Senior Data Scientist
Raidriar7170/hermes-skilleval
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.
Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework.
Install Mfg Predictive Maintenance
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenance --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/mfg-predictive-maintenance .claude/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .claude/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenanceType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenance --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/mfg-predictive-maintenance .agents/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .agents/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenance --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/mfg-predictive-maintenance .cursor/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .cursor/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/asgard-ai-platform/skills.git --path mfg-predictive-maintenance--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenance --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/mfg-predictive-maintenance .gemini/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .gemini/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenanceInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/mfg-predictive-maintenance .github/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .github/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install asgard-ai-platform/skills mfg-predictive-maintenance --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/mfg-predictive-maintenance .opencode/skills/mfg-predictive-maintenance && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "mfg-predictive-maintenance" agent skill from https://github.com/asgard-ai-platform/skills/tree/main/mfg-predictive-maintenance into .opencode/skills/mfg-predictive-maintenance/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "mfg-predictive-maintenance", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
mfg-predictive-maintenance- GitHub stars
- 242
- Token cost
- ~1.5k tokens
- SKILL.md length
- 411 words
- Files
- 4 (incl. references)
- Skills in repo
- 207
- Repo updated
- First seen
- Licence
- MIT
At a glance
Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework.
- The user needs to reduce unplanned downtime
- SKILL.md covers Framework, Output Format, Gotchas and References
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Transition from reactive to predictive maintenance
What it does
Mfg Predictive Maintenance is an agent skill from asgard-ai-platform/skills. Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework. Use this skill when the user needs to reduce unplanned downtime, transition from reactive to predictive maintenance, evaluate sensor/IoT investments, or estimate equipment failure probability — even if they say 'machines keep breaking down', 'when will this equipment fail', 'should we invest in IoT sensors', or 'reduce unplanned downtime'.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `examples/sample_scenario.md`, `references/rul-tutorial.md` and `references/sensor-guide.md`).
It sits in Data & Analytics, covering Machine learning and Forecasting and time series. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.
When your agent uses it
- The user needs to reduce unplanned downtime
- Transition from reactive to predictive maintenance
- Evaluate sensor/IoT investments
- Estimate equipment failure probability — even if they say machines keep breaking down
Example prompts
- “machines keep breaking down”
- “when will this equipment fail”
- “should we invest in IoT sensors”
- “/mfg-predictive-maintenance”
What it can do on your machine
Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Mfg Predictive Maintenance loads about 1.5k tokens when it runs, and up to ~8.2k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 411 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 411 words, ~1,520 tokens.
Download SKILL.mdSave it as
.claude/skills/mfg-predictive-maintenance/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.- name
- mfg-predictive-maintenance
- description
- Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework. Use this skill when the user needs to reduce unplanned downtime, transition from reactive to predictive maintenance, evaluate sensor/IoT investments, or estimate equipment failure probability — even if they say 'machines keep breaking down', 'when will this equipment fail', 'should we invest in IoT sensors', or 'reduce unplanned downtime'.
- metadata.category
- WP-03 製造業
- metadata.tags
- manufacturing, predictive-maintenance, iot, reliability
Predictive Maintenance
Framework
IRON LAW: Predictive > Preventive > Reactive (but each has its place)
Reactive (fix after failure): cheapest per-event, most expensive in downtime
Preventive (fix on schedule): prevents some failures, causes unnecessary maintenance
Predictive (fix based on condition): lowest total cost, requires sensor investment
Not ALL equipment justifies predictive maintenance. Apply to equipment where
unplanned downtime cost >> sensor investment cost.Maintenance Strategy Comparison
| Strategy | When to Maintain | Advantage | Disadvantage | Best For |
|---|---|---|---|---|
| Reactive | After failure | Zero upfront cost | Max downtime, safety risk | Non-critical, cheap-to-replace equipment |
| Preventive | On schedule (time/cycles) | Predictable, simple | Over-maintenance (replacing parts that still work) | Equipment with known wear patterns |
| Predictive | Based on condition data | Minimize downtime AND maintenance cost | Requires sensors, data infrastructure, models | Critical, expensive, failure-has-cascading-effect equipment |
P-F Curve (Potential Failure → Functional Failure)
Condition
│
│ ●─── P (Potential failure detected by sensor)
│ ╲
│ ╲ ← P-F Interval (time to act)
│ ╲
│ ● F (Functional failure — equipment stops)
│
└──────────────────── Time
The P-F interval is your window of opportunity. Detect at P, schedule
repair before F. The longer the P-F interval, the more planning time.Sensor Data Types
| Data Type | What It Detects | Equipment |
|---|---|---|
| Vibration | Bearing wear, imbalance, misalignment | Rotating machinery (motors, pumps, turbines) |
| Temperature | Overheating, friction, electrical faults | Motors, transformers, bearings |
| Current/Power | Load changes, electrical degradation | Electric motors, drives |
| Acoustic | Leaks, cavