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Agent Skill · 前端设计

typegpu(typegpu 命令行)

typegpu 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

typegpu 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 2,839。

总安装 1,842周安装 76GitHub Stars 151下载 602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:typegpu(typegpu 命令行)
来源仓库:https://github.com/software-mansion-labs/skills
仓库路径:skills/typegpu
安装命令:
npx skills add https://github.com/software-mansion-labs/skills --skill typegpu
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/software-mansion-labs/skills --skill typegpu

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,辅助项目协作管理。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕代码变更和协作事项进行整理。

通过 GitHub 安装,使用 npx skills add 命令从 software-mansion-labs/skills 仓库添加技能。安装前需确认权限范围和维护状态,避免触发不必要的联网或文件操作。typegpu 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/typegpu

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/software-mansion-labs/skills --skill typegpu;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

    它需要需要联网,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

typegpu 怎么安装?

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

typegpu 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。

typegpu 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

typegpu 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。

typegpu 用的人多吗?

公开数据是累计安装 1,842 次,近一周安装 76 次,下载 602 次,GitHub 151 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。

typegpu 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 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。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 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。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
typegpu

typegpu 命令行 · 本页

1,842151需要联网未明确通过

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

TypeGPU

A single schema (d.*) defines a GPU type, CPU buffer layout, and TypeScript type at once - no manual alignment, type mapping, or casting. The build plugin unplugin-typegpu transforms 'use gpu'-marked TypeScript for runtime WGSL transpilation, enabling type inference and polymorphism across the CPU/GPU boundary.

This skill targets TypeGPU 0.11.2. If the user's project is on an older release, verify API availability before relying on examples or recommended patterns here.


When to read reference files

Read before writing virtually any shader or GPU function — these two cover the rules that trip people up most:

  • references/types.md — abstract type resolution, exactly when d.f32() is required vs redundant, sampler/texture schemas for tgpu.fn signatures, CPU-side TgpuBuffer/TgpuTexture TypeScript types. If you skip this, you'll hit type errors.
  • references/shaders.md — full std library listing, loops (std.range, tgpu.unroll), tgpu.comptime, outer-scope capture rules, complete builtin reference for all three shader stages, console.log. Read this for any non-trivial shader logic.

Read when the task specifically involves:

  • references/pipelines.md — vertex buffers/layouts, attribs wiring, MRT, fullscreen triangle, depth/stencil, blend modes, fragDepth output, loading 3D models (@loaders.gl), resolve API
  • references/matrices.md — wgpu-matrix integration, column-major layout, camera uniforms, common.writeSoA, fast-path CPU writes. Read for any 3D work (view/projection matrices, animated transforms, model loading)
  • references/textures.md — texture creation, views, samplers, storage textures, mipmaps, multisampling
  • references/noise.md — @typegpu/noise (random, distributions, Perlin 2D/3D)
  • references/sdf.md — @typegpu/sdf (2D/3D primitives, operators, ray marching, AA masking)
  • references/setup.md — install, unplugin-typegpu build plugin, tsover operator overloading
  • references/advanced.md — buffer reinterpretation, indirect drawing/dispatch, custom encoders

Setup

import tgpu, { d, std, common } from 'typegpu';

const root = await tgpu.init();             // request a GPU device
const root = tgpu.initFromDevice(device);   // or wrap an existing GPUDevice

const context = root.configureContext({ canvas, alphaMode: 'premultiplied' });

Create one root at app startup. Resources from different roots cannot interact.


Data schemas (d.*)

A schema defines memory layout and infers TypeScript types; the same schema is used for buffers, shader signatures, and bind group entries.

Scalars

d.f32    d.i32    d.u32    d.f16
// d.bool is NOT host-shareable - use d.u32 in buffers

Vectors and matrices

d.vec2f  d.vec3f  d.vec4f     // f32
d.vec2i  d.vec3i  d.vec4i     // i32
d.vec2u  d.vec3u  d.vec4u     // u32
d.vec2h  d.vec3h  d.vec4h     // f16

d.mat2x2f   d.mat3x3f   d.mat4x4f

Instance types: d.vec3f() -> d.v3f, d.mat4x4f() -> d.m4x4f.

