Monitoring Stack Deployer

Trust Score 58/100

Deploy and configure production-ready monitoring stacks (Prometheus, Grafana, Datadog) with collectors, dashboards, and alerting rules for Kubernetes, Docker, o

triggers:deploy monitoring stacksetup prometheusconfigure grafanainstall datadog agentcreate dashboardsdefine alerts

GitHub SKILL.md

What it does

Automates guidance and example configs for deploying monitoring stacks including Prometheus, Grafana, and Datadog. Provides platform-specific instructions, collectors/exporters deployment patterns, scraping configuration, storage/retention guidance, dashboard templates, and alerting rules for production systems.

When to use it

Use this skill when you need to provision observability for an application or cluster — e.g., set up Prometheus+Grafana on Kubernetes, install Datadog agents across hosts, create dashboards for key KPIs, or define alerting for SRE workflows. Triggers include deploying monitoring, configuring scraping, adding dashboards, or creating alert rules.

What's included

Scripts: Yes — repo contains scripts and monitoring examples for generating config and manifests.
References: Yes — links to Prometheus and Grafana docs and example dashboards in the repository.
Instructions: Step-by-step guidance to select platform, deploy collectors, configure scraping, set storage retention, build dashboards, and define alerts; includes YAML/JSON examples and troubleshooting notes.

Compatible agents

Likely compatible with CLI and code-oriented agents (Copilot/Codex/Claude Code) and MCP-style tool integrations that can run repo scripts for deployment and config generation.

Audit Summary

Monitoring stack deployer skill with promising topic but entirely boilerplate implementation. All three scripts (deploy_prometheus.sh, deploy_grafana.sh, deploy_datadog_agent.sh) are identical Python files misnamed with .sh extensions, causing immediate bash syntax errors. They contain no actual monitoring deployment logic — just a generic file-copy Deployer class. SKILL.md has broken YAML frontmatter and provides only high-level steps without actionable detail.

Watch Out

Scripts use .sh extension but contain Python code — will crash when executed as shell scripts
All 3 deploy scripts are identical copy-paste with different filenames and descriptions
Dict type used without importing from typing — would fail even if run as Python
SKILL.md has malformed frontmatter (markdown code fence embedded in YAML)

Notes

Clearly AI-generated boilerplate skill from the pi_agent_rust test suite. No real functionality — the scripts copy files from source to target but contain zero monitoring-specific logic. The SKILL.md is a template with placeholder examples. Not malicious, just useless.

Information

Repository: pi_agent_rust
Stars: 1,284

Trust Score

Overall58

Security88

Code Quality28

Architecture30

Usefulness25

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