jeremylongshore

linear-observability

@jeremylongshore/linear-observability
jeremylongshore
1,004
123 forks
Updated 1/18/2026
View on GitHub

Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, creating dashboards, or configuring alerts for Linear API usage. Trigger with phrases like "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear integration".

Installation

$skills install @jeremylongshore/linear-observability
Claude Code
Cursor
Copilot
Codex
Antigravity

Details

Pathplugins/saas-packs/linear-pack/skills/linear-observability/SKILL.md
Branchmain
Scoped Name@jeremylongshore/linear-observability

Usage

After installing, this skill will be available to your AI coding assistant.

Verify installation:

skills list

Skill Instructions


name: linear-observability description: | Implement monitoring, logging, and alerting for Linear integrations. Use when setting up metrics collection, creating dashboards, or configuring alerts for Linear API usage. Trigger with phrases like "linear monitoring", "linear observability", "linear metrics", "linear logging", "monitor linear integration". allowed-tools: Read, Write, Edit, Grep version: 1.0.0 license: MIT author: Jeremy Longshore jeremy@intentsolutions.io

Linear Observability

Overview

Comprehensive monitoring, logging, and alerting for Linear integrations.

Prerequisites

  • Linear integration deployed
  • Metrics infrastructure (Prometheus, Datadog, etc.)
  • Logging infrastructure (ELK, CloudWatch, etc.)
  • Alerting system configured

Instructions

Step 1: Metrics Collection

// lib/metrics.ts
import { Counter, Histogram, Gauge, Registry } from "prom-client";

const registry = new Registry();

// Request metrics
export const linearRequestsTotal = new Counter({
  name: "linear_api_requests_total",
  help: "Total Linear API requests",
  labelNames: ["operation", "status"],
  registers: [registry],
});

export const linearRequestDuration = new Histogram({
  name: "linear_api_request_duration_seconds",
  help: "Linear API request duration in seconds",
  labelNames: ["operation"],
  buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
  registers: [registry],
});

export const linearComplexityCost = new Histogram({
  name: "linear_api_complexity_cost",
  help: "Linear API query complexity cost",
  labelNames: ["operation"],
  buckets: [10, 50, 100, 250, 500, 1000, 2500],
  registers: [registry],
});

// Rate limit metrics
export const linearRateLimitRemaining = new Gauge({
  name: "linear_rate_limit_remaining",
  help: "Remaining Linear API rate limit",
  registers: [registry],
});

export const linearComplexityRemaining = new Gauge({
  name: "linear_complexity_remaining",
  help: "Remaining Linear complexity quota",
  registers: [registry],
});

// Webhook metrics
export const linearWebhooksReceived = new Counter({
  name: "linear_webhooks_received_total",
  help: "Total Linear webhooks received",
  labelNames: ["type", "action"],
  registers: [registry],
});

export const linearWebhookProcessingDuration = new Histogram({
  name: "linear_webhook_processing_duration_seconds",
  help: "Linear webhook processing duration",
  labelNames: ["type"],
  buckets: [0.01, 0.05, 0.1, 0.25, 0.5, 1, 2.5],
  registers: [registry],
});

// Cache metrics
export const linearCacheHits = new Counter({
  name: "linear_cache_hits_total",
  help: "Total Linear cache hits",
  registers: [registry],
});

export const linearCacheMisses = new Counter({
  name: "linear_cache_misses_total",
  help: "Total Linear cache misses",
  registers: [registry],
});

export { registry };

Step 2: Instrumented Client Wrapper

// lib/instrumented-client.ts
import { LinearClient } from "@linear/sdk";
import {
  linearRequestsTotal,
  linearRequestDuration,
  linearRateLimitRemaining,
  linearComplexityRemaining,
} from "./metrics";

export function createInstrumentedClient(apiKey: string): LinearClient {
  const client = new LinearClient({
    apiKey,
    fetch: async (url, init) => {
      const operation = extractOperationName(init?.body);
      const timer = linearRequestDuration.startTimer({ operation });

      try {
        const response = await fetch(url, init);

