dd-logs

Log-Management - Suche, Archive, Metriken und Kostenkontrolle.

npx skills add https://github.com/datadog-labs/agent-skills --skill dd-logs

Datadog Logs

Search, process, and archive logs with cost awareness.

Prerequisites

Datadog Pup should already be installed. See Setup Pup if not.

Command Execution Order (Token-Efficient)

For scoped commands, use this order:

  1. Check context first (prior outputs, conversation, saved values).
  2. If a required value is missing, run a discovery command first.
  3. If still ambiguous, ask the user to confirm.
  4. Then run the target command.
  5. Avoid speculative commands likely to fail.

Quick Start

pup auth login

Search Logs

# Basic search
pup logs search --query="status:error" --from="1h"

# With filters
pup logs search --query="service:api status:error" --from="1h" --limit 100

# JSON output
pup logs search --query="@http.status_code:>=500" --from="1h"

Search Syntax

QueryMeaning
errorFull-text search
status:errorTag equals
@http.status_code:500Attribute equals
@http.status_code:>=400Numeric range
service:api AND env:prodBoolean
@message:*timeout*Wildcard

Configuration APIs

Available log configuration commands in pup 0.42.0:

# List log archives
pup logs archives list

# List log restriction queries
pup logs restriction-queries list

# List custom log destinations
pup logs custom-destinations list

Common Processors

{
  "name": "API Logs",
  "filter": {"query": "service:api"},
  "processors": [
    {
      "type": "grok-parser",
      "name": "Parse nginx",
      "source": "message",
      "grok": {"match_rules": "%{IPORHOST:client_ip} %{DATA:method} %{DATA:path} %{NUMBER:status}"}
    },
    {
      "type": "status-remapper",
      "name": "Set severity",
      "sources": ["level", "severity"]
    },
    {
      "type": "attribute-remapper",
      "name": "Remap user_id",
      "sources": ["user_id"],
      "target": "usr.id"
    }
  ]
}

Exclusion Filters (Cost Control)

Index only what matters:

{
  "name": "Drop debug logs",
  "filter": {"query": "status:debug"},
  "is_enabled": true
}

High-Volume Exclusions

# Find noisiest log sources
pup logs search --query="*" --from="1h" | jq 'group_by(.service) | map({service: .[0].service, count: length}) | sort_by(-.count)[:10]'
ExcludeQuery
Health checks@http.url:"/health" OR @http.url:"/ready"
Debug logsstatus:debug
Static assets@http.url:*.css OR @http.url:*.js
Heartbeats@message:*heartbeat*

Archives

Store logs cheaply for compliance:

# List archives
pup logs archives list

# Archive config (S3 example)
{
  "name": "compliance-archive",
  "query": "*",
  "destination": {
    "type": "s3",
    "bucket": "my-logs-archive",
    "path": "/datadog"
  },
  "rehydration_tags": ["team:platform"]
}

Rehydrate (Restore)

# No `pup logs rehydrate` command in pup 0.42.0.
# Use Datadog UI/API for rehydration workflows.

Log-Based Metrics

Create metrics from logs (cheaper than indexing):

# List log-based metrics
pup logs metrics list

# Get one metric by ID
pup logs metrics get api.errors.count

Cardinality warning: Group by bounded values only.

Sensitive Data

Scrubbing Rules

{
  "type": "hash-remapper",
  "name": "Hash emails",
  "sources": ["email", "@user.email"]
}

Never Log

# In your app - sanitize before sending
import re

def sanitize_log(message: str) -> str:
    # Remove credit cards
    message = re.sub(r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b', '[REDACTED]', message)
    # Remove SSNs
    message = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED]', message)
    return message

Troubleshooting

ProblemFix
Logs not appearingCheck agent, pipeline filters
High costsAdd exclusion filters
Search slowNarrow time range, use indexes
Missing attributesCheck grok parser

References/Documentation

Mehr Skills von datadog-labs

dd-audit
datadog-labs
Prüfpfad-Untersuchungen – wer was geändert hat, Schlüsselkompromittierung, Ursache von Kostenanstiegen, Compliance-Nachweise (SOC 2/PCI) und Prüfung von KI-Aktivitäten.
official
agent-install
datadog-labs
Installieren Sie den Datadog Agent auf Kubernetes mit dem Datadog Operator – erforderlich, bevor Single Step Instrumentation (SSI) aktiviert werden kann, das automatisch…
official
agent-observability-auto-experiment
datadog-labs
Führe einen iterativen Code-Verbesserungs-Hill-Climb gegen echte Datadog-LLM-Obs-Daten lokal mit Claude Code als Agent durch. Etabliert eine Baseline-Evaluierung, macht eine…
official
agent-observability-eval-bootstrap
datadog-labs
Bootstrappen Sie Evaluatoren aus Produktionstraces — standardmäßig werden Online-LLM-Judge-Evaluatoren vorgeschlagen, und nach Ihrer Bestätigung werden sie in Datadog als deaktivierte Entwürfe erstellt…
official
agent-observability-eval-pipeline
datadog-labs
End-to-End-Agent-Observability-Pipeline für eine instrumentierte ml_app — Produktions-Traces klassifizieren, Fehlerursachen ermitteln, Evaluatoren bootstrapen, dann (optional)…
official
agent-observability-experiment-analyzer
datadog-labs
Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says "analyze experiment", "compare…
official
agent-observability-replay-trace
datadog-labs
Verwenden Sie dies, wenn ein Entwickler an EINER bestimmten Agent Observability / LLM Obs-Trace iterieren möchte, deren Ausgabe ihm nicht gefallen hat — indem er diese Trace erneut gegen seine…
official
agent-observability-trace-rca
datadog-labs
Ursachenanalyse bei Produktions-LLM-Traces. Diagnostiziert, warum eine LLM-Anwendung fehlschlägt – arbeitet mit Eval-Judge-Urteilen, Laufzeitfehlern oder strukturellen…
official