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Official agent skill

Dd Monitors

by DataDog in DataDog/pup

Monitor management - create, update, mute, and alerting best practices.

OfficialApache-2.0Auto-check passedDevOps & Cloud

Install Dd Monitors

skills CLI
$ npx skills add DataDog/pup --skill dd-monitors -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install DataDog/pup dd-monitors --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/DataDog/pup.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/dd-monitors .claude/skills/dd-monitors && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dd-monitors
GitHub stars
1k
Token cost
~1.1k tokens
SKILL.md length
191 words
Files
1
Skills in repo
12
Repo updated
First seen
Licence
Apache-2.0

At a glance

Monitor management - create, update, mute, and alerting best practices.

  • Works in 4 steps: Avoid Alert Fatigue → Use Proper Scoping → Set Recovery Thresholds → …
  • DevOps & Cloud work in your project
  • SKILL.md covers Prerequisites, Quick Start, Common Operations and ⚠️ Monitor Creation Best…, plus 6 more sections
  • Calls jq and cargo

What it does

Dd Monitors is an agent skill from DataDog/pup, published by the product's own GitHub organization. Monitor management - create, update, mute, and alerting best practices.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud. It works with Datadog. The repository describes itself as: Give your AI agent a Pup — a CLI companion with 200+ commands across 33+ Datadog products. The licence is Apache-2.0.

When your agent uses it

  • DevOps & Cloud work in your project

Example prompts

  • “/dd-monitors”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Avoid Alert Fatigue
  2. Use Proper Scoping
  3. Set Recovery Thresholds
  4. Include Context in Messages

What it can do on your machine

Read from SKILL.md and the folder at commit 6a3c662. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • jq
    • cargo

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • docs.datadoghq.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dd Monitors loads about 1.1k tokens when it runs. Until then it costs about 21 tokens; SKILL.md has 191 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~21
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from DataDog/pup at commit 6a3c662, republished under its Apache-2.0 licence (© DataDog). 191 words, ~1,135 tokens.

Download SKILL.mdSave it as .claude/skills/dd-monitors/SKILL.md (or your agent's skills folder).
name
dd-monitors
description
Monitor management - create, update, mute, and alerting best practices.
metadata.version
1.0.0
metadata.author
datadog-labs
metadata.repository
https://github.com/datadog-labs/agent-skills
metadata.tags
datadog,monitors,alerting,alerts,dd-monitors
metadata.globs
**/datadog*.yaml,**/*monitor*
metadata.alwaysApply
false

Datadog Monitors

Create, manage, and maintain monitors for alerting.

Prerequisites

This requires the pup binary in your path.

pup - cargo install --git https://github.com/DataDog/pup

Quick Start

bash
pup auth login

Common Operations

List Monitors
bash
pup monitors list
pup monitors list --tags "team:platform"
pup monitors search --query "status:Alert"
Get Monitor
bash
pup monitors get <id>
Create Monitor
bash
pup monitors create --file monitor.json
Mute/Unmute
bash
# Mute with duration
pup monitors update 12345 --file monitor-muted.json

# Or mute with specific end time
pup monitors update 12345 --file monitor-muted-until.json

# Unmute
pup monitors update 12345 --file monitor-unmuted.json

⚠️ Monitor Creation Best Practices

1. Avoid Alert Fatigue
RuleWhy
No flapping alertsUse last_Xm not last_1m
Meaningful thresholdsBased on SLOs, not guesses
Actionable alertsIf no action needed, don't alert
Include runbook@runbook-url in message
python
# WRONG - will flap constantly
query = "avg(last_1m):avg:system.cpu.user{*} > 50"  # ❌ Too sensitive

# CORRECT - stable alerting
query = "avg(last_5m):avg:system.cpu.user{env:prod} by {host} > 80"  # ✅ Reasonable window
2. Use Proper Scoping
python
# WRONG - alerts on everything
query = "avg(last_5m):avg:system.cpu.user{*} > 80"  # ❌ No scope

# CORRECT - scoped to what matters
query = "avg(last_5m):avg:system.cpu.user{env:prod,service:api} by {host} > 80"  # ✅
3. Set Recovery Thresholds
python
monitor = {
    "query": "avg(last_5m):avg:system.cpu.user{env:prod} > 80",
    "options": {
        "thresholds": {
            "critical": 80,
            "critical_recovery": 70,  # ✅ Prevents flapping
            "warning": 60,
            "warning_recovery": 50
        }
    }
}
4. Include Context in Messages
python
message = """
## High CPU Alert

Host: {{host.name}}
Current Value: {{value}}
Threshold: {{threshold}}

### Runbook
1. Check top processes: `ssh {{host.name}} 'top -bn1 | head -20'`
2. Check recent deploys
3. Scale if needed

@slack-ops @pagerduty-oncall
"""

