gear-diagnosis

gear-diagnosis is a skill for Claude Code from LGDiMaggio/predictive-maintenance-mcp. It costs 78 tokens per session (1,203 once invoked), scanned A, original, no licence file.

A workflow for diagnosing gear and gearbox faults from vibration data, including gear-mesh and sideband patterns. Gear mesh is the repeated contact between gear teeth; sidebands are nearby frequency patterns that can indicate a fault.

In plain words
What is it for?
Use it for gear-fault diagnosis, gearbox analysis, gear-mesh-frequency checks, tooth-damage detection, and sideband analysis.
Why use it?
It helps connect vibration patterns with problems such as damaged gear teeth or other gear defects.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the predictive-maintenance plugin — 8 skills, 3 commands, 2 agents shipped together

Good fit Use it for gear-fault diagnosis, gearbox analysis, gear-mesh-frequency checks, tooth-damage detection, and sideband analysis.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis
Install

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.

Any agent
npx skills add LGDiMaggio/predictive-maintenance-mcp --skill gear-diagnosis
Clone the repo
git clone --depth 1 https://github.com/LGDiMaggio/predictive-maintenance-mcp

Made for: Claude Code.

Or install predictive-maintenance, the plugin that ships this one along with the rest of its 8 skills, 3 commands, 2 agents.

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 gear-diagnosis

README.md
[![agentmods](https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis/github.svg)](https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis)
Your own site
<a href="https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis"><img src="https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis/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 gear-diagnosis

Your own site · 80×15
<a href="https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis"><img src="https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/gear-diagnosis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,203 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 unknown 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 $0.00078 $0.01203
Opus 5 $0.00039 $0.00602
Sonnet 5 $0.00016 $0.00241
Haiku 4.5 $0.00008 $0.00120

Measured 11d ago against content hash cc894ab2d2b6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

gear-diagnosis 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 11d 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.

plugin/skills/gear-diagnosis/SKILL.md · 129 lines

The source is not reproduced here

A licence we could not identify

The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.

Read it on GitHub

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. 11d ago First seen · 129 lines · 78 tokens per session scan A cc894ab2d2b6

Subscribe to this mod's changes

gear-diagnosis is a skill published in the GitHub repository LGDiMaggio/predictive-maintenance-mcp (83 stars, last pushed 21d ago), with no licence file. It adds 78 tokens to every session and 1,203 once invoked, about $0.0004 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.

Related

Other skills, from other repositories

offensive-wifi

Wireless / 802.11 attack methodology for red team engagements and wireless security assessments. Covers monitor-mode setup, WPA/WPA2-PSK handshake capture and PMKID attacks, WPA3 SAE downgrade and Dragonblood, WPA-Enterprise (EAP) attacks (MSCHAPv2 cracking, EAP-TLS cert theft, evil-twin RADIUS), Karma / Known Beacons…

SnailSploit/Claude-Red · 183 tokens

offensive-lorawan-sub-ghz

LoRaWAN and sub-GHz (433 / 868 / 915 MHz) attack methodology — LoRaWAN ABP/OTAA join attack, network/session key reuse, frame counter replay, downlink injection on TTN/Helium-style networks, sub-GHz protocol replay (KeeLoq garage doors, fixed-code remotes, TPMS spoofing, smart plug telemetry), HackRF / RTL-SDR /…

SnailSploit/Claude-Red · 163 tokens

offensive-wifi-recon

Wi-Fi reconnaissance methodology — adapter selection, monitor mode and packet injection setup, regulatory domain handling, multi-band airspace mapping, hidden SSID discovery, BSSID/ESSID/channel/PMF/encryption fingerprinting, client probe analysis, vendor OUI lookup, war-driving with Kismet/airodump-ng/Wigle, and…

SnailSploit/Claude-Red · 130 tokens

offensive-z-wave

Z-Wave attack methodology — sniffing with Z-Force / EZ-Wave / RTL-SDR + ZniffMobile, S0 (legacy) network-key derivation flaw and key reuse, S2 (modern) ECDH commissioning analysis, replay/injection on unauthenticated nodes, default-key brute-force on test deployments, and home-automation hub pivots. Use when targeting…

SnailSploit/Claude-Red · 113 tokens

offensive-zigbee-thread-matter

Zigbee, Thread, and Matter mesh-protocol attack methodology — IEEE 802.15.4 sniffing with TI CC2531 / CC2540 / Sonoff Zigbee Dongle E, KillerBee toolkit, Touchlink commissioning abuse with the well-known transport key, replay/injection attacks, Zigbee Cluster Library command abuse for door locks and bulbs, Thread…

SnailSploit/Claude-Red · 126 tokens

shodan-reconnaissance

This skill should be used when the user asks to "search for exposed devices on the internet," "perform Shodan reconnaissance," "find vulnerable services using Shodan," "scan IP ranges with Shodan," or "discover IoT devices and open ports." It provides comprehensive guidance for using Shodan's search engine, CLI, and…

zebbern/claude-code-guide · 79 tokens