Vector constructors are richly overloaded - use them. They compose from any mix of scalars and smaller vectors that adds up to the right component count:

d.vec3f()              // zero-init: (0, 0, 0)
d.vec3f(1)             // broadcast:  (1, 1, 1)
d.vec3f(1, 2, 3)       // individual components
d.vec3f(someVec2, 1)   // vec2 + scalar
d.vec3f(1, someVec2)   // scalar + vec2

d.vec4f()              // zero-init: (0, 0, 0, 0)
d.vec4f(0.5)           // broadcast:  (0.5, 0.5, 0.5, 0.5)
d.vec4f(rgb, 1)        // vec3 + scalar (common: color + alpha)
d.vec4f(v2a, v2b)      // two vec2s
d.vec4f(1, uv, 0)      // scalar + vec2 + scalar

Swizzles (.xy, .zw, .rgb, .ba, etc.) return vector instances that work as constructor arguments: d.vec4f(pos.xy, vel.zw).

Prefer these overloads over manual component decomposition. Instead of d.vec3f(v.x, v.y, newZ), write d.vec3f(v.xy, newZ).

Compound types

const Particle = d.struct({
  position: d.vec2f,
  velocity: d.vec2f,
  color:    d.vec4f,
});

const ParticleArray = d.arrayOf(Particle, 1000); // fixed-size

Runtime-sized schemas. d.arrayOf(Element) without a count returns a *function* (n: number) => WgslArray<Element>. This dual nature is the key: pass the function itself (unsized) to bind group layouts, call it with a count (sized) for buffer creation.

// Plain array - arrayOf without count is already a factory:
const layout = tgpu.bindGroupLayout({
  data: { storage: d.arrayOf(d.f32), access: 'mutable' },  // unsized for layout
});
const buf = root.createBuffer(d.arrayOf(d.f32, 1024)).$usage('storage'); // sized for buffer

// Struct with a runtime-sized last field - wrap in a factory function:
const RuntimeStruct = (n: number) =>
  d.struct({
    counter: d.atomic(d.u32),
    items:   d.arrayOf(d.f32, n),  // last field gets the runtime size
  });

const layout2 = tgpu.bindGroupLayout({
  runtimeData: { storage: RuntimeStruct, access: 'mutable' }, // unsized (the function)
});
const buf2 = root.createBuffer(RuntimeStruct(1024)).$usage('storage'); // sized (called)

You cannot pass an unsized schema directly to createBuffer - size must be known on the CPU.


GPU functions

TypeGPU compiles TypeScript marked with 'use gpu' into WGSL.

Plain callback (polymorphic)

No explicit signature; best for helper math and flexible utilities.

const rotate = (v: d.v2f, angle: number) => {
  'use gpu';
  const c = std.cos(angle);
  const s = std.sin(angle);
  return d.vec2f(c * v.x - s * v.y, s * v.x + c * v.y);
};

number parameters and unions like d.v2f | d.v3f are polymorphic - TypeGPU generates one WGSL overload per unique call-site type combination. Values captured from outer scope are inlined as WGSL literals; use buffers/uniforms for anything that changes at runtime.

tgpu.fn (explicit types)

Pinned WGSL signature. Use for library code or when you need a fixed WGSL interface.

const rotate = tgpu.fn([d.vec2f, d.f32], d.vec2f)((v, angle) => {
  'use gpu';
  // ...
});

Shader entrypoints

// Compute
const myCompute = tgpu.computeFn({
  workgroupSize: [64],
  in: { gid: d.builtin.globalInvocationId },
})((input) => { 'use gpu'; /* input.gid: d.v3u */ });

// Vertex
const myVertex = tgpu.vertexFn({
  in:  { position: d.vec3f, uv: d.vec2f },
  out: { position: d.builtin.position, fragUv: d.vec2f },
})((input) => {
  'use gpu';
  return { position: d.vec4f(input.position, 1), fragUv: input.uv };
});

// Fragment
const myFragment = tgpu.fragmentFn({
  in: { fragUv: d.vec2f },
  out: d.vec4f,
})((input) => { 'use gpu'; return d.vec4f(input.fragUv, 0, 1); });

Vertex in may include builtins: d.builtin.vertexIndex, d.builtin.instanceIndex.

