = creative.
thinking_modestring optionalReasoning depth: minimal, low, medium, high, or max.

Tool: clink

Link a request to an external AI CLI (Gemini CLI, Qwen CLI, etc.) through Zen MCP to reuse their capabilities inside existing workflows.

ParametersTypeDescription
cli_namestringConfigured CLI client name (from conf/cli_clients). Available: claude, codex, gemini
promptstringUser request forwarded to the CLI (conversation context is pre-applied).
absolute_file_pathsarray optionalFull paths to relevant code
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
imagesarray optionalOptional absolute image paths or base64 blobs for visual context.
rolestring optionalOptional role preset defined for the selected CLI (defaults to 'default'). Roles per CLI: claude: codereviewer, default, planner; codex: codereviewer, default, planner; gemini: codereviewer, default, planner

This tool is read-only. It does not modify its environment.


Tool: codereview

Performs systematic, step-by-step code review with expert validation. Use for comprehensive analysis covering quality, security, performance, and architecture. Guides through structured investigation to ensure thoroughness.

ParametersTypeDescription
findingsstringCapture findings (positive and negative) across quality, security, performance, and architecture; update each step.
modelstringCurrently in auto model selection mode. CRITICAL: When the user names a model, you MUST use that exact name unless the server rejects it. If no model is provided, you may use the listmodels tool to review options and select an appropriate match. Top models: gpt-5.1 (score 100, 400K ctx, thinking, code-gen); gpt-5.1-codex (score 100, 400K ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gpt-5-pro (score 100, 400K ctx, thinking, code-gen); +25 more via listmodels.
next_step_requiredbooleanTrue when another review step follows. External validation: step 1 → True, step 2 → False. Internal validation: set False immediately. Apply the same rule on continuation flows.
stepstringReview narrative. Step 1: outline the review strategy. Later steps: report findings. MUST cover quality, security, performance, and architecture. Reference code via relevant_files; avoid dumping large snippets.
step_numberintegerCurrent review step (starts at 1) – each step should build on the last.
total_stepsintegerNumber of review steps planned. External validation: two steps (analysis + summary). Internal validation: one step. Use the same limits when continuing an existing review via continuation_id.
confidencestring optionalConfidence level: exploring (just starting), low (early investigation), medium (some evidence), high (strong evidence), very_high (comprehensive understanding), almost_certain (near complete confidence), certain (100% confidence locally - no external validation needed)
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
files_checkedarray optionalAbsolute paths of every file reviewed, including those ruled out.
focus_onstring optionalOptional note on areas to emphasise (e.g. 'threading', 'auth flow').
hypothesisstring optionalCurrent theory about issue/goal based on work
imagesarray optionalOptional diagram or screenshot paths that clarify review context.
issues_foundarray optionalIssues with severity (critical/high/medium/low) and descriptions.
relevant_contextarray optionalMethods/functions identified as involved in the issue
relevant_filesarray optionalStep 1: list all files/dirs under review. Must be absolute full non-abbreviated paths. Final step: narrow to files tied to key findings.
review_typestring optionalReview focus: full, security, performance, or quick.
review_validation_typestring optionalSet 'external' (default) for expert follow-up or 'internal' for local-only review.
severity_filterstring optionalLowest severity to include when reporting issues (critical/high/medium/low/all).
standardsstring optionalCoding standards or style guides to enforce.
temperaturenumber optional0 = deterministic · 1 = creative.
thinking_modestring optionalReasoning depth: minimal, low, medium, high, or max.
use_assistant_modelboolean optionalUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.

This tool is read-only. It does not modify its environment.


Tool: consensus

Builds multi-model consensus through systematic analysis and structured debate. Use for complex decisions, architectural choices, feature proposals, and technology evaluations. Consults multiple models with different stances to synthesize comprehensive recommendations.

