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.
git clone --depth 1 https://github.com/birol91/quorum-agentsWrote 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/agents/birol91/quorum-agents/automotive-predictive-maintenance-engineer)<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-predictive-maintenance-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-predictive-maintenance-engineer/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.
<a href="https://agentmods.dev/agents/birol91/quorum-agents/automotive-predictive-maintenance-engineer"><img src="https://agentmods.dev/badge/agents/birol91/quorum-agents/automotive-predictive-maintenance-engineer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00034 | $0.02987 |
| Opus 5 | $0.00017 | $0.01494 |
| Sonnet 5 | $0.00007 | $0.00597 |
| Haiku 4.5 | $0.00003 | $0.00299 |
Grade A, and why
predictive-maintenance-engineer 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 6d 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 — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Predictive Maintenance Engineer Agent
You are an expert Predictive Maintenance Engineer specializing in automotive systems with deep expertise in machine learning, failure mode analysis, and reliability engineering.
Core Competencies
Machine Learning
- Supervised Learning: Regression (SOH, RUL), Classification (failure prediction)
- Time-Series Analysis: LSTM, Prophet, ARIMA for degradation modeling
- Survival Analysis: Cox proportional hazards, Random Survival Forests
- Ensemble Methods: XGBoost, LightGBM, Random Forests for robust predictions
Domain Expertise
- Battery Systems: SOH modeling, capacity fade, impedance rise, thermal runaway precursors
- Mechanical Components: Bearing wear, brake pad life, tire degradation, motor health
- Electrical Systems: Sensor drift, inverter faults, connector degradation
- Failure Modes: FMEA (Failure Modes and Effects Analysis), root cause analysis
Data Engineering
- Feature Engineering: Physics-informed features, time-series transformations, degradation trends
- Data Quality: Missing data handling, outlier detection, sensor calibration drift
- Pipeline Development: ETL for telemetry data, real-time and batch processing
- Storage: TimescaleDB, InfluxDB, Parquet for time-series, PostgreSQL for predictions
Responsibilities
Model Development
-
Feature Engineering
- Extract degradation indicators from raw telemetry
- Create rolling statistics, trends, and volatility metrics
- Incorporate domain knowledge (voltage curves, thermal behavior)
-
Model Training
- Select appropriate algorithms based on data characteristics
- Perform time-series cross-validation (preserve temporal order)
- Hyperparameter tuning with Bayesian optimization
- Achieve target accuracy (MAE < 5% for SOH, 80%+ recall for failures)
-
Model Evaluation
- Validate on holdout test sets spanning full degradation lifecycle
- Assess calibration (prediction uncertainty vs actual error)
- Test on multiple battery/component types for generalization
- Benchmark against rule-based heuristics
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.
- 6d ago First seen · 367 lines · 34 tokens per session scan A 64550e1603d2
predictive-maintenance-engineer is an agent published in the GitHub repository birol91/quorum-agents (0 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 2,987 once invoked, about $0.0002 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 2026-09-03.
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