Full shader syntax, branch pruning, the std library, and type inference: see references/shaders.md.


Values vs references — the most common source of ResolutionError. See references/shaders.md.

Idiomatic patterns (vector ops, struct constructors, register pressure): see references/shaders.md.


Buffers

Creating

// Schema only:
const buf = root.createBuffer(d.arrayOf(Particle, 1000)).$usage('storage');

// With typed initial value (only when non-zero — all buffers are zero-initialized by default):
const uBuf = root.createBuffer(Config, { time: 1, scale: 2.0 }).$usage('uniform');

// With an initializer callback - buffer is still mapped (cheapest CPU path):
const buf = root.createBuffer(Schema, (mappedBuffer) => {
  mappedBuffer.write([10, 20], { startOffset: firstChunk.offset });
  mappedBuffer.write([30, 40], { startOffset: secondChunk.offset });
});

// Wrap an existing GPUBuffer (you own its lifecycle and flags):
const buf = root.createBuffer(d.u32, existingGPUBuffer);
buf.write(12);

Usage flags

LiteralShader access
'uniform'var<uniform>
'storage'var<storage, read> (or read_write with access: 'mutable')
'vertex'vertex input, paired with tgpu.vertexLayout
'index'index buffer (d.u16 or d.u32 schema only)
'indirect'indirect dispatch/draw

All buffers get COPY_SRC | COPY_DST automatically. $addFlags(GPUBufferUsage.X) adds any flag not covered by $usage.

Writing

.write(value) handles alignment. Four input forms (slowest → fastest):

FormExample (vec3f)Notes
Typed instanced.vec3f(1, 2, 3)Allocates a wrapper — fine for setup/prototypes
Plain JS array / tuple[1, 2, 3]No allocation, padding added automatically
TypedArraynew Float32Array([1, 2, 3])Bytes copied verbatim — must include WGSL padding
ArrayBufferrawBytesMaximum throughput, bytes copied verbatim

Cache plain arrays or Float32Array at setup and reuse. For the padding rules (vec3f = 16 bytes, mat3x3f per-column padding) and full fast-path guidance, see references/matrices.md.

Slice write - update a sub-region using d.memoryLayoutOf to get byte offsets:

const layout = d.memoryLayoutOf(schema, (a) => a[3]);
buffer.write([4, 5, 6], { startOffset: layout.offset });

.patch(data) - update specific struct fields or array indices without touching the rest:

planetBuffer.patch({
  mass: 123.1,
  colors: { 2: [1, 0, 0], 4: d.vec3f(0, 0, 1) },
});

common.writeSoA(buffer, {field: Float32Array,...}) - scatter separate packed per-field arrays into the GPU's AoS layout with correct padding. The idiomatic path for particle systems, simulations, and model loading where CPU data is already field-separated. See references/matrices.md for examples and references/pipelines.md for the model-loading pattern.

GPU-side copy: destBuffer.copyFrom(srcBuffer) (schemas must match).

Reading

const data = await buffer.read(); // returns a typed JS value matching the schema

Shorthand "fixed" resources

Skip manual bind groups - the buffer is always bound when referenced in any shader:

const particlesMutable = root.createMutable(d.arrayOf(Particle, 1000));  // var<storage, read_write>
const configUniform    = root.createUniform(Config);                     // var<uniform>
const bufReadonly      = root.createReadonly(d.arrayOf(d.f32, N));       // var<storage, read>

Access inside shaders via particles.$, config.$. Prefer fixed resources by default; switch to manual bind groups when you need to swap resources per frame, manage @group indices, or share layouts across pipelines.


Bind group layouts (manual binding)

const layout = tgpu.bindGroupLayout({
  config:    { uniform: ConfigSchema },
  particles: { storage: d.arrayOf(Particle), access: 'mutable' },
  mySampler: { sampler: 'filtering' },   // 'filtering' | 'non-filtering' | 'comparison'
  myTexture: { texture: d.texture2d(d.f32) },
});

// Inside shaders: layout.$.config, layout.$.particles, ...

const bindGroup = root.createBindGroup(layout, {
  config:    configBuffer,
  particles: particleBuffer,
  mySampler: tgpuSampler,
  myTexture: textureOrView,
});

pipeline.with(bindGroup).dispatchWorkgroups(N);

Explicit @group index (only needed when integrating with raw WGSL that hardcodes group indices): layout.$idx(0).


