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".
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/linear-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How linear-observability Compares
| Feature / Agent | linear-observability | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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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)Related Skills
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