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Agent Skill · 研究检索

reference-engine(参考引擎)

reference-engine 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

reference-engine 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为接触敏感数据;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 66,724。

总安装 259周安装 11GitHub Stars 24下载 91核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reference-engine(参考引擎)
来源仓库:https://github.com/noobygains/godmode
仓库路径:skills/reference-engine
安装命令:
npx skills add https://github.com/noobygains/godmode --skill reference-engine
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/noobygains/godmode --skill reference-engine

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

reference-engine 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。适用于研究检索类任务,如信息搜集、资料筛选和知识整理,尤其适合多源数据聚合与初步分析。

通过关键词、任务描述或来源仓库提供查询条件,返回结构化候选结果列表供进一步处理。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。可结合来源仓库、安装命令和原始 README 继续核验具体用法。

来源仓库内路径:skills/reference-engine

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/noobygains/godmode --skill reference-engine;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

reference-engine 怎么安装?

在终端执行 npx skills add https://github.com/noobygains/godmode --skill reference-engine。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

reference-engine 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

reference-engine 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

reference-engine 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

reference-engine 用的人多吗?

公开数据是累计安装 259 次,近一周安装 11 次,下载 91 次,GitHub 24 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

reference-engine 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
reference-engine

参考引擎 · 本页

25924接触敏感数据未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

YOU MUST LOCATE A REFERENCE BEFORE YOU BUILD. NO EXCEPTIONS.

Reference Engine: The Universal Reference-First System

Overview

Every domain has thousands of hours of professional engineering already completed. APIs have been designed, schemas have been refined, pipelines have been battle-tested, patterns have been hardened. Leveraging them as your reference is not laziness — it is engineering intelligence.

Core principle: Before building ANYTHING, locate the best existing reference. Extract its patterns. Build on proven foundations, not assumptions.

No exceptions. No workarounds. No shortcuts.

The Prime Directive

NO BUILDING WITHOUT A REFERENCE

If you have not searched for proven references in the domain you are working in, you are disregarding thousands of hours of professional engineering. A payment API should resemble Stripe's API patterns, not a generic AI-generated endpoint.

When to Use

Always. This protocol is the router. It determines WHICH reference system to engage based on what is being built.

digraph reference_router {
    rankdir=TB;
    node [shape=box style=filled];

    task [label="Task Received" fillcolor=lightyellow shape=doublecircle];

    classify [label="Classify Task Domain" fillcolor=lightyellow];

    ui [label="UI / Frontend?\n-> ux-patterns\n-> ui-engineering\n-> design-integration" fillcolor="#e8f5e9"];
    website [label="Website Design?\n-> design-research\n-> ux-patterns" fillcolor="#e8f5e9"];
    code [label="Code / Library?\n-> github-search (external)\n-> codebase-research (internal)\n-> pattern-matching" fillcolor="#e8f5e9"];
    api [label="API Design?\n-> API References\n(this protocol)" fillcolor="#fff3e0"];
    database [label="Database / Schema?\n-> Schema References\n(this protocol)" fillcolor="#fff3e0"];
    testing [label="Testing Strategy?\n-> Testing References\n(this protocol)" fillcolor="#fff3e0"];
    devops [label="CI/CD / DevOps?\n-> DevOps References\n(this protocol)" fillcolor="#fff3e0"];
    arch [label="Architecture?\n-> system-design\n-> Architecture References\n(this protocol)" fillcolor="#fff3e0"];
    quality [label="Code Quality?\n-> quality-enforcement\n-> Quality References\n(this protocol)" fillcolor="#fff3e0"];
    perf [label="Performance?\n-> performance-tuning\n-> Perf References\n(this protocol)" fillcolor="#fff3e0"];
    security [label="Security?\n-> security-protocol\n-> Security References\n(this protocol)" fillcolor="#fff3e0"];
    other [label="Other Domain?\n-> Research + Build Reference\n(this protocol)" fillcolor="#fce4ec"];

    task -> classify;
    classify -> ui;
    classify -> website;
    classify -> code;
    classify -> api;
    classify -> database;
    classify -> testing;
    classify -> devops;
    classify -> arch;
    classify -> quality;
    classify -> perf;
    classify -> security;
    classify -> other;
}

Green nodes = dedicated protocol exists, invoke it. Orange nodes = use reference libraries from THIS protocol. Red nodes = no reference exists yet — research first, then build.

