What is bearing-diagnosis?

A workflow for diagnosing bearing faults from vibration data. Bearings are machine parts that let shafts rotate while supporting their load.

Use it to check for bearing faults such as inner-race, outer-race, ball, or cage defects.

It helps identify possible bearing damage and narrow it to common fault locations.

bearing-diagnosis — Skill for Claude Code by LGDiMaggio

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 bearing-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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/bearing-diagnosis/github.svg)](https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/bearing-diagnosis)

Your own site

<a href="https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/bearing-diagnosis"><img src="https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/bearing-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/lgdimaggio/predictive-maintenance-mcp/bearing-diagnosis"><img src="https://agentmods.dev/badge/skills/lgdimaggio/predictive-maintenance-mcp/bearing-diagnosis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,838 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.

Model	Per session	Once invoked
Fable 5.1	$0.00093	$0.02838
Opus 5.5	$0.00037	$0.01135
Sonnet 5.5	$0.00019	$0.00568
Haiku 4.5	$0.00009	$0.00284

Measured 8d ago against content hash df42b5db30c7, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

bearing-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 8d 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/bearing-diagnosis/SKILL.md · 223 lines

How it starts

The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Bearing Fault Diagnosis

Evidence-based bearing fault detection using vibration signals. Orchestrate MCP tools in a precise diagnostic sequence (ISO 13374 blocks 1-4) with decision gates at each step. This workflow supports the judgment of a qualified vibration engineer — it never replaces it.

Prerequisite: The predictive-maintenance-mcp MCP server must be connected.

Never infer a fault from a signal id or filename — "OuterRaceFault" in an id is not evidence. Base every conclusion exclusively on tool outputs.

Workflow

Step 1: Signal Discovery (ISO 13374 Block 1)

Call list_signals(scope="memory") to check which signal_ids are already loaded, or list_signals(scope="disk") to browse loadable files under the data/signals/ directory. If the target signal is not loaded, load it:

Call load_signal(filepath="real_train/OuterRaceFault_1.csv", signal_unit="g")

The returned signal_id (derived from the relative path, e.g. real_train_OuterRaceFault_1) is the single handle for every later call.
For raw binary files (.bin/.raw/.dat), also declare sample_format and sampling_rate (or provide a companion <stem>_metadata.json) — see the signal-management skill.
Declare signal_unit ("g", "m/s2", "mm/s", or "m/s") if you know it — the ISO severity verdict in Step 6 is REFUSED without a declared unit. Units are never guessed. Ask the user; do not invent one.
Verify with get_signal_info(signal_id="<id>"): sampling rate, duration, declared unit, and any companion metadata (rpm, reference frequencies).

If the sampling rate is missing, STOP and ask the user — do not guess.

Step 2: Statistical Screening (Block 2)

Call analyze_statistics(signal_id="<id>").

Evaluate screening flags (excess kurtosis, Fisher convention):

Indicator	Threshold	Meaning
Kurtosis > 0	Mild	Non-Gaussian content (possible impulses)
Kurtosis > 3	Moderate	Significant impulsive content
Kurtosis > 6	Severe	Strong impulsiveness
Crest Factor > 4	Mild	Impulsiveness present
Crest Factor > 6	Strong	Strong impulsiveness

Read the full file on GitHub · 223 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.

8d ago Changed · +46 lines df42b5db30c7
14d ago Changed 56f5fab6ed51
2mo ago First seen · 177 lines · 93 tokens per session scan A 4359d8ea4b92

Subscribe to this mod's changes

bearing-diagnosis is a skill published in the GitHub repository LGDiMaggio/predictive-maintenance-mcp (109 stars, last pushed yesterday), licensed MIT. It adds 93 tokens to every session and 2,838 once invoked, about $0.0004 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 2026-08-30.

Hardware and IoT Scientific anomaly-detection bearing-fault-diagnosis claude-agents claude-ai condition-monitoring envelope-analysis

bearing-diagnosis next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
bearing-diagnosisLGDiMaggio/predictive-maintenance-mcp	Skill	109	yesterday	MIT	A	93	Claude Code
machine-vibration-monitoringLGDiMaggio/claude-stwinbox-diagnostics	Skill	10	7mo ago	Apache-2.0	A	107	Claude Code, Codex
vibration-fault-diagnosisLGDiMaggio/claude-stwinbox-diagnostics	Skill	10	7mo ago	Apache-2.0	A	117	Claude Code, Codex
ruview-applicationsruvnet/RuView	Skill	103k	yesterday	MIT	A	79	Claude Code
lab-hardware-cadK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	108	Claude Code
pylabrobotK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	49	Claude Code
pybammK-Dense-AI/scientific-agent-skills	Skill	56k	4d ago	MIT	A	70	Claude Code, Codex