home / skills / andrueandersoncs / claude-skill-effect-ts / observability

observability skill

/skills/observability

This skill helps you understand and implement logging, metrics, and tracing with Effect, improving observability across applications.

npx playbooks add skill andrueandersoncs/claude-skill-effect-ts --skill observability

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SKILL.md
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---
name: Observability
description: This skill should be used when the user asks about "Effect logging", "Effect.log", "Effect metrics", "Effect tracing", "spans", "telemetry", "Metric.counter", "Metric.gauge", "Metric.histogram", "OpenTelemetry", "structured logging", "log levels", "Effect.logDebug", "Effect.logInfo", "Effect.logWarning", "Effect.logError", or needs to understand how Effect handles logging, metrics, and distributed tracing.
version: 1.0.0
---

# 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

```typescript
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

```typescript
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

```typescript
// 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

```typescript
// 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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
const statusCodes = Metric.frequency("http_status_codes")

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

### Tagged Metrics

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

```typescript
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

Overview

This skill explains how Effect handles observability: structured logging, metrics, and distributed tracing. It focuses on Effect APIs for logging levels, annotations, spans, counters, gauges, histograms, summaries, and OpenTelemetry integration. Use it to learn concrete patterns to instrument Effect-based applications for production monitoring and debugging.

How this skill works

Effect embeds observability into its execution model so logs, metrics, and traces are first-class effects that compose with your program. Logging supports leveled, structured messages and scoped annotations/spans. Metrics provide counters, gauges, histograms, summaries, and tagged/frequency metrics. Tracing creates nested spans, sets attributes and status, and can be wired to OpenTelemetry exporters or custom tracers.

When to use it

  • You need structured, leveled logging within Effect programs.
  • You want to count occurrences, track current values, or measure distributions (counters, gauges, histograms).
  • You need distributed tracing with nested spans and span attributes.
  • You want to export traces to OpenTelemetry or provide a custom tracer/logger implementation.
  • You need tagged metrics or dynamic labels for service/version/method breakdowns.

Best practices

  • Use structured logging and annotate logs with contextual fields (userId, request path) to make logs queryable.
  • Name spans descriptively (verb-noun) and nest them to reflect call structure for easier trace analysis.
  • Record key metrics: request count, latency histograms, error counts, and current active connections.
  • Set appropriate log levels: Debug for development, Info for production, and use Warning/Error/Fatal for incidents.
  • Attach meaningful span attributes and set span status on failures to correlate traces with errors and logs.

Example use cases

  • Increment a Metric.counter for each HTTP request and expose it to a metrics backend.
  • Wrap request handling in Effect.withSpan("handle-request") and annotate spans with http.method and http.url for traceability.
  • Use Effect.log with Effect.annotateLogs to add userId and session data to all logs in a request scope.
  • Measure request latency with Metric.histogram and use Metric.trackDuration to automate observation.
  • Replace the default logger/tracer with a JSON logger or a custom tracer that forwards spans to OpenTelemetry/OTLP exporters.

FAQ

How do I add contextual fields to all logs for a request?

Use Effect.annotateLogs around the request handler to add annotations (e.g., userId, path) that apply to all logs in that scope.

Can I export traces to OpenTelemetry?

Yes. Provide an OpenTelemetry-enabled tracer layer (NodeSdk.layer with an OTLP exporter) to wire Effect traces to OTLP backends.