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kv-store-grpcKV store gRPC 命令行

Agent Skill

kv-store-grpc 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。

总安装

808

周安装

33

GitHub Stars

93

下载量

261
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

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:kv-store-grpc(KV store gRPC 命令行)
来源仓库:https://github.com/letta-ai/skills
仓库路径:skills/kv-store-grpc
安装命令:
npx skills add https://github.com/letta-ai/skills --skill kv-store-grpc
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/letta-ai/skills --skill kv-store-grpc

简介

kv-store-grpc 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合围绕仓库状态和协作事项进行整理。

  • 适用场景包括代码审查、项目进度跟踪、协作流程管理和仓库元数据分析等开发管理工作。
  • 核心能力涵盖仓库信息检索、变更追踪和协作事项归纳,支持多分支和多项目并行管理。
  • 可通过 npx skills add 命令从指定 GitHub 仓库安装,需确认权限范围和访问限制后再使用。
  • 使用前应检查是否会触发网络请求、API 调用或文件操作,避免越权访问或数据泄露风险。

SKILL.md

gRPC Key-Value Store Implementation

Overview

This skill provides procedural guidance for implementing gRPC-based key-value store services in Python. It covers the full workflow from protobuf definition to server implementation and verification, with emphasis on avoiding common pitfalls.

Implementation Workflow

Step 1: Install Dependencies

Install the required gRPC packages:

pip install grpcio grpcio-tools

Verification: After installation, confirm packages are available:

pip list | grep grpc

Do not proceed until installation is verified. Missing or incorrect package versions cause silent failures during code generation.

Step 2: Define the Protocol Buffer

Create a .proto file with the service definition. Key considerations:

Type Selection for Values:

  • Use int32 for integer values (standard choice for most use cases)
  • Use int64 if values may exceed 32-bit range
  • Use string for keys (allows flexible key naming)

Message Design Pattern:

syntax = "proto3";

message SetRequest {
    string key = 1;
    int32 value = 2;
}

message SetResponse {
    bool success = 1;
}

message GetRequest {
    string key = 1;
}

message GetResponse {
    int32 value = 1;
    bool found = 2;  // Consider adding to distinguish missing keys from zero values
}

Edge Case Consideration: When returning values for non-existent keys, returning a default value (e.g., 0) is ambiguous. Consider:

  • Adding a found or exists boolean field to responses
  • Using gRPC status codes to indicate missing keys
  • Documenting the chosen behavior explicitly

Step 3: Generate Python Code

Run the protobuf compiler to generate Python gRPC code:

python -m grpc_tools.protoc -I. --python_out=. --grpc_python_out=. <filename>.proto

This generates two files:

  • <filename>_pb2.py - Message classes
  • <filename>_pb2_grpc.py - Service stubs and servicer base classes

Verification: Confirm both files were generated before proceeding:

ls -la *_pb2*.py

Step 4: Implement the Server

Create the server implementation with these considerations:

Server Class Structure:

class KVStoreServicer(kv_pb2_grpc.KVStoreServicer):
    def __init__(self):
        self.store = {}

    def SetVal(self, request, context):
        self.store[request.key] = request.value
        return kv_pb2.SetResponse(success=True)

    def GetVal(self, request, context):
        value = self.store.get(request.key, 0)
        # Document: Returns 0 for non-existent keys
        return kv_pb2.GetResponse(value=value)

Server Initialization:

def serve(port):
    server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
    kv_pb2_grpc.add_KVStoreServicer_to_server(KVStoreServicer(), server)
    server.add_insecure_port(f'[::]:{port}')
    server.start()
    print(f"Server started on port {port}", flush=True)  # flush=True is critical
    server.wait_for_termination()

Critical: Use flush=True for print statements when running servers in background processes. Without flushing, output may not appear immediately, making it difficult to verify server startup.

Step 5: Verify Server Operation

Prefer functional testing over system diagnostics. Testing with an actual gRPC client is the definitive verification method.

Avoid this approach (unreliable, tool-dependent):

# These may not be available and don't confirm gRPC functionality
netstat -tlnp | grep <port>
ss -tlnp | grep <port>
lsof -i :<port>

Use this approach (direct functional test):

import grpc
import kv_pb2
import kv_pb2_grpc

channel = grpc.insecure_channel('localhost:<port>')
stub = kv_pb2_grpc.KVStoreStub(channel)

# Test Set operation
response = stub.SetVal(kv_pb2.SetRequest(key="test", value=42))
print(f"Set success: {response.success}")

# Test Get operation
response = stub.GetVal(kv_pb2.GetRequest(key="test"))
print(f"Got value: {response.value}")

Run inline if possible to avoid creating temporary test files:

python -c "import grpc; ..."

Common Pitfalls

1. Missing Key Ambiguity

Returning default values (0, empty string) for missing keys is indistinguishable from keys with those actual values. Design the protocol to handle this explicitly.

2. Output Buffering in Background Processes

Print statements without flush=True may never appear when running servers in background. Always flush output for startup confirmation messages.

3. Excessive Diagnostic Commands

Avoid chaining system utilities (netstat, ss, lsof, ps) to verify server status. These tools may not be installed and don't confirm gRPC protocol functionality. Use a real client instead.

4. Unverified Package Installation

Always verify pip installations succeeded before proceeding. Silent installation failures cause confusing errors during code generation or runtime.

5. Missing Error Handling

Consider adding try-except blocks around RPC method implementations to handle unexpected errors gracefully and return appropriate gRPC status codes.

Verification Checklist

Before considering the task complete:

  • Dependencies installed and verified with pip list
  • Proto file compiles without errors
  • Both _pb2.py and _pb2_grpc.py files generated
  • Server starts and prints confirmation (with flush=True)
  • Functional test with gRPC client succeeds for all operations
  • Edge cases documented (missing keys, zero values)

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

28.79%
按下载量换算75

Gemini CLI

24.96%
按下载量换算65

Antigravity

19.6%
按下载量换算51

windsurf

11.56%
按下载量换算30

OpenCode

8.55%
按下载量换算22

Codex

3.1%
按下载量换算8

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

权限需确认

当前来源未能明确判断权限范围,默认进入异常复核队列。

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。

来源信息

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