ParametersTypeDescription
findingsstringStep 1: your independent analysis for later synthesis (not shared with other models). Steps 2+: summarize the newest model response.
next_step_requiredbooleanTrue if more model consultations remain; set false when ready to synthesize.
stepstringConsensus prompt. Step 1: write the exact proposal/question every model will see (use 'Evaluate…', not meta commentary). Steps 2+: capture internal notes about the latest model response—these notes are NOT sent to other models.
step_numberintegerCurrent step index (starts at 1). Step 1 is your analysis; steps 2+ handle each model response.
total_stepsintegerTotal steps = number of models consulted plus the final synthesis step.
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
current_model_indexinteger optional0-based index of the next model to consult (managed internally).
imagesarray optionalOptional absolute image paths or base64 references that add helpful visual context.
model_responsesarray optionalInternal log of responses gathered so far.
modelsarray optionalUser-specified roster of models to consult (provide at least two entries). User-specified list of models to consult (provide at least two entries). Each entry may include model, stance (for/against/neutral), and stance_prompt. Each (model, stance) pair must be unique, e.g. [{'model':'gpt5','stance':'for'}, {'model':'pro','stance':'against'}]. When the user names a model, you MUST use that exact value or report the provider error—never swap in another option. Use the listmodels tool for the full roster. Top models: gpt-5.1 (score 100, 400K ctx, thinking, code-gen); gpt-5.1-codex (score 100, 400K ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gpt-5-pro (score 100, 400K ctx, thinking, code-gen); +25 more via listmodels.
relevant_filesarray optionalOptional supporting files that help the consensus analysis. Must be absolute full, non-abbreviated paths.
use_assistant_modelboolean optionalUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.

This tool is read-only. It does not modify its environment.


Tool: debug

Performs systematic debugging and root cause analysis for any type of issue. Use for complex bugs, mysterious errors, performance issues, race conditions, memory leaks, and integration problems. Guides through structured investigation with hypothesis testing and expert analysis.

ParametersTypeDescription
findingsstringDiscoveries: clues, code/log evidence, disproven theories. Be specific. If no bug found, document clearly as valid.
modelstringCurrently in auto model selection mode. CRITICAL: When the user names a model, you MUST use that exact name unless the server rejects it. If no model is provided, you may use the listmodels tool to review options and select an appropriate match. Top models: gpt-5.1 (score 100, 400K ctx, thinking, code-gen); gpt-5.1-codex (score 100, 400K ctx, thinking, code-gen); gemini-2.5-pro (score 100, 1.0M ctx, thinking, code-gen); gemini-3-pro-preview (score 100, 1.0M ctx, thinking, code-gen); gpt-5-pro (score 100, 400K ctx, thinking, code-gen); +25 more via listmodels.
next_step_requiredbooleanTrue if you plan to continue the investigation with another step. False means root cause is known or investigation is complete. IMPORTANT: When continuation_id is provided (continuing a previous conversation), set this to False to immediately proceed with expert analysis.
stepstringInvestigation step. Step 1: State issue+direction. Symptoms misleading; 'no bug' valid. Trace dependencies, verify hypotheses. Use relevant_files for code; this for text only.
step_numberintegerCurrent step index (starts at 1). Build upon previous steps.
total_stepsintegerEstimated total steps needed to complete the investigation. Adjust as new findings emerge. IMPORTANT: When continuation_id is provided (continuing a previous conversation), set this to 1 as we're not starting a new multi-step investigation.
confidencestring optionalYour confidence in the hypothesis: exploring (starting out), low (early idea), medium (some evidence), high (strong evidence), very_high (very strong evidence), almost_certain (nearly confirmed), certain (100% confidence - root cause and fix are both confirmed locally with no need for external validation). WARNING: Do NOT use 'certain' unless the issue can be fully resolved with a fix, use 'very_high' or 'almost_certain' instead when not 100% sure. Using 'certain' means you have ABSOLUTE confidence locally and PREVENTS external model validation.
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
files_checkedarray optionalAll examined files (absolute paths), including ruled-out ones.
hypothesisstring optionalConcrete root cause theory from evidence. Can revise. Valid: 'No bug found - user misunderstanding' or 'Symptoms unrelated to code' if supported.
imagesarray optionalOptional screenshots/visuals clarifying issue (absolute paths).
issues_foundarray optionalIssues identified with severity levels during work
relevant_contextarray optionalMethods/functions identified as involved in the issue
relevant_filesarray optionalFiles directly relevant to issue (absolute paths). Cause, trigger, or manifestation locations.
temperaturenumber optional0 = deterministic · 1 = creative.
thinking_modestring optionalReasoning depth: minimal, low, medium, high, or max.
use_assistant_modelboolean optionalUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.

