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.
curl -O https://raw.githubusercontent.com/ryanmat/mcp-server-logicmonitor/main/.claude/skills/lm-remediate/SKILL.mdgit clone --depth 1 https://github.com/ryanmat/mcp-server-logicmonitorWrote 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/ryanmat/mcp-server-logicmonitor/lm-remediate)<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="Measured on agentmods" 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 | $0.00020 | $0.01764 |
| Opus 5 | $0.00010 | $0.00882 |
| Sonnet 5 | $0.00004 | $0.00353 |
| Haiku 4.5 | $0.00002 | $0.00176 |
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 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 — 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_detailswith that ID. - String value -- Treat as a device name. Call
get_alertswith 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.
- Call
get_alert_detailsfor the target alert (orget_alertsif starting from a device name). - Call
get_devicefor 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.
- Call
score_device_healthfor the affected device. - Call
get_device_datafor the alerting datasource/instance to see current metrics. - Call
correlate_alertsto find related alerts that may share a root cause. - Call
correlate_changesto 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)
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 · 211 lines · 20 tokens per session scan A 41a8b416e1a5
lm-remediate is a skill published in the GitHub repository ryanmat/mcp-server-logicmonitor (0 stars, last pushed 27d ago), licensed MIT. It adds 20 tokens to every session and 1,764 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 2026-08-31.
Other skills, from other repositories
debug-optimize-lcp
Guides debugging and optimizing Largest Contentful Paint (LCP) using Chrome DevTools MCP tools. Use this skill whenever the user asks about LCP performance, slow page loads, Core Web Vitals optimization, or wants to understand why their page's main content takes too long to appear. Also use when the user mentions…
systematic-debugging
Use when debugging a failing test, build error, or runtime issue that isn't immediately obvious. Guides a 4-phase root cause analysis instead of random fix attempts.
diagnose
Trace from a reproduced symptom to the source code that causes it. Pin the specific file and approximate line, rate confidence in the cause and clarity of the fix independently, and always propose a concrete fix.
repro-admin
Reproduce an EmDash admin UI bug. Attach a container, start the demo dev server, drive the admin with agent-browser using the dev-bypass session, and capture the reproduction as screenshots plus a replayable transcript.
log-error-digest
Analyze log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports. Supports JSON, syslog, and Nginx formats with automatic detection. Use when a user uploads a .log file and asks to analyze errors, find patterns, debug issues, or…
byted-util-volcengine-detect-retry
An orchestration workflow for Volcengine Cloud Detect, a service that checks websites or network endpoints from test locations.