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observability可观测性

Agent Skill

observability 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。

总安装

245

周安装

10

GitHub Stars

7

下载量

78
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:observability(可观测性)
来源仓库:https://github.com/andrueandersoncs/claude-skill-effect-ts
仓库路径:skills/observability
安装命令:
npx skills add https://github.com/andrueandersoncs/claude-skill-effect-ts --skill Observability
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/andrueandersoncs/claude-skill-effect-ts --skill Observability

简介

基于 Effect 框架内置的可观测性能力,提供结构化日志、指标整理与分布式追踪的统一集成方案。

  • 适用于需要监控 Effect 程序执行流程、性能指标与错误溯源的应用场景。
  • 支持分级日志输出(debug/info/warning/error)、计数器、直方图与 span 追踪。
  • 所有组件无缝集成于 Effect 执行模型,无需额外配置即可启用。
  • 需导入 effect 相关模块并遵循其执行上下文规则,不适合传统回调式代码。

SKILL.md

Observability in Effect

Overview

Effect provides built-in observability:

  • Logging - Structured, leveled logging
  • Metrics - Counters, gauges, histograms
  • Tracing - Distributed tracing with spans

All three integrate seamlessly with Effect's execution model.

Logging

Basic Logging

import { Effect } from "effect";

const program = Effect.gen(function* () {
  yield* Effect.log("Starting process");
  yield* Effect.logDebug("Debug information");
  yield* Effect.logInfo("Processing item");
  yield* Effect.logWarning("Resource running low");
  yield* Effect.logError("Failed to connect");
  yield* Effect.logFatal("Critical system failure");
});

Log Levels

import { LogLevel, Logger } from "effect";

// Set minimum log level
const filtered = program.pipe(Logger.withMinimumLogLevel(LogLevel.Info));

// Available levels (lowest to highest):
// Trace, Debug, Info, Warning, Error, Fatal, None

Structured Logging

// Log with structured data
yield *
  Effect.log("User action").pipe(
    Effect.annotateLogs({
      userId: "123",
      action: "login",
      ip: "192.168.1.1",
    }),
  );

// Annotations apply to all logs in scope
const program = Effect.gen(function* () {
  yield* Effect.log("First log"); // Has userId annotation
  yield* Effect.log("Second log"); // Has userId annotation
}).pipe(Effect.annotateLogs({ userId: "123" }));

Log Spans

// Add timing/context spans
const program = Effect.gen(function* () {
  yield* Effect.log("Processing");
  yield* processItems();
  yield* Effect.log("Complete");
}).pipe(Effect.withLogSpan("request-handler"));
// Logs include: [request-handler 45ms] Processing

Custom Logger

import { Logger } from "effect";

const JsonLogger = Logger.make(({ logLevel, message, annotations, date }) => {
  console.log(
    JSON.stringify({
      level: logLevel.label,
      message: String(message),
      timestamp: date.toISOString(),
      ...annotations,
    }),
  );
});

const program = Effect.gen(function* () {
  yield* Effect.log("Hello");
}).pipe(Effect.provide(Logger.replace(Logger.defaultLogger, JsonLogger)));

Metrics

Counter - Track Occurrences

import { Metric } from "effect";

const requestCount = Metric.counter("http_requests_total", {
  description: "Total HTTP requests",
});

const program = Effect.gen(function* () {
  yield* Metric.increment(requestCount);
  yield* Metric.incrementBy(requestCount, 5);
});

const tracked = handleRequest.pipe(Metric.trackAll(requestCount));

Gauge - Track Current Value

const activeConnections = Metric.gauge("active_connections", {
  description: "Current active connections",
});

const program = Effect.gen(function* () {
  yield* Metric.set(activeConnections, 10);
  yield* Metric.incrementBy(activeConnections, 1);
  yield* Metric.decrementBy(activeConnections, 1);
});

Histogram - Track Distributions

const requestDuration = Metric.histogram("http_request_duration_ms", {
  description: "Request duration in milliseconds",
  boundaries: [10, 50, 100, 250, 500, 1000],
});

yield * Metric.observe(requestDuration, 125);

const tracked = handleRequest.pipe(Metric.trackDuration(requestDuration));

Summary - Statistical Summary

const responseSizes = Metric.summary("response_size_bytes", {
  description: "Response payload sizes",
  maxAge: "1 minute",
  maxSize: 100,
  quantiles: [0.5, 0.9, 0.99],
});

yield * Metric.observe(responseSizes, 1024);

Frequency - Count by Tag

const statusCodes = Metric.frequency("http_status_codes");

yield * Metric.observe(statusCodes, "200");
yield * Metric.observe(statusCodes, "404");
yield * Metric.observe(statusCodes, "500");