Compute pipelines

// Standard - you control workgroup sizing
const pipeline = root.createComputePipeline({ compute: myComputeFn });
pipeline.with(bindGroup).dispatchWorkgroups(Math.ceil(N / 64));

// Guarded - TypeGPU handles workgroup sizing and bounds checking automatically.
// The callback's parameter count sets the dimensionality (0D to 3D):
const p0 = root.createGuardedComputePipeline(() => { 'use gpu'; /* runs once */ });
const p1 = root.createGuardedComputePipeline((x: number) => { 'use gpu'; });
const p2 = root.createGuardedComputePipeline((x: number, y: number) => { 'use gpu'; });
const p3 = root.createGuardedComputePipeline((x: number, y: number, z: number) => { 'use gpu'; });

// dispatchThreads matches the callback's arity - pass thread counts, not workgroup counts.
// TypeGPU picks workgroup sizes internally and injects a bounds guard so threads
// outside the requested range are no-ops.
p2.with(bindGroup).dispatchThreads(width, height);

// WGSL builtins like globalInvocationId are NOT available - use the callback parameters instead.

WebGPU coordinate conventions

WebGPU matches DirectX/Metal, not OpenGL/WebGL — porting tutorials verbatim causes subtle bugs:

  • NDC z: [0, 1], not [-1, 1]. A copy-pasted gluPerspective clips the near plane. Use wgpu-matrix's mat4.perspective (already targets [0, 1]), or mat4.perspectiveReverseZ for better depth precision.
  • Framebuffer (0, 0) is top-left, +y down — opposite of OpenGL. d.builtin.position.xy in a fragment shader is pixel-space with this origin.
  • Texture UV (0, 0) is top-left. Do *not* pre-flip v — createImageBitmap already matches this.
  • Matrices are column-major: d.mat4x4f(c0, c1, c2, c3) takes columns. Inside shaders use mat.columns[c][r]; plain mat[i] is rejected. Composition: projection * view * model * position. See references/matrices.md.

Render pipelines

const pipeline = root.createRenderPipeline({
  vertex:   myVertex,
  fragment: myFragment,
  targets:  { format: presentationFormat }, // single target - shorthand
  primitive?:    GPUPrimitiveState,
  depthStencil?: GPUDepthStencilState,
  multisample?:  GPUMultisampleState,
});

pipeline
  .with(bindGroup)
  .withColorAttachment({
    view: context,
    // loadOp/storeOp/clearValue have defaults
  })
  .withDepthStencilAttachment({ /* ... */ })
  .withIndexBuffer(indexBuffer)  // enables .drawIndexed()
  .draw(vertexCount, instanceCount /* optional */);

Shell-less inline vertex/fragment lambdas are also valid for simple cases.

Multiple render targets (MRT)

Use a named record for fragment out, pipeline targets, and withColorAttachment — TypeScript enforces matching keys. Keys become WGSL struct field names verbatim; no $-prefixes. Builtins (fragDepth) go in out but do not appear in targets or withColorAttachment.

Full MRT example, per-target blend/writeMask config, and the fragDepth footgun: see references/pipelines.md.

Cache bind groups and views

root.createBindGroup(...) and texture.createView(...) allocate fresh GPU objects each call. Fine for prototypes; for anything you care about, create them once at setup (near the resource they wrap), store handles in consts, and reuse. Per-frame allocation isn't slow per se, but it raises GC pressure and introduces stutters. When a view or bind group legitimately varies each frame, cache the small set you cycle through.

For vertex buffer layouts, the attribs spread trick, and the common.fullScreenTriangle helper: references/pipelines.md.


GPU-scoped variables

tgpu.workgroupVar(schema) — shared across all threads in a workgroup (compute only). tgpu.privateVar(schema) — thread-private. tgpu.const(schema, value) — compile-time constant embedded as a WGSL literal. Access all via .$. Full examples in references/shaders.md.


