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 bobmatnyc/claude-mpm-skills --skill datadoggit clone --depth 1 https://github.com/bobmatnyc/claude-mpm-skillsWrote 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/bobmatnyc/claude-mpm-skills/datadog)<a href="https://agentmods.dev/skills/bobmatnyc/claude-mpm-skills/datadog"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-skills/datadog/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/bobmatnyc/claude-mpm-skills/datadog"><img src="https://agentmods.dev/badge/skills/bobmatnyc/claude-mpm-skills/datadog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 74 Potential security issue detected. Manual review is recommended.Fix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium MCP Rug Pull · line 71 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00043 | $0.01657 |
| Opus 5 | $0.00022 | $0.00829 |
| Sonnet 5 | $0.00009 | $0.00331 |
| Haiku 4.5 | $0.00004 | $0.00166 |
Grade A, and why
datadog 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datadog Observability
Overview
Datadog is a SaaS observability platform providing unified monitoring across infrastructure, applications, logs, and user experience. It offers AI-powered anomaly detection, 1000+ integrations, and OpenTelemetry compatibility.
Core Capabilities:
- APM: Distributed tracing with automatic instrumentation for 8+ languages
- Infrastructure: Host, container, and cloud service monitoring
- Logs: Centralized collection with processing pipelines and 15-month retention
- Metrics: Custom metrics via DogStatsD with cardinality management
- Synthetics: Proactive API and browser testing from 29+ global locations
- RUM: Frontend performance with Core Web Vitals and session replay
When to Use This Skill
Activate when:
- Setting up production monitoring and observability
- Implementing distributed tracing across microservices
- Configuring log aggregation and analysis pipelines
- Creating custom metrics and dashboards
- Setting up alerting and anomaly detection
- Optimizing Datadog costs
Do not use when:
- Building with open-source stack (use Prometheus/Grafana instead)
- Cost is primary concern and budget is limited
- Need maximum customization over managed solution
Quick Start
1. Install Datadog Agent
Docker (simplest):
docker run -d --name dd-agent \
-e DD_API_KEY=<YOUR_API_KEY> \
-e DD_SITE="datadoghq.com" \
-v /var/run/docker.sock:/var/run/docker.sock:ro \
-v /proc/:/host/proc/:ro \
-v /sys/fs/cgroup/:/host/sys/fs/cgroup:ro \
gcr.io/datadoghq/agent:7
Kubernetes (Helm):
helm repo add datadog https://helm.datadoghq.com
helm install datadog-agent datadog/datadog \
--set datadog.apiKey=<YOUR_API_KEY> \
--set datadog.apm.enabled=true \
--set datadog.logs.enabled=true
2. Instrument Your Application
Python:
from ddtrace import tracer, patch_all
# Automatic instrumentation for common libraries
patch_all()
# Manual span for custom operations
with tracer.trace("custom.operation", service="my-service") as span:
span.set_tag("user.id", user_id)
# your code here
What ships with it
8 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 205 lines · 43 tokens per session scan A 53e26a52e8b8
datadog is a skill published in the GitHub repository bobmatnyc/claude-mpm-skills (74 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 1,657 once invoked, about $0.0002 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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