linear-observability

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, dashboards, or configuring alerts for Linear API usage. Trigger: "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear", "linear Prometheus", "linear Grafana".

1,868 stars

Best use case

linear-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, dashboards, or configuring alerts for Linear API usage. Trigger: "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear", "linear Prometheus", "linear Grafana".

Teams using linear-observability should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/linear-observability/SKILL.md --create-dirs "https://raw.githubusercontent.com/jeremylongshore/claude-code-plugins-plus-skills/main/plugins/saas-packs/linear-pack/skills/linear-observability/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/linear-observability/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How linear-observability Compares

Feature / Agentlinear-observabilityStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, dashboards, or configuring alerts for Linear API usage. Trigger: "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear", "linear Prometheus", "linear Grafana".

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Linear Observability

## Overview
Production monitoring for Linear integrations using Prometheus metrics, structured logging with pino, health checks, and alerting rules. Track API latency, error rates, rate limit headroom, and webhook throughput.

## Prerequisites
- Linear integration deployed
- Prometheus or Datadog for metrics
- Structured logging (pino, winston)
- Alerting system (PagerDuty, OpsGenie, Slack)

## Instructions

### Step 1: Define Metrics
```typescript
// src/metrics/linear-metrics.ts
import { Counter, Histogram, Gauge, register } from "prom-client";

export const metrics = {
  // API request tracking
  apiRequests: new Counter({
    name: "linear_api_requests_total",
    help: "Total Linear API requests",
    labelNames: ["operation", "status"],
  }),

  // Request duration
  apiLatency: new Histogram({
    name: "linear_api_request_duration_seconds",
    help: "Linear API request duration",
    labelNames: ["operation"],
    buckets: [0.1, 0.25, 0.5, 1, 2, 5, 10],
  }),

  // Rate limit headroom
  rateLimitRemaining: new Gauge({
    name: "linear_rate_limit_remaining",
    help: "Remaining rate limit budget",
    labelNames: ["type"], // "requests" or "complexity"
  }),

  // Webhook tracking
  webhooksReceived: new Counter({
    name: "linear_webhooks_received_total",
    help: "Total webhooks received",
    labelNames: ["type", "action"],
  }),

  webhookProcessingDuration: new Histogram({
    name: "linear_webhook_processing_seconds",
    help: "Webhook processing duration",
    labelNames: ["type"],
    buckets: [0.01, 0.05, 0.1, 0.5, 1, 5],
  }),

  // Cache effectiveness
  cacheHits: new Counter({
    name: "linear_cache_hits_total",
    help: "Cache hit count",
    labelNames: ["key"],
  }),
  cacheMisses: new Counter({
    name: "linear_cache_misses_total",
    help: "Cache miss count",
    labelNames: ["key"],
  }),
};

// Expose metrics endpoint
app.get("/metrics", async (req, res) => {
  res.set("Content-Type", register.contentType);
  res.end(await register.metrics());
});
```

### Step 2: Instrumented Client Wrapper
```typescript
import { LinearClient } from "@linear/sdk";

function instrumentedCall<T>(
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  const timer = metrics.apiLatency.startTimer({ operation });

  return fn()
    .then((result) => {
      metrics.apiRequests.inc({ operation, status: "success" });
      timer();
      return result;
    })
    .catch((error: any) => {
      const status = error.status === 429 ? "rate_limited" : "error";
      metrics.apiRequests.inc({ operation, status });
      timer();
      throw error;
    });
}

// Usage
const client = new LinearClient({ apiKey: process.env.LINEAR_API_KEY! });

const teams = await instrumentedCall("teams", () => client.teams());
const issues = await instrumentedCall("issues", () =>
  client.issues({ first: 50 })
);
```

### Step 3: Structured Logging
```typescript
import pino from "pino";

const logger = pino({
  level: process.env.LOG_LEVEL ?? "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
});

const linearLog = logger.child({ component: "linear" });

// Log API calls
function logApiCall(operation: string, durationMs: number, success: boolean, meta?: any) {
  linearLog.info({
    event: "api_call",
    operation,
    durationMs,
    success,
    ...meta,
  });
}

// Log webhook events
function logWebhook(type: string, action: string, deliveryId: string, meta?: any) {
  linearLog.info({
    event: "webhook",
    type,
    action,
    deliveryId,
    ...meta,
  });
}

// Log errors with context
function logError(operation: string, error: any) {
  linearLog.error({
    event: "error",
    operation,
    errorMessage: error.message,
    errorStatus: error.status,
    errorType: error.type,
    // Never log API keys or tokens
  });
}
```