        // Record rate limit headers
        const remaining = response.headers.get("x-ratelimit-remaining");
        const complexity = response.headers.get("x-complexity-remaining");

        if (remaining) linearRateLimitRemaining.set(parseInt(remaining));
        if (complexity) linearComplexityRemaining.set(parseInt(complexity));

        // Record success/failure
        const status = response.ok ? "success" : "error";
        linearRequestsTotal.inc({ operation, status });

        timer();
        return response;
      } catch (error) {
        linearRequestsTotal.inc({ operation, status: "error" });
        timer();
        throw error;
      }
    },
  });

  return client;
}

function extractOperationName(body: BodyInit | undefined): string {
  if (!body || typeof body !== "string") return "unknown";

  try {
    const parsed = JSON.parse(body);
    // Extract operation name from GraphQL query
    const match = parsed.query?.match(/(?:query|mutation)\s+(\w+)/);
    return match?.[1] || "anonymous";
  } catch {
    return "unknown";
  }
}

Step 3: Structured Logging

// lib/logger.ts
import pino from "pino";

export const logger = pino({
  level: process.env.LOG_LEVEL || "info",
  formatters: {
    level: (label) => ({ level: label }),
  },
  base: {
    service: "linear-integration",
    environment: process.env.NODE_ENV,
  },
});

// Linear-specific logger
export const linearLogger = logger.child({ component: "linear" });

// Log API calls
export function logApiCall(operation: string, duration: number, success: boolean) {
  linearLogger.info({
    event: "api_call",
    operation,
    duration_ms: duration,
    success,
  });
}

// Log webhook events
export function logWebhook(type: string, action: string, id: string) {
  linearLogger.info({
    event: "webhook_received",
    webhook_type: type,
    webhook_action: action,
    entity_id: id,
  });
}

// Log errors with context
export function logError(error: Error, context: Record<string, unknown>) {
  linearLogger.error({
    event: "error",
    error_message: error.message,
    error_stack: error.stack,
    ...context,
  });
}

Step 4: Health Check Endpoint

// api/health.ts
import { LinearClient } from "@linear/sdk";
import { registry } from "../lib/metrics";

interface HealthStatus {
  status: "healthy" | "degraded" | "unhealthy";
  checks: {
    linear_api: { status: string; latency_ms?: number; error?: string };
    cache: { status: string; hit_rate?: number };
    rate_limit: { status: string; remaining?: number; percentage?: number };
  };
  timestamp: string;
}

export async function healthCheck(client: LinearClient): Promise<HealthStatus> {
  const checks: HealthStatus["checks"] = {
    linear_api: { status: "unknown" },
    cache: { status: "unknown" },
    rate_limit: { status: "unknown" },
  };

  // Check Linear API
  const start = Date.now();
  try {
    await client.viewer;
    checks.linear_api = {
      status: "healthy",
      latency_ms: Date.now() - start,
    };
  } catch (error) {
    checks.linear_api = {
      status: "unhealthy",
      error: error instanceof Error ? error.message : "Unknown error",
    };
  }

  // Check rate limit status
  const metrics = await registry.getMetricsAsJSON();
  const rateLimitMetric = metrics.find(m => m.name === "linear_rate_limit_remaining");
  if (rateLimitMetric) {
    const remaining = (rateLimitMetric as any).values?.[0]?.value || 0;
    const percentage = (remaining / 1500) * 100;
    checks.rate_limit = {
      status: percentage > 10 ? "healthy" : percentage > 5 ? "degraded" : "unhealthy",
      remaining,
      percentage: Math.round(percentage),
    };
  }

