Clade Observability
DevOps & Securityclade-observabilityClade Observability is an Agent Skill for devops & security published in the jeremylongshore/claude-code-plugins-plus-skills repository on GitHub: a SKILL.md instruction file an AI agent can load to do this task.
Monitor Claude API calls — log tokens, latency, costs, errors, and Use when working with observability patterns. set up alerts for production Claude integrations. Trigger with "anthropic monitoring", "claude observability", "track claude usage", "anthropic logging".
How to use this skill
Bring this guide into your coding agent with a prompt tailored to the tool you use.
- Open your project in Codex.
- Copy and paste the prompt below.
- Review files and risks before approving any action.
Inspect the Agent Skill at https://github.com/jeremylongshore/claude-code-plugins-plus-skills/blob/HEAD/plugins/saas-packs/claude-pack/skills/clade-observability/SKILL.md and all supporting files. Report provenance, permissions, scripts, dependencies, and security risks. Propose an exact installation into .agents/skills/clade-observability, but wait for my approval before writing files or running scripts.
Copying this prompt does not install or run the skill. Review third-party files before use.
Anthropic Observability
Overview
Every messages.create call should be instrumented. Track tokens, latency, cost, model, and errors.
Logging Wrapper
import Anthropic from '@claude-ai/sdk';
const client = new Anthropic();
async function trackedCreate(params: Anthropic.MessageCreateParams) {
const start = performance.now();
try {
const message = await client.messages.create(params);
const durationMs = Math.round(performance.now() - start);
const log = {
timestamp: new Date().toISOString(),
model: message.model,
input_tokens: message.usage.input_tokens,
output_tokens: message.usage.output_tokens,
cache_read_tokens: message.usage.cache_read_input_tokens || 0,
duration_ms: durationMs,
stop_reason: message.stop_reason,
estimated_cost: estimateCost(message.model, message.usage),
};
console.log('anthropic_request', JSON.stringify(log));
return message;
} catch (err) {
const durationMs = Math.round(performance.now() - start);
console.error('anthropic_error', JSON.stringify({
timestamp: new Date().toISOString(),
model: params.model,
error_type: err instanceof Anthropic.APIError ? err.error?.type : 'unknown',
status: err instanceof Anthropic.APIError ? err.status : null,
request_id: err instanceof Anthropic.APIError ? err.headers?.['request-id'] : null,
duration_ms: durationMs,
}));
throw err;
}
}
function estimateCost(model: string, usage: Anthropic.Usage): number {
const rates: Record<string, [number, number]> = {
'claude-opus-4-20250514': [15, 75],
'claude-sonnet-4-20250514': [3, 15],
'claude-haiku-4-5-20251001': [0.80, 4],
};
const [inputRate, outputRate] = rates[model] || [3, 15];
return (usage.input_tokens * inputRate + usage.output_tokens * outputRate) / 1_000_000;
}
Key Metrics to Track
| Metric | Source | Alert Threshold |
|---|---|---|
| Error rate | error logs | > 5% over 5 minutes |
| p95 latency | duration_ms | > 10s (Sonnet) |
| Daily cost | estimated_cost sum | > 2x daily average |
| 429 rate | error_type = rate_limit | > 10/minute |
| 529 rate | error_type = overloaded | > 5/minute |
| Token usage | input_tokens + output_tokens | > daily budget |
Anthropic Console Monitoring
- Usage dashboard: console.anthropic.com → Usage
- Spending limits: console.anthropic.com → Settings → Limits
- API logs: Not available via API — use your own logging
Output
- Every Claude API call logged with tokens, latency, cost estimate, and model
- Error calls logged with request ID, status code, and error type
- Metrics dashboarded: error rate, p95 latency, daily cost, 429/529 rates
- Spending alerts configured in Anthropic console
Error Handling
| Error | Cause | Solution |
|---|---|---|
| API Error | Check error type and status code | See clade-common-errors |
Examples
See Logging Wrapper with trackedCreate(), estimateCost() function, Key Metrics table with alert thresholds, and Anthropic Console Monitoring section above.
Resources
Next Steps
See clade-incident-runbook for when things go wrong.
Prerequisites
- Completed
clade-install-auth - Logging infrastructure (console, structured logs, or observability platform)
- Production Claude integration to monitor
Instructions
Step 1: Review the patterns below
Each section contains production-ready code examples. Copy and adapt them to your use case.
Step 2: Apply to your codebase
Integrate the patterns that match your requirements. Test each change individually.
Step 3: Verify
Run your test suite to confirm the integration works correctly.