Tagged Metrics

const requestCount = Metric.counter("requests").pipe(Metric.tagged("service", "api"), Metric.tagged("version", "v1"));

// Dynamic tags
const taggedCount = Metric.counter("requests").pipe(Metric.taggedWithLabels(["method", "endpoint"]));

yield * Metric.increment(taggedCount).pipe(Metric.taggedWithLabels(["GET", "/users"]));

Reading Metrics

const program = Effect.gen(function* () {
  yield* Metric.increment(requestCount);
  yield* Metric.increment(requestCount);

  const snapshot = yield* Metric.value(requestCount);
  // snapshot.count === 2
});

Tracing

Creating Spans

import { Effect } from "effect";

const traced = handleRequest.pipe(Effect.withSpan("handle-request"));

const traced = handleRequest.pipe(
  Effect.withSpan("handle-request", {
    attributes: {
      "http.method": "GET",
      "http.url": "/api/users",
    },
  }),
);

Nested Spans

const program = Effect.gen(function* () {
  yield* fetchUser(id).pipe(Effect.withSpan("fetch-user"));
  yield* processData(data).pipe(Effect.withSpan("process-data"));
  yield* saveResult(result).pipe(Effect.withSpan("save-result"));
}).pipe(Effect.withSpan("main-operation"));
// Creates: main-operation
//            ├── fetch-user
//            ├── process-data
//            └── save-result

Adding Span Attributes

import { Tracer } from "effect";

const program = Effect.gen(function* () {
  yield* Effect.annotateCurrentSpan("user.id", userId);

  yield* Effect.annotateCurrentSpan("event", "user_validated");

  const result = yield* processUser(userId);

  yield* Effect.annotateCurrentSpan("result.status", result.status);
});

Span Status

const program = Effect.gen(function* () {
  try {
    return yield* riskyOperation;
  } catch (error) {
    yield* Effect.setSpanStatus({
      code: "error",
      message: error.message,
    });
    return yield* Effect.fail(error);
  }
}).pipe(Effect.withSpan("risky-operation"));

Custom Tracer

import { Tracer } from "effect";

const ConsoleTracer = Tracer.make({
  span: (name, parent, context, links, startTime) => ({
    attribute: (key, value) => console.log(`[${name}] ${key}=${value}`),
    end: (endTime, exit) => console.log(`[${name}] ended`),
    event: (name, startTime, attributes) => console.log(`[${name}] event: ${name}`),
    status: (status) => console.log(`[${name}] status: ${status.code}`),
  }),
});

const program = myEffect.pipe(Effect.provide(Tracer.layer(ConsoleTracer)));

OpenTelemetry Integration

import { NodeSdk } from "@effect/opentelemetry";
import { OTLPTraceExporter } from "@opentelemetry/exporter-trace-otlp-http";

const TracingLive = NodeSdk.layer(() => ({
  resource: { serviceName: "my-service" },
  spanProcessor: new BatchSpanProcessor(new OTLPTraceExporter()),
}));

const program = myEffect.pipe(Effect.provide(TracingLive));

Combining Observability

const handleRequest = (req: Request) =>
  Effect.gen(function* () {
    yield* Effect.log("Request received").pipe(Effect.annotateLogs({ path: req.path, method: req.method }));

    yield* Metric.increment(requestCount);

    const result = yield* processRequest(req);

    yield* Effect.annotateCurrentSpan("response.status", result.status);
    yield* Metric.observe(requestDuration, result.duration);

    return result;
  }).pipe(
    Effect.withSpan("handle-request", {
      attributes: {
        "http.method": req.method,
        "http.url": req.path,
      },
    }),
  );

Best Practices

  1. Use structured logging - Add context via annotations
  2. Name spans descriptively - Use verb-noun format
  3. Add meaningful attributes - Enable debugging/analysis
  4. Track key metrics - Request count, latency, errors
  5. Use appropriate log levels - Debug in dev, Info in prod

Additional Resources

For comprehensive observability documentation, consult ${CLAUDE_PLUGIN_ROOT}/references/llms-full.txt.

Search for these sections:

  • "Built-in Logging" for logging APIs
  • "Metrics" for metric types
  • "Tracing" for distributed tracing

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

33.24%
按下载量换算26

OpenCode

21.33%
按下载量换算17

Gemini CLI

17.94%
按下载量换算14

Antigravity

11.86%
按下载量换算9

windsurf

7.28%
按下载量换算6

Codex

3.23%
按下载量换算3

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

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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