mcp-server-logicmonitor: Skill for Claude Code

.claude/skills/lm-remediate/SKILL.md

lm-remediate is a skill for Claude Code from ryanmat/mcp-server-logicmonitor. It costs 20 tokens per session (1,764 once invoked), scanned A, original, MIT.

A workflow for investigating LogicMonitor alerts and applying fixes through Ansible Automation Platform, a system for running automated infrastructure tasks. It connects alert details, diagnosis, playbook selection, remediation, and verification.

In plain words
What is it for?
Use it with an alert ID or device name to inspect active alerts, choose the relevant Ansible playbook, run remediation, and confirm the result.
Why use it?
It reduces the manual handoff between monitoring an infrastructure problem and running the approved automation to fix it. It checks the automation platform connection before proceeding.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

This is ryanmat/mcp-server-logicmonitor's own configuration. It tells Claude Code how to work on mcp-server-logicmonitor itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything mcp-server-logicmonitor configures →

Reuse

Borrowing it

Nothing to install: this file belongs to ryanmat/mcp-server-logicmonitor. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/ryanmat/mcp-server-logicmonitor/main/.claude/skills/lm-remediate/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/ryanmat/mcp-server-logicmonitor

Made for: Claude Code.

Wrote 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.

agentmods badge for lm-remediate

README.md
[![agentmods](https://agentmods.dev/badge/skills/ryanmat/mcp-server-logicmonitor/lm-remediate/github.svg)](https://agentmods.dev/skills/ryanmat/mcp-server-logicmonitor/lm-remediate)
Your own site
<a href="https://agentmods.dev/skills/ryanmat/mcp-server-logicmonitor/lm-remediate"><img src="https://agentmods.dev/badge/skills/ryanmat/mcp-server-logicmonitor/lm-remediate/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.

agentmods 80×15 button for lm-remediate

Your own site · 80×15
<a href="https://agentmods.dev/skills/ryanmat/mcp-server-logicmonitor/lm-remediate"><img src="https://agentmods.dev/badge/skills/ryanmat/mcp-server-logicmonitor/lm-remediate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 20 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,764 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00020 $0.01764
Opus 5.5 $0.00008 $0.00706
Sonnet 5.5 $0.00004 $0.00353
Haiku 4.5 $0.00002 $0.00176

Measured 2mo ago against content hash 41a8b416e1a5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

lm-remediate 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 2mo 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.

.claude/skills/lm-remediate/SKILL.md · 211 lines

How it starts

The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LogicMonitor Alert Remediation

You are a remediation operator for LogicMonitor + Ansible Automation Platform. Your job is to diagnose an alert, find the right playbook, execute a safe remediation, and verify the fix. You bridge monitoring (LogicMonitor) and automation (AAP) into a single structured workflow.

Argument Parsing

Parse the user's input to determine the remediation target:

  • Numeric value -- Treat as an alert ID. Call get_alert_details with that ID.
  • String value -- Treat as a device name. Call get_alerts with a device filter to find active alerts on that device.
  • No argument -- Ask the user what to remediate (alert ID or device name).

Workflow

Execute these steps in order. Present findings at each step before moving on.

Step 0: Connection Check

Call test_awx_connection to verify the Ansible Automation Platform is reachable.

  • If the connection fails, stop immediately. Report the failure and suggest checking AWX_URL, AWX_TOKEN, and network connectivity.
  • If the connection succeeds, proceed.

Step 1: Alert Context

Gather the alert and device context.

  1. Call get_alert_details for the target alert (or get_alerts if starting from a device name).
  2. Call get_device for the affected device.

Capture: alert ID, device ID, device name, datasource, datapoint, severity, alert value, threshold, alert start time.

If no active alerts are found for the target, report that and stop.

Step 2: Diagnosis

Build the remediation context with deeper analysis.

  1. Call score_device_health for the affected device.
  2. Call get_device_data for the alerting datasource/instance to see current metrics.
  3. Call correlate_alerts to find related alerts that may share a root cause.
  4. Call correlate_changes to check for recent changes that may have caused the issue.

Present a diagnosis summary:

  • Health score and contributing factors
  • Current metric values vs. thresholds
  • Correlated alerts (if any)
  • Recent changes (if any)

Read the full file on GitHub · 211 lines

Changes

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.

  1. 2mo ago First seen · 211 lines · 20 tokens per session scan A 41a8b416e1a5

Subscribe to this mod's changes

lm-remediate is a skill published in the GitHub repository ryanmat/mcp-server-logicmonitor (1 stars, last pushed 7d ago), licensed MIT. It adds 20 tokens to every session and 1,764 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.

Related

lm-remediate compared with skills from other repositories

lm-remediate next to 6 skills from other repositories
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