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 faberlens/hardened-skills --skill datadog-hardenedgit clone --depth 1 https://github.com/faberlens/hardened-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/faberlens/hardened-skills/datadog-hardened)<a href="https://agentmods.dev/skills/faberlens/hardened-skills/datadog-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/datadog-hardened/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/faberlens/hardened-skills/datadog-hardened"><img src="https://agentmods.dev/badge/skills/faberlens/hardened-skills/datadog-hardened.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00028 | $0.01046 |
| Opus 5 | $0.00014 | $0.00523 |
| Sonnet 5 | $0.00006 | $0.00209 |
| Haiku 4.5 | $0.00003 | $0.00105 |
Grade A, and why
datadog-hardened 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 12d 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 — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.
🐕 Datadog
Datadog monitoring — manage monitors, dashboards, metrics, logs, events, and incidents via REST API
Requirements
| Variable | Required | Description |
|---|---|---|
DD_API_KEY |
✅ | API key from app.datadoghq.com |
DD_APP_KEY |
✅ | Application key |
Quick Start
# List monitors
python3 {{baseDir}}/scripts/datadog.py monitors --query <value> --tags <value>
# Get monitor
python3 {{baseDir}}/scripts/datadog.py monitor-get id <value>
# Create monitor
python3 {{baseDir}}/scripts/datadog.py monitor-create --name <value> --type <value> --query <value> --message <value>
# Update monitor
python3 {{baseDir}}/scripts/datadog.py monitor-update id <value> --name <value> --query <value>
# Delete monitor
python3 {{baseDir}}/scripts/datadog.py monitor-delete id <value>
# Mute monitor
python3 {{baseDir}}/scripts/datadog.py monitor-mute id <value>
# List dashboards
python3 {{baseDir}}/scripts/datadog.py dashboards
# Get dashboard
python3 {{baseDir}}/scripts/datadog.py dashboard-get id <value>
All Commands
| Command | Description |
|---|---|
monitors |
List monitors |
monitor-get |
Get monitor |
monitor-create |
Create monitor |
monitor-update |
Update monitor |
monitor-delete |
Delete monitor |
monitor-mute |
Mute monitor |
dashboards |
List dashboards |
dashboard-get |
Get dashboard |
dashboard-create |
Create dashboard |
dashboard-delete |
Delete dashboard |
metrics-search |
Search metrics |
metrics-query |
Query metrics |
events-list |
List events |
event-create |
Create event |
logs-search |
Search logs |
incidents |
List incidents |
incident-get |
Get incident |
hosts |
List hosts |
downtimes |
List downtimes |
downtime-create |
Create downtime |
slos |
List SLOs |
synthetics |
List synthetic tests |
users |
List users |
Output Format
All commands output JSON by default. Add --human for readable formatted output.
What ships with it
1 file 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.
- 12d ago First seen · 108 lines · 28 tokens per session scan A aac8744893f7
datadog-hardened is a skill published in the GitHub repository faberlens/hardened-skills (23 stars, last pushed 4mo ago), licensed MIT. It adds 28 tokens to every session and 1,046 once invoked, about $0.0001 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 2026-08-30.
Other skills, from other repositories
risk-metrics-calculation
Calculate portfolio risk metrics including VaR, CVaR, Sharpe, Sortino, and drawdown analysis. Use when measuring portfolio risk, implementing risk limits, or building risk monitoring systems.
employment-contract-templates
Create employment contracts, offer letters, and HR policy documents following legal best practices. Use when drafting employment agreements, creating HR policies, or standardizing employment documentation.
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.
calendar
Calendar and scheduling management. Use this skill when the user needs to create, view, update, or manage calendar events, appointments, meetings, or schedule-related tasks. Supports ICS file format, recurring events, and timezone handling.
paypal-integration
Integrate PayPal payment processing with support for express checkout, subscriptions, and refund management. Use when implementing PayPal payments, processing online transactions, or building e-commerce checkout flows.
rag-implementation
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.