The Entry Protocol

BEFORE building anything:

1. CLASSIFY: What domain is this task in?
2. ROUTE: Does a dedicated reference protocol exist? (ux-patterns, design-research, github-search, codebase-research, etc.)
   -> YES: Invoke that protocol
   -> NO: Continue to step 3
3. SEARCH: Locate reference implementations for this domain
   - External: Use github-search for open-source repos, libraries, and patterns
   - Internal: Use codebase-research for existing conventions and similar code
   - GitHub: Search for gold-standard implementations
   - Documentation: Find official best practices (RFC specs, framework docs, cloud provider guides)
   - Industry leaders: What do Stripe, GitHub, Vercel, AWS do for this?
4. EXTRACT: Isolate the patterns that make these references excellent
5. PRESENT: Show the user your references and recommended approach
6. BUILD: Implement using the reference

Skip any step = building from assumptions instead of knowledge

The Reference Philosophy

"Accumulated Expertise" Principle:

Every professional implementation represents:
- Months of design iteration
- Thousands of users providing feedback
- Production incidents that drove improvements
- Security audits that uncovered vulnerabilities
- Performance tuning under real load

When you generate from scratch, you inherit NONE of this.
When you use a reference, you inherit ALL of it.

Domain Reference Libraries

API Design References

Gold-standard implementations to study:

API StyleReferenceStudy For
RESTStripe APIResource naming, versioning, error format, pagination, idempotency
RESTGitHub API v3Hypermedia, conditional requests, rate limiting headers
RESTTwilio APINested resources, webhooks, status callbacks
GraphQLGitHub API v4Schema design, pagination (connections), error handling
GraphQLShopify StorefrontQuery complexity limits, versioning strategy
RPC/gRPCGoogle Cloud APIsProto design, error model, long-running operations
WebhooksStripe WebhooksEvent types, signing, retry policy, idempotency
Real-timeDiscord GatewayWebSocket lifecycle, heartbeats, reconnection, intents

API Reference Checklist:

BEFORE designing any API:

1. Resource naming: Use nouns, plural, lowercase
   Reference: Stripe -> /v1/customers, /v1/payment_intents

2. Error format: Consistent error object
   Reference: Stripe -> { error: { type, code, message, param } }

3. Pagination: Cursor-based for real-time data, offset for static
   Reference: GitHub -> Link headers + per_page + page params
   Reference: Stripe -> has_more + starting_after cursor

4. Versioning: URL path or header
   Reference: Stripe -> /v1/ prefix
   Reference: GitHub -> Accept header with version

5. Auth: API keys for server, OAuth for users
   Reference: Stripe -> Bearer token in Authorization header

6. Rate limiting: Return limits in headers
   Reference: GitHub -> X-RateLimit-Limit, X-RateLimit-Remaining, X-RateLimit-Reset

7. Idempotency: Idempotency keys for mutations
   Reference: Stripe -> Idempotency-Key header

8. Filtering/sorting: Consistent query parameter patterns
   Reference: Stripe -> created[gte]=timestamp, status=active

Database Schema References

Reference patterns by domain:

DomainSchema PatternSource
Users & AuthUsers -> Roles -> Permissions (RBAC)Auth0, Supabase auth schema
E-commerceProducts -> Variants -> Orders -> LineItemsShopify schema, Medusa.js
Multi-tenant SaaSOrganizations -> Members -> ResourcesClerk, WorkOS patterns
CMSContent -> Versions -> Media -> TaxonomiesStrapi, Payload CMS schema
SocialUsers -> Posts -> Comments -> Reactions -> FollowsMastodon, Lemmy schema
MessagingConversations -> Participants -> MessagesMatrix protocol, Slack data model
SchedulingEvents -> Slots -> Bookings -> AvailabilityCal.com schema
AnalyticsEvents -> Sessions -> Properties (star schema)PostHog, Plausible schema
InventoryProducts -> Warehouses -> Stock -> MovementsOdoo inventory module

