clade-observability
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".
Best use case
clade-observability is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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".
Teams using clade-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/clade-observability/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How clade-observability Compares
| Feature / Agent | clade-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?
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".
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.
SKILL.md Source
# Anthropic Observability
## Overview
Every `messages.create` call should be instrumented. Track tokens, latency, cost, model, and errors.
## Logging Wrapper
```typescript
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
- [Usage Dashboard](https://console.anthropic.com/settings/usage)
- [Rate Limits](https://docs.anthropic.com/en/api/rate-limits)
## 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.Related Skills
exa-observability
Set up monitoring, metrics, and alerting for Exa search integrations. Use when implementing monitoring for Exa operations, building dashboards, or configuring alerting for search quality and latency. Trigger with phrases like "exa monitoring", "exa metrics", "exa observability", "monitor exa", "exa alerts", "exa dashboard".
evernote-observability
Implement observability for Evernote integrations. Use when setting up monitoring, logging, tracing, or alerting for Evernote applications. Trigger with phrases like "evernote monitoring", "evernote logging", "evernote metrics", "evernote observability".
documenso-observability
Implement monitoring, logging, and tracing for Documenso integrations. Use when setting up observability, implementing metrics collection, or debugging production issues. Trigger with phrases like "documenso monitoring", "documenso metrics", "documenso logging", "documenso tracing", "documenso observability".
deepgram-observability
Set up comprehensive observability for Deepgram integrations. Use when implementing monitoring, setting up dashboards, or configuring alerting for Deepgram integration health. Trigger: "deepgram monitoring", "deepgram metrics", "deepgram observability", "monitor deepgram", "deepgram alerts", "deepgram dashboard".
databricks-observability
Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
customerio-observability
Set up Customer.io monitoring and observability. Use when implementing metrics, structured logging, alerting, or Grafana dashboards for Customer.io integrations. Trigger: "customer.io monitoring", "customer.io metrics", "customer.io dashboard", "customer.io alerts", "customer.io observability".
coreweave-observability
Set up GPU monitoring and observability for CoreWeave workloads. Use when implementing GPU metrics dashboards, configuring alerts, or tracking inference latency and throughput. Trigger with phrases like "coreweave monitoring", "coreweave observability", "coreweave gpu metrics", "coreweave grafana".
cohere-observability
Set up comprehensive observability for Cohere API v2 with metrics, traces, and alerts. Use when implementing monitoring for Chat/Embed/Rerank operations, setting up dashboards, or configuring alerts for Cohere integrations. Trigger with phrases like "cohere monitoring", "cohere metrics", "cohere observability", "monitor cohere", "cohere alerts", "cohere tracing".
coderabbit-observability
Monitor CodeRabbit review effectiveness with metrics, dashboards, and alerts. Use when tracking review coverage, measuring comment acceptance rates, or building dashboards for CodeRabbit adoption across your organization. Trigger with phrases like "coderabbit monitoring", "coderabbit metrics", "coderabbit observability", "monitor coderabbit", "coderabbit alerts", "coderabbit dashboard".
clickup-observability
Monitor ClickUp API integrations with metrics, tracing, structured logging, and alerting using Prometheus, OpenTelemetry, and Grafana. Trigger: "clickup monitoring", "clickup metrics", "clickup observability", "monitor clickup", "clickup alerts", "clickup tracing", "clickup dashboard".
clickhouse-observability
Monitor ClickHouse with Prometheus metrics, Grafana dashboards, system table queries, and alerting for query performance, merge health, and resource usage. Use when setting up ClickHouse monitoring, building Grafana dashboards, or configuring alerts for production ClickHouse deployments. Trigger: "clickhouse monitoring", "clickhouse metrics", "clickhouse Grafana", "clickhouse observability", "monitor clickhouse", "clickhouse Prometheus".
clerk-observability
Implement monitoring, logging, and observability for Clerk authentication. Use when setting up monitoring, debugging auth issues in production, or implementing audit logging. Trigger with phrases like "clerk monitoring", "clerk logging", "clerk observability", "clerk metrics", "clerk audit log".