  // Determine overall status
  const statuses = Object.values(checks).map(c => c.status);
  let status: HealthStatus["status"] = "healthy";
  if (statuses.includes("unhealthy")) status = "unhealthy";
  else if (statuses.includes("degraded")) status = "degraded";

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

Step 5: Alerting Rules

# prometheus/alerts.yml
groups:
  - name: linear-integration
    rules:
      # High error rate
      - alert: LinearHighErrorRate
        expr: |
          sum(rate(linear_api_requests_total{status="error"}[5m]))
          / sum(rate(linear_api_requests_total[5m])) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: High Linear API error rate
          description: "Linear API error rate is {{ $value | humanizePercentage }}"

      # Rate limit approaching
      - alert: LinearRateLimitLow
        expr: linear_rate_limit_remaining < 100
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: Linear rate limit running low
          description: "Only {{ $value }} requests remaining in rate limit window"

      # Slow API responses
      - alert: LinearSlowResponses
        expr: |
          histogram_quantile(0.95, rate(linear_api_request_duration_seconds_bucket[5m])) > 2
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: Slow Linear API responses
          description: "95th percentile response time is {{ $value }}s"

      # Webhook processing errors
      - alert: LinearWebhookErrors
        expr: |
          sum(rate(linear_webhooks_received_total{status="error"}[5m])) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: Linear webhook processing errors
          description: "Webhook error rate: {{ $value }} per second"

Step 6: Grafana Dashboard

{
  "dashboard": {
    "title": "Linear Integration",
    "panels": [
      {
        "title": "API Request Rate",
        "type": "graph",
        "targets": [
          {
            "expr": "sum(rate(linear_api_requests_total[5m])) by (status)",
            "legendFormat": "{{ status }}"
          }
        ]
      },
      {
        "title": "Request Latency (p95)",
        "type": "gauge",
        "targets": [
          {
            "expr": "histogram_quantile(0.95, rate(linear_api_request_duration_seconds_bucket[5m]))"
          }
        ]
      },
      {
        "title": "Rate Limit Remaining",
        "type": "stat",
        "targets": [
          {
            "expr": "linear_rate_limit_remaining"
          }
        ]
      },
      {
        "title": "Webhooks by Type",
        "type": "piechart",
        "targets": [
          {
            "expr": "sum(linear_webhooks_received_total) by (type)"
          }
        ]
      }
    ]
  }
}

Error Handling

ErrorCauseSolution
Metrics not collectingMissing instrumentationAdd metrics to client wrapper
Alerts not firingWrong thresholdAdjust alert thresholds
Missing labelsLogger misconfiguredCheck logger configuration

Resources

Next Steps

Create incident runbooks with linear-incident-runbook.

More by jeremylongshore

View all
oauth-callback-handler
1,004

Oauth Callback Handler - Auto-activating skill for API Integration. Triggers on: oauth callback handler, oauth callback handler Part of the API Integration skill category.

rabbitmq-queue-setup
1,004

Rabbitmq Queue Setup - Auto-activating skill for Backend Development. Triggers on: rabbitmq queue setup, rabbitmq queue setup Part of the Backend Development skill category.

model-evaluation-suite
1,004

evaluating-machine-learning-models: This skill allows Claude to evaluate machine learning models using a comprehensive suite of metrics. It should be used when the user requests model performance analysis, validation, or testing. Claude can use this skill to assess model accuracy, precision, recall, F1-score, and other relevant metrics. Trigger this skill when the user mentions "evaluate model", "model performance", "testing metrics", "validation results", or requests a comprehensive "model evaluation".

neural-network-builder
1,004

building-neural-networks: This skill allows Claude to construct and configure neural network architectures using the neural-network-builder plugin. It should be used when the user requests the creation of a new neural network, modification of an existing one, or assistance with defining the layers, parameters, and training process. The skill is triggered by requests involving terms like "build a neural network," "define network architecture," "configure layers," or specific mentions of neural network types (e.g., "CNN," "RNN," "transformer").