Schema Reference Checklist:

BEFORE designing any database schema:

1. Find the domain pattern above (or search GitHub for "[domain] database schema")
2. Study the reference implementation's:
   - Table relationships and foreign keys
   - Indexing strategy
   - Soft delete approach (deleted_at vs status)
   - Audit trail pattern (created_at, updated_at, created_by)
   - Multi-tenancy approach (row-level vs schema-level)
3. Standard columns for EVERY table:
   - id (UUID or ULID, not auto-increment for distributed systems)
   - created_at (timestamp with timezone)
   - updated_at (timestamp with timezone)
4. Naming convention: snake_case for tables and columns
5. Junction tables: {table_a}_{table_b} alphabetically

Testing Strategy References

Reference frameworks by project type:

Project TypeTesting ApproachTools
React/Vue/SvelteComponent -> Integration -> E2ETesting Library + Vitest + Playwright
API/BackendUnit -> Integration -> Contract -> E2EJest/Vitest + Supertest + Pact
CLI ToolUnit -> Integration -> SnapshotJest + mock-stdin + snapshot testing
Library/PackageUnit -> Property-based -> CompatibilityVitest + fast-check + matrix CI
MobileComponent -> Screen -> E2EDetox (RN), XCTest (iOS), Espresso (Android)
Data PipelineUnit -> Integration -> Data qualityGreat Expectations, dbt tests
InfrastructurePlan -> Apply -> VerifyTerratest, kitchen-terraform

Testing Reference Checklist:

BEFORE writing tests:

1. Identify project type -> select testing approach above
2. Test pyramid for this project:
   - Unit tests: 70% (fast, isolated, mock dependencies)
   - Integration tests: 20% (real dependencies, test interactions)
   - E2E tests: 10% (user flows, critical paths only)
3. What to test:
   - Happy path (minimum viable test)
   - Edge cases from spec (empty, null, max, concurrent)
   - Error paths (invalid input, network failure, timeout)
   - Security paths (injection, auth bypass, privilege escalation)
4. What NOT to test:
   - Framework internals (React renders correctly)
   - Third-party library behavior (axios sends requests)
   - Implementation details (internal state shape)
5. Test naming: describe("[unit]", () => it("should [behavior] when [condition]"))
6. Test data: Use factories/fixtures, not inline magic values

CI/CD Pipeline References

Reference pipelines by platform:

PlatformSourceKey Patterns
GitHub Actionsgithub/starter-workflowsMatrix builds, caching, artifact upload
GitHub ActionsVercel's Next.js workflowPreview deploys, environment protection
GitLab CIgitlab-org/gitlabMulti-stage, DAG pipelines, includes
CircleCIcircleci/circleci-docsOrbs, workspace persistence
AWSaws-actions/*OIDC auth, CodeBuild, ECS deploy
GCPgoogle-github-actions/*Workload Identity, Cloud Run deploy

CI/CD Reference Checklist:

BEFORE setting up CI/CD:

1. Standard pipeline stages:
   Install -> Lint -> Type Check -> Test -> Build -> Deploy

2. Caching strategy:
   - Node: cache node_modules with package-lock.json hash
   - Python: cache .venv with requirements.txt hash
   - Go: cache go/pkg/mod with go.sum hash
   - Rust: cache target/ with Cargo.lock hash

3. Required checks before merge:
   - All tests pass
   - Linting passes
   - Type checking passes
   - Build succeeds
   - Security audit passes (npm audit, pip audit)

4. Deployment strategy:
   - Preview deploys for PRs (Vercel, Netlify, or custom)
   - Staging auto-deploy from main
   - Production manual approval or tag-based

5. Secrets management:
   - Use platform secret stores (GitHub Secrets, Vault)
   - Never echo secrets in logs
   - Rotate on compromise

Code Pattern References

Reference implementations by language/framework:

