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.
npx skills add pjt222/agent-almanac --skill correlate-observability-signalsgit clone --depth 1 https://github.com/pjt222/agent-almanacWrote 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.
[](https://agentmods.dev/skills/pjt222/agent-almanac/correlate-observability-signals)<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.
<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>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once 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 |
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.
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}
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.
- 7d ago First seen · 460 lines · 83 tokens per session scan A f0db3bda3a5e
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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