correlate-observability-signals

correlate-observability-signals is a skill for Claude Code from pjt222/agent-almanac. It costs 83 tokens per session (3,136 once invoked), scanned A, original, MIT.

A guide to connecting metrics, logs, and traces for debugging. Metrics measure system behavior, logs record events, and traces follow a request across services.

In plain words
What is it for?
Use it to propagate trace IDs, connect logs with requests, build observability dashboards, and support root-cause analysis.
Why use it?
It helps investigate incidents that cross multiple systems by bringing related evidence together instead of leaving it split across separate tools.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the agent-almanac plugin — 122 skills, 76 agents shipped together

Good fit Use it to propagate trace IDs, connect logs with requests, build observability dashboards, and support root-cause analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/pjt222/agent-almanac/correlate-observability-signals
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add pjt222/agent-almanac --skill correlate-observability-signals
Clone the repo
git clone --depth 1 https://github.com/pjt222/agent-almanac

Made for: Claude Code.

Or install agent-almanac, the plugin that ships this one along with the rest of its 122 skills, 76 agents.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for correlate-observability-signals

README.md
[![agentmods](https://agentmods.dev/badge/skills/pjt222/agent-almanac/correlate-observability-signals/github.svg)](https://agentmods.dev/skills/pjt222/agent-almanac/correlate-observability-signals)
Your own site
<a href="https://agentmods.dev/skills/pjt222/agent-almanac/correlate-observability-signals"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/correlate-observability-signals/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for correlate-observability-signals

Your own site · 80×15
<a href="https://agentmods.dev/skills/pjt222/agent-almanac/correlate-observability-signals"><img src="https://agentmods.dev/badge/skills/pjt222/agent-almanac/correlate-observability-signals.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 83 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,136 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00083 $0.03136
Opus 5 $0.00042 $0.01568
Sonnet 5 $0.00017 $0.00627
Haiku 4.5 $0.00008 $0.00314

Measured 7d ago against content hash f0db3bda3a5e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

correlate-observability-signals scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

i18n/caveman-lite/skills/correlate-observability-signals/SKILL.md · 460 lines

How it starts

The opening of the file, as written. The whole thing — 460 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Correlate Observability Signals

Connect metrics, logs, and traces for unified debugging across the three pillars of observability.

When to Use

  • Investigating complex incidents that span multiple systems
  • Reducing MTTR (mean time to resolution)
  • Building unified observability dashboards
  • Implementing distributed tracing
  • Moving from siloed tools to unified observability

Inputs

  • Required: Prometheus (metrics)
  • Required: Log aggregation system (Loki, Elasticsearch, CloudWatch)
  • Required: Distributed tracing backend (Tempo, Jaeger, Zipkin)
  • Optional: Grafana for unified visualization
  • Optional: OpenTelemetry instrumentation

Procedure

See Extended Examples for complete configuration files and templates.

Step 1: Implement Trace Context Propagation

Add trace IDs to all logs and metrics using OpenTelemetry:

// Go example: Propagate trace context to logs
package main

import (
    "context"
    "log"

    "go.opentelemetry.io/otel"
    "go.opentelemetry.io/otel/trace"
)

func handleRequest(ctx context.Context, userID string) {
    // Extract trace context
    span := trace.SpanFromContext(ctx)
    traceID := span.SpanContext().TraceID().String()

    // Include trace ID in structured logs
    log.Printf("trace_id=%s user_id=%s action=process_request", traceID, userID)

    // Business logic here
    processData(ctx, userID)
}

func processData(ctx context.Context, userID string) {
    tracer := otel.Tracer("my-service")
    ctx, span := tracer.Start(ctx, "processData")
    defer span.End()

    traceID := span.SpanContext().TraceID().String()
    log.Printf("trace_id=%s user_id=%s action=process_data", traceID, userID)

    // More work
}

Python example:

# Python: Flask with OpenTelemetry
from flask import Flask, request
from opentelemetry import trace
from opentelemetry.instrumentation.flask import FlaskInstrumentor
import logging

app = Flask(__name__)
FlaskInstrumentor().instrument_app(app)

logging.basicConfig(
    format='%(asctime)s trace_id=%(otelTraceID)s span_id=%(otelSpanID)s %(message)s',
    level=logging.INFO
)

@app.route('/api/users/<user_id>')
def get_user(user_id):
    span = trace.get_current_span()
    trace_id = format(span.get_span_context().trace_id, '032x')

    logging.info(f"Fetching user {user_id}", extra={
        'otelTraceID': trace_id,
        'otelSpanID': format(span.get_span_context().span_id, '016x')
    })

    # Business logic
    return {"user_id": user_id}

Read the full file on GitHub · 460 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 7d ago First seen · 460 lines · 83 tokens per session scan A f0db3bda3a5e

Subscribe to this mod's changes

correlate-observability-signals is a skill published in the GitHub repository pjt222/agent-almanac (32 stars, last pushed today), licensed MIT. It adds 83 tokens to every session and 3,136 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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