PatternSourceWhen to Use
Error handling (TS)Effect-TS, neverthrowTyped errors, Result pattern
Error handling (Go)Standard libraryerrors.Is/As, wrapping, sentinel errors
Error handling (Rust)thiserror + anyhowCustom error types + context
State machinesXState, RobotComplex UI state, workflows
Event sourcingEventStoreDB examplesAudit trails, temporal queries
CQRSAxon Framework examplesRead/write separation at scale
Repository patternSpring Data, TypeORMData access abstraction
Middleware patternExpress, Koa, HonoRequest pipeline, cross-cutting concerns
Plugin systemVite, ESLint, WebpackExtensibility, hooks
Queue/workerBullMQ, CeleryBackground jobs, async processing
Pub/subRedis Streams, NATSEvent-driven communication
Rate limitingUpstash ratelimitAPI protection, fair usage
Feature flagsUnleash, LaunchDarkly SDKProgressive rollout, A/B testing
CachingRedis patterns, SWRPerformance, stale-while-revalidate

Code Pattern Reference Checklist:

BEFORE implementing a pattern:

1. Identify the pattern needed from the table above
2. Search GitHub for the reference implementation
3. Study HOW it implements the pattern:
   - What's the public API? (how do consumers use it?)
   - What's the internal structure? (how is it organized?)
   - How does it handle errors?
   - How does it handle edge cases?
4. Extract the minimal pattern for your use case
5. Implement following the reference structure

Architecture References

Reference architectures by scale:

ScaleArchitectureSource
Solo/MVPMonolith + managed DBRails, Django, Next.js full-stack
Small teamModular monolithShopify's approach (components), Laravel modules
GrowingMonolith -> extract servicesSegment's centrifuge pattern
ScaleMicroservices + event busNetflix OSS, Uber's domain-oriented
ServerlessFunctions + managed servicesSST (sst.dev) patterns, Vercel's architecture
EdgeEdge compute + CDNCloudflare Workers patterns, Deno Deploy

Security References

Reference implementations by concern:

ConcernSourceKey Patterns
AuthenticationAuth.js (NextAuth)Session strategy, provider pattern, CSRF protection
AuthorizationCASL, CasbinABAC/RBAC policies, permission checking
Input validationZod, ValibotSchema validation at boundaries
Rate limitingUpstash ratelimitSliding window, token bucket
CORSExpress CORS middlewareAllowlist origins, credentials handling
CSPHelmet.jsContent-Security-Policy headers
Secrets1Password CLI, VaultSecret rotation, zero-trust access
Encryptionlibsodium, Web CryptoEnvelope encryption, key derivation

DevOps / Infrastructure References

Reference patterns by provider:

ProviderSourceCovers
AWSaws-samples/*VPC, ECS, Lambda, RDS, S3 patterns
GCPGoogleCloudPlatform/*Cloud Run, GKE, Pub/Sub, Firestore
AzureAzure-Samples/*App Service, Functions, Cosmos DB
Kuberneteskubernetes/examplesDeployments, services, ingress, HPA
Terraformhashicorp/terraform-provider-*Module patterns, state management
Dockerdocker/awesome-composeMulti-service compose patterns
Monitoringgrafana/grafanaDashboard templates, alert rules

Documentation References

Doc TypeSourceStudy For
API docsStripe docsClear examples, language tabs, copy-paste ready
READMEBest-of-breed GitHub READMEsBadges, quick start, feature list, contributing
Architecturearc42, C4 modelDecision records, context diagrams
RunbooksPagerDuty runbooksIncident response, escalation
ChangelogsKeep a ChangelogVersioning, categorization

The Research Process

When no specific reference library above covers your domain:

1. GitHub Search:
   - "[domain] [language] example" (e.g., "payment processing typescript example")
   - "[domain] boilerplate" or "[domain] starter"
   - Sort by stars, filter to recently updated

2. Official Documentation:
   - Framework guides (Next.js docs, Django docs, Rails guides)
   - Cloud provider best practices (AWS Well-Architected, GCP Architecture Center)
   - RFC specifications (for protocols, standards)

3. Industry Leaders:
   - What does Stripe do for payments?
   - What does GitHub do for API design?
   - What does Vercel do for deployment?
   - What does Cloudflare do for edge computing?