### Step 4: Health Check Endpoint
```typescript
interface HealthCheck {
  status: "healthy" | "degraded" | "unhealthy";
  checks: Record<string, {
    status: string;
    latencyMs?: number;
    error?: string;
  }>;
  timestamp: string;
}

async function checkLinearHealth(client: LinearClient): Promise<HealthCheck> {
  const checks: HealthCheck["checks"] = {};

  // Check API connectivity
  const apiStart = Date.now();
  try {
    const viewer = await client.viewer;
    checks.linear_api = {
      status: "healthy",
      latencyMs: Date.now() - apiStart,
    };
  } catch (error: any) {
    checks.linear_api = {
      status: "unhealthy",
      latencyMs: Date.now() - apiStart,
      error: error.message,
    };
  }

  // Check rate limit headroom
  try {
    const resp = await fetch("https://api.linear.app/graphql", {
      method: "POST",
      headers: {
        Authorization: process.env.LINEAR_API_KEY!,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ query: "{ viewer { id } }" }),
    });
    const remaining = parseInt(resp.headers.get("x-ratelimit-requests-remaining") ?? "5000");
    metrics.rateLimitRemaining.set({ type: "requests" }, remaining);

    checks.rate_limit = {
      status: remaining > 100 ? "healthy" : "degraded",
      latencyMs: remaining,
    };
  } catch {
    checks.rate_limit = { status: "unknown" };
  }

  const overall = Object.values(checks).some(c => c.status === "unhealthy")
    ? "unhealthy"
    : Object.values(checks).some(c => c.status === "degraded")
    ? "degraded"
    : "healthy";

  return { status: overall, checks, timestamp: new Date().toISOString() };
}

app.get("/health/linear", async (req, res) => {
  const health = await checkLinearHealth(client);
  res.status(health.status === "unhealthy" ? 503 : 200).json(health);
});
```

### Step 5: Alerting Rules (Prometheus)
```yaml
# prometheus/linear-alerts.yml
groups:
  - name: linear
    rules:
      - alert: LinearHighErrorRate
        expr: |
          rate(linear_api_requests_total{status="error"}[5m])
          / rate(linear_api_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Linear API error rate > 5%"

      - alert: LinearRateLimitLow
        expr: linear_rate_limit_remaining{type="requests"} < 100
        for: 2m
        labels:
          severity: critical
        annotations:
          summary: "Linear rate limit remaining < 100 requests"

      - alert: LinearHighLatency
        expr: |
          histogram_quantile(0.95, rate(linear_api_request_duration_seconds_bucket[5m])) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Linear API p95 latency > 2 seconds"

      - alert: LinearWebhookProcessingSlow
        expr: |
          histogram_quantile(0.95, rate(linear_webhook_processing_seconds_bucket[5m])) > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Webhook processing p95 > 5 seconds"
```

### Step 6: Webhook Instrumentation
```typescript
// Instrument webhook handler
app.post("/webhooks/linear", express.raw({ type: "*/*" }), async (req, res) => {
  const start = Date.now();
  // ... signature verification ...

  const event = JSON.parse(req.body.toString());
  const delivery = req.headers["linear-delivery"] as string;

  metrics.webhooksReceived.inc({ type: event.type, action: event.action });
  logWebhook(event.type, event.action, delivery);

  res.json({ ok: true });

  try {
    await processEvent(event);
    metrics.webhookProcessingDuration.observe(
      { type: event.type },
      (Date.now() - start) / 1000
    );
  } catch (error: any) {
    logError("webhook_processing", error);
  }
});
```

## Error Handling

| Error | Cause | Solution |
|-------|-------|----------|
| Metrics not collecting | Missing instrumentation | Wrap all client calls with `instrumentedCall()` |
| Alerts not firing | Thresholds too high | Adjust based on actual traffic patterns |
| Health check timeout | Linear API slow | Add 10s timeout to health check |
| Log volume too high | Debug level in production | Set `LOG_LEVEL=info` in prod |

## Examples

### Quick Health Check
```bash
curl -s http://localhost:3000/health/linear | jq .
# { "status": "healthy", "checks": { "linear_api": { "status": "healthy", "latencyMs": 150 } } }
```

## Resources
- [Prometheus Client](https://github.com/siimon/prom-client)
- [Pino Logger](https://getpino.io/)
- [Grafana Dashboards](https://grafana.com/docs/grafana/latest/dashboards/)
- [Linear API Status](https://status.linear.app)

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