This tool is read-only. It does not modify its environment.


Tool: docgen

Generates comprehensive code documentation with systematic analysis of functions, classes, and complexity. Use for documentation generation, code analysis, complexity assessment, and API documentation. Analyzes code structure and patterns to create thorough documentation.

ParametersTypeDescription
comments_on_complex_logicbooleanTrue (default) to add inline comments around non-obvious logic.
document_complexitybooleanInclude algorithmic complexity (Big O) analysis when True (default).
document_flowbooleanInclude call flow/dependency notes when True (default).
findingsstringImportant findings, evidence and insights discovered in this step
next_step_requiredbooleanWhether another work step is needed. When false, aim to reduce total_steps to match step_number to avoid mismatch.
num_files_documentedintegerCount of files finished so far. Increment only when a file is fully documented.
stepstringCurrent work step content and findings from your overall work
step_numberintegerCurrent step number in work sequence (starts at 1)
total_files_to_documentintegerTotal files identified in discovery; completion requires matching this count.
total_stepsintegerEstimated total steps needed to complete work
update_existingbooleanTrue (default) to polish inaccurate or outdated docs instead of leaving them untouched.
continuation_idstring optionalUnique thread continuation ID for multi-turn conversations. Works across different tools. ALWAYS reuse the last continuation_id you were given—this preserves full conversation context, files, and findings so the agent can resume seamlessly.
issues_foundarray optionalIssues identified with severity levels during work
relevant_contextarray optionalMethods/functions identified as involved in the issue
relevant_filesarray optionalFiles identified as relevant to issue/goal (FULL absolute paths to real files/folders - DO NOT SHORTEN)
use_assistant_modelboolean optionalUse assistant model for expert analysis after workflow steps. False skips expert analysis, relies solely on your personal investigation. Defaults to True for comprehensive validation.

This tool is read-only. It does not modify its environment.


Tool: listmodels

Shows which AI model providers are configured, available model names, their aliases and capabilities.

Tool: planner

Breaks down complex tasks through interactive, sequential planning with revision and branching capabilities. Use for complex project planning, system design, migration strategies, and architectural decisions. Builds plans incrementally with deep reflection for complex scenarios.

ParametersTypeDescription
modelstringCurrently in auto model selection mode. CRITICAL: When

[...]

查看更多 zen 相关镜像 →
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linuxserver/zen
by linuxserver.io
linuxserver/zen是Zen Browser的Docker镜像,Zen Browser是基于Mozilla Firefox的免费开源浏览器分支,专注于隐私保护、高度可定制性和现代设计,提供便捷的容器化部署方式。
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上次更新:7 天前

用户好评

来自真实用户的反馈,见证轩辕镜像的优质服务

oldzhang的头像

oldzhang

运维工程师

Linux服务器

5

"Docker加速体验非常流畅,大镜像也能快速完成下载。"

