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Clade Observability

DevOps & Security
clade-observability

Clade 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".

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QUICK START

How to use this skill

Bring this guide into your coding agent with a prompt tailored to the tool you use.

  1. Open your project in Codex.
  2. Copy and paste the prompt below.
  3. Review files and risks before approving any action.
Prompt to paste
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

MetricSourceAlert Threshold
Error rateerror logs> 5% over 5 minutes
p95 latencyduration_ms> 10s (Sonnet)
Daily costestimated_cost sum> 2x daily average
429 rateerror_type = rate_limit> 10/minute
529 rateerror_type = overloaded> 5/minute
Token usageinput_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

ErrorCauseSolution
API ErrorCheck error type and status codeSee 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.