A workflow for predicting how equipment may deteriorate and estimating its Remaining Useful Life (RUL), meaning how long it may continue working before failure or required maintenance.

Use it for trend analysis, detecting when degradation begins, failure prediction, and RUL estimation from vibration data.

It helps turn changes in equipment condition into an assessment of degradation and possible failure timing.

prognostics — 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 prognostics

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

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Your own site

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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

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Per session 154 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 2,070 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	$0.00154	$0.02070
Opus 5.5	$0.00062	$0.00828
Sonnet 5.5	$0.00031	$0.00414
Haiku 4.5	$0.00015	$0.00207

Measured 11d ago against content hash f2e8d503f7f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

prognostics 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/prognostics/SKILL.md · 175 lines

How it starts

The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Prognostic Assessment (ISO 13374 Block 5)

Screen degradation trends within a recording and estimate Remaining Useful Life from repeated measurements over time. Three distinct scopes; do not confuse them:

Within-recording screening (analyze_signal_trend): is this single recording stationary, or does a feature drift inside it? This is a screening result, NOT a prognosis.
Prognosis (estimate_rul): extrapolation of a degradation trend across MULTIPLE measurements of the same machine taken days/weeks/months apart. RUL from a single recording is physically meaningless and the tool refuses it.
Indicator projection (forecast_asset_indicator, not yet released): where one indicator's values are heading over the next hours or days. A projection, NOT a prognosis.

RUL estimates are planning input for the maintenance engineer — never a guarantee, never a substitute for engineering judgment.

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

Workflow A — Within-Recording Screening

Step 1 — Signal Discovery

Call list_signals(scope="memory") for loaded signal_ids, or list_signals(scope="disk") and load_signal(filepath="<file>") to load a recording. This works best on long monitoring recordings or run-to-failure data where degradation evolves within the file. 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.

Step 2 — Trend + Onset Screening (one call)

Call analyze_signal_trend(signal_id="<id>", feature_name="rms").

The tool segments the recording, extracts the chosen feature per segment, tests trend significance (slope p-value, not bare R-squared), and detects the first post-baseline segment exceeding baseline mean + N sigma — trend AND onset in one result.

Parameters:

feature_name: "rms" (default), "kurtosis", "crest_factor", "peak_to_peak", ...
segment_duration (default 0.1 s) and overlap_ratio (default 0.5)
onset_threshold_sigma: baseline standard deviations that trigger onset detection (default 3.0; lower to 2.0 for more sensitivity, with more false positives)

Read the full file on GitHub · 175 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.

11d ago Changed · +30 lines · +34 tokens per session f2e8d503f7f3
2mo ago First seen · 145 lines · 120 tokens per session scan A 979345505eb3

Subscribe to this mod's changes

prognostics is a skill published in the GitHub repository LGDiMaggio/predictive-maintenance-mcp (109 stars, last pushed yesterday), licensed MIT. It adds 154 tokens to every session and 2,070 once invoked, about $0.0006 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

prognostics next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
prognosticsLGDiMaggio/predictive-maintenance-mcp	Skill	109	yesterday	MIT	A	154	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