用户评价详情

oldzhang - 运维工程师

Linux服务器

5

Docker加速体验非常流畅,大镜像也能快速完成下载。

Nana - 后端开发

Mac桌面

4.9

配置Docker镜像源后,拉取速度快了数倍,开发体验提升明显。

Qiang - 平台研发

K8s集群

5

轩辕镜像在K8s集群中表现很稳定,容器部署速度明显加快。

小敏 - 测试工程师

Windows桌面

4.8

Docker镜像下载不再超时,测试环境搭建更加高效。

晨曦 - DevOps工程师

宝塔面板

5

配置简单,Docker镜像源稳定,适合快速部署环境。

阿峰 - 资深开发

群晖NAS

5

在群晖NAS上配置后,镜像下载速度飞快,非常适合家庭实验环境。

俊仔 - 后端工程师

飞牛NAS

4.9

Docker加速让容器搭建顺畅无比,再也不用等待漫长的下载。

Lily - 测试经理

Linux服务器

4.8

镜像源覆盖面广,更新及时,团队一致反馈体验不错。

浩子 - 云平台工程师

Podman容器

5

使用轩辕镜像后,Podman拉取镜像稳定无比,生产环境可靠。

Kai - 运维主管

爱快路由

5

爱快系统下配置加速服务,Docker镜像拉取速度提升非常大。

翔子 - 安全工程师

Linux服务器

4.9

镜像源稳定性高,安全合规,Docker拉取无忧。

亮哥 - 架构师

K8s containerd

5

大规模K8s集群下镜像加速效果显著,节省了大量时间。

慧慧 - 平台开发

Docker Compose

4.9

配置Compose镜像加速后,整体构建速度更快了。

Tina - 技术支持

Windows桌面

4.8

配置简单,镜像拉取稳定,适合日常开发环境。

宇哥 - DevOps Leader

极空间NAS

5

在极空间NAS上使用Docker加速,体验流畅稳定。

小静 - 数据工程师

Linux服务器

4.9

Docker镜像源下载速度快,大数据环境搭建轻松完成。

磊子 - SRE

宝塔面板

5

使用轩辕镜像后,CI/CD流程整体快了很多,值得推荐。

阿Yang - 前端开发

Mac桌面

4.9

国内网络环境下,Docker加速非常给力,前端环境轻松搭建。

Docker迷 - 架构师

威联通NAS

5

威联通NAS下配置镜像加速后,Docker体验比官方源好很多。

方宇 - 系统工程师

绿联NAS

5

绿联NAS支持加速配置,Docker镜像下载快且稳定。

常见问题

Q1:轩辕镜像免费版与专业版有什么区别?

免费版仅支持 Docker Hub 加速,不承诺可用性和速度;专业版支持更多镜像源,保证可用性和稳定速度,提供优先客服响应。

Q2:轩辕镜像免费版与专业版有分别支持哪些镜像?

免费版仅支持 docker.io;专业版支持 docker.io、gcr.io、ghcr.io、registry.k8s.io、nvcr.io、quay.io、mcr.microsoft.com、docker.elastic.co 等。

Q3:流量耗尽错误提示

当返回 402 Payment Required 错误时,表示流量已耗尽,需要充值流量包以恢复服务。

Q4:410 错误问题

通常由 Docker 版本过低导致,需要升级到 20.x 或更高版本以支持 V2 协议。

Q5:manifest unknown 错误

先检查 Docker 版本,版本过低则升级;版本正常则验证镜像信息是否正确。

Q6:镜像拉取成功后,如何去掉轩辕镜像域名前缀?

使用 docker tag 命令为镜像打上新标签,去掉域名前缀,使镜像名称更简洁。

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轩辕镜像下载加速使用手册

探索更多轩辕镜像的使用方法,找到最适合您系统的配置方式

登录仓库拉取

通过 Docker 登录认证访问私有仓库

Linux

在 Linux 系统配置镜像加速服务

Windows/Mac

在 Docker Desktop 配置镜像加速

Docker Compose

Docker Compose 项目配置加速

K8s Containerd

Kubernetes 集群配置 Containerd

宝塔面板

在宝塔面板一键配置镜像加速

群晖

Synology 群晖 NAS 配置加速

飞牛

飞牛 fnOS 系统配置镜像加速

极空间

极空间 NAS 系统配置加速服务

爱快路由

爱快 iKuai 路由系统配置加速

绿联

绿联 NAS 系统配置镜像加速

威联通

QNAP 威联通 NAS 配置加速

Podman

Podman 容器引擎配置加速

Singularity/Apptainer

HPC 科学计算容器配置加速

其他仓库配置

ghcr、Quay、nvcr 等镜像仓库

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