Slots

tgpu.slot<T>() is a typed placeholder; fill with .with(slot, value) at pipeline, root, or function scope. Any type fits: GPU values, functions, callbacks. Slots are the idiomatic way to build configurable/reusable shaders.

const distFnSlot = tgpu.slot<(pos: d.v3f) => number>();

const rayMarcher = tgpu.computeFn({
  workgroupSize: [64],
  in: { gid: d.builtin.globalInvocationId },
})(({ gid }) => {
  'use gpu';
  const dist = distFnSlot.$(d.vec3f(gid)); // call the injected function
});

root
  .with(distFnSlot, (pos) => {
    'use gpu';
    return std.length(pos - d.vec3f(0, 0, -5)) - 1.0; // sphere SDF
  })
  .createComputePipeline({ compute: rayMarcher });

Scalar/vector slot with a default:

const colorSlot = tgpu.slot(d.vec4f(1, 0, 0, 1));
pipeline.with(colorSlot, d.vec4f(0, 1, 0, 1)).draw(3);

Accessors

tgpu.accessor(schema, initial?) is schema-aware - the value can be a buffer binding, a constant, a literal, or a 'use gpu' function returning one. The shader is agnostic about how the value is sourced. If they can be cleanly used, they should be preferred over slots.

const colorAccess = tgpu.accessor(d.vec3f);

// Fill with a uniform buffer:
root.with(colorAccess, colorUniform).createComputePipeline(...)

// Fill with a literal (inlined):
root.with(colorAccess, d.vec3f(1, 0, 0)).createComputePipeline(...)

// Fill with a GPU function:
root.with(colorAccess, () => { 'use gpu'; return computeColor(); }).createComputePipeline(...)

Write access: tgpu.mutableAccessor(schema, initial?).


Type utilities

d.InferInput<typeof Schema> — CPU-side type accepted by .write(). d.InferGPU<typeof Schema> — type inside 'use gpu' functions. AnyData (from 'typegpu') — broadest schema constraint for generics. Full buffer/texture TypeScript types (TgpuBuffer, TgpuUniform, TgpuTexture, usage flags): references/types.md.


Common pitfalls

  1. Numeric literals: 1.0 may strip -> abstractInt. Use d.f32(1). See types.md.
  2. Outer-scope captures are constants: not runtime-mutable. Use createUniform/createMutable. See shaders.md.
  3. TypedArray/ArrayBuffer alignment: bytes copied verbatim. vec3f elements are 16 bytes (12 + 4 padding). Plain arrays handle padding; typed arrays must include it.
  4. Integer division: a / b on primitives is f32. Use d.i32()/d.u32() for integer semantics. See types.md.
  5. Uninitialised variables: let x; is invalid - always initialise so the type can be inferred: let x = d.f32(0).
  6. Ternary operators: runtime ternaries aren't supported. Use std.select(falseVal, trueVal, condition).
  7. Fragment output is always d.vec4f, even for fewer-channel formats. A pipeline with targets: {format: 'r8unorm'} or 'rg16float' still requires out: d.vec4f and return d.vec4f(...). WebGPU drops the unused channels.

Companion packages

  • @typegpu/noise - real PRNG (randf), distributions (uniform, normal, hemisphere,...), and Perlin noise (perlin2d/perlin3d) with optional precomputed gradient caches (~10x speedups). Prefer over hand-rolled hashes. See references/noise.md.
  • @typegpu/sdf - 2D/3D signed distance primitives (sdDisk, sdBox2d, sdRoundedBox2d, sdBezier, sdSphere, sdBox3d, sdCapsule, sdPlane,...) and operators (opUnion, opSmoothUnion, opSmoothDifference, opExtrudeX/Y/Z). All tgpu.fn with pinned types, callable directly from 'use gpu'. For ray marching, UI masking, AA vector drawing. See references/sdf.md.
  • wgpu-matrix - canonical math library for TypeGPU. TypeGPU vectors/matrices can be passed as dst to wgpu-matrix calls to avoid allocations. See references/matrices.md for full integration patterns.

平台分布

Codex

35.07%
按下载量换算211

Claude

31.93%
按下载量换算192

Cursor

18.51%
按下载量换算111

Gemini CLI

9.13%
按下载量换算55

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

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