4. Open Source Implementations:
   - Search for mature, well-maintained projects in the same domain
   - Examine how they structure their code
   - Cherry-pick patterns from 3+ implementations

Multi-Reference Cherry-Picking

The best results come from combining references from multiple sources:

Example: Building a SaaS billing system

Reference 1 (Stripe API patterns):
  -> Take: Resource naming, error format, idempotency
  -> Take: Webhook event structure and signing

Reference 2 (Lago open-source billing):
  -> Take: Usage-based metering data model
  -> Take: Invoice generation pipeline

Reference 3 (Supabase auth schema):
  -> Take: Multi-tenant organization structure
  -> Take: Row-level security patterns

Reference 4 (Cal.com):
  -> Take: Subscription lifecycle state machine
  -> Take: Webhook delivery with retry logic

Result: A billing system built on patterns from 4 production-tested systems,
each designed by teams who spent months on exactly these problems.

Reference Quality Criteria

Not all references are equal. Evaluate by:

CriterionWeightWhat to Check
Production usageHighIs this deployed in production by real organizations?
Community sizeHighStars, contributors, download counts
MaintenanceHighRecent commits, responsive issue handling
DocumentationMediumAre patterns documented and explained?
Test coverageMediumDoes the reference have strong tests?
Security auditedMediumHas it passed security review?
SimplicityMediumIs the pattern minimal and clear?
PortabilityLowCan the pattern be adapted to other stacks?

Cognitive Traps

RationalizationTruth
"I know how to build this"You know how to build A version. References give you the BEST version.
"This is too simple for a reference"Simple things done wrong compound. A bad schema pattern affects every query forever.
"I'll consult references later"Research FIRST. Structural decisions made early are hardest to reverse.
"The user didn't request research"They requested quality. References ARE how you deliver quality.
"There's no reference for this"There is always a reference. Adjacent domains, similar patterns, analogous systems.
"References slow me down"Building the wrong thing slows you down MORE.
"I can improve on the reference"Prove it. Show the reference first, then propose improvements.
"AI can generate strong patterns"AI generates plausible patterns. Plausible does not mean production-tested.

Guardrails

Prohibited:

  • Generating API designs without studying Stripe/GitHub/Twilio patterns
  • Creating database schemas without locating domain-specific references
  • Setting up CI/CD without examining starter workflows
  • Implementing security without studying auth library patterns
  • Writing tests without a testing strategy reference
  • Choosing architecture without studying reference architectures

Mandatory:

  • Locate at least 2 reference implementations before building
  • Cherry-pick from 3+ sources for complex systems
  • Present your references and approach to the user
  • Explain WHY you chose specific patterns from specific references
  • Update references when you discover superior ones

Integration

This protocol is the ROUTER. It invokes other protocols:

  • godmode:ux-patterns — UI/UX references
  • godmode:design-research — Website design references
  • godmode:github-search — External code and library research (GitHub, package registries, open-source ecosystems)
  • godmode:codebase-research — Internal codebase pattern matching (conventions, similar files, existing implementations)
  • godmode:system-design — Architecture decision references
  • godmode:quality-enforcement — Quality standard references
  • godmode:security-protocol — Security pattern references
  • godmode:performance-tuning — Performance references
  • godmode:project-bootstrap — Project structure references

Invoked by:

  • godmode:intent-discovery — During the "what exists?" phase
  • godmode:specification-first — To inform specs with proven patterns
  • godmode:task-planning — To ground plans in reality

The hierarchy:

reference-engine (this protocol - the universal router)
+-- ux-patterns (UI/UX domain)
+-- design-research (website design domain)
+-- github-search (external code research domain)
+-- codebase-research (internal code pattern domain)
+-- system-design (structural design domain)
+-- quality-enforcement (quality domain)
+-- security-protocol (security domain)
+-- performance-tuning (performance domain)
+-- project-bootstrap (structure domain)
+-- [this protocol's built-in libraries] (API, DB, testing, CI/CD, DevOps, docs)

平台分布

Codex

35.87%
按下载量换算33

Claude

31.11%
按下载量换算28

Cursor

19.92%
按下载量换算18

Gemini CLI

10.32%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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