⚠️ NEVER Delete Monitors Directly

Use safe deletion workflow (same as dashboards):

python
def safe_mark_monitor_for_deletion(monitor_id: str, client) -> bool:
    """Mark monitor instead of deleting."""
    monitor = client.get_monitor(monitor_id)
    name = monitor.get("name", "")
    
    if "[MARKED FOR DELETION]" in name:
        print(f"Already marked: {name}")
        return False
    
    new_name = f"[MARKED FOR DELETION] {name}"
    client.update_monitor(monitor_id, {"name": new_name})
    print(f"✓ Marked: {new_name}")
    return True

Monitor Types

TypeUse Case
metric alertCPU, memory, custom metrics
query alertComplex metric queries
service checkAgent check status
event alertEvent stream patterns
log alertLog pattern matching
compositeCombine multiple monitors
apmAPM metrics

Audit Monitors

bash
# Find monitors without owners
pup monitors list | jq '.[] | select(.tags | contains(["team:"]) | not) | {id, name}'

# Find noisy monitors (high alert count)
pup monitors list | jq 'sort_by(.overall_state_modified) | .[:10] | .[] | {id, name, status: .overall_state}'

Downtime vs Muting

UseWhen
Mute monitorQuick one-off, < 1 hour
DowntimeScheduled maintenance, recurring
bash
# Downtime (preferred)
pup downtime create --file downtime.json

Failure Handling

ProblemFix
Alert not firingCheck query returns data, thresholds
Too many alertsIncrease window, add recovery threshold
No data alertsCheck agent connectivity, metric exists
Auth errorpup auth refresh

References

© DataDog, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/dd-monitors of DataDog/pup.

Open the folder on GitHubat commit 6a3c662

Compare with similar skills

Dd Monitors next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dd Monitors compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dd Monitors this skillDataDog/pup1k—~1.1kAutomated safety check: PassApache-2.0
Follow PRDataDog/datadog-agent3.8k—~3.2kAutomated safety check: PassApache-2.0
Apm IntegrationsDataDog/dd-trace-js836—~3kAutomated safety check: PassCustom licence
Datadog Data Source GeneratorDataDog/terraform-provider-datadog468—~2.7kAutomated safety check: PassMPL-2.0
Apm IntegrationsDataDog/dd-trace-java736—~3.7kAutomated safety check: NotesApache-2.0
Tool ConnectorZhixiangLuo/10xProductivity478—~925Automated safety check: PassMIT

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Works with

Categories

Questions about Dd Monitors

What does Dd Monitors do?

Monitor management - create, update, mute, and alerting best practices. Dd Monitors is an agent skill from DataDog/pup, published by the product's own GitHub organization. Monitor management - create, update, mute, and alerting best practices.

When should I use Dd Monitors?

Dd Monitors fits situations like: devOps & Cloud work in your project.

How do I install Dd Monitors in Claude Code?

Run `npx skills add DataDog/pup --skill dd-monitors -a claude-code`. Or copy the skill folder (skills/dd-monitors in DataDog/pup) into .claude/skills/dd-monitors in your project. Claude Code loads it when a task matches its description.

How do I install Dd Monitors in Codex?

Run `npx skills add DataDog/pup --skill dd-monitors -a codex`. Or copy the skill folder (skills/dd-monitors in DataDog/pup) into .agents/skills/dd-monitors in your project. Codex loads it when a task matches its description.

Can I use Dd Monitors in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add DataDog/pup --skill dd-monitors -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dd-monitors, .gemini/skills/dd-monitors, .github/skills/dd-monitors and .opencode/skills/dd-monitors in your project.

What does Dd Monitors need to run?

Going by SKILL.md and its folder, Dd Monitors needs the command-line tools its instructions call (jq and cargo). Our summary lists: Python 3.

Does Dd Monitors access the network?

SKILL.md names 1 domain. As links in the text: docs.datadoghq.com. This is read from the text; nothing was executed.

Is Dd Monitors safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dd Monitors use?

Dd Monitors is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dd Monitors use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Dd Monitors?

Skills that share tags, products or a category with Dd Monitors: Follow PR (DataDog/datadog-agent, 3.8k stars), Apm Integrations (DataDog/dd-trace-js, 836 stars), Datadog Data Source Generator (DataDog/terraform-provider-datadog, 468 stars) and Apm Integrations (DataDog/dd-trace-java, 736 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dd Monitors?

DataDog (a GitHub organization, an official publisher) maintains it in DataDog/pup, which has 1,027 GitHub stars. The repository holds 12 skills in this directory. The repository was last updated on October 7, 2026.

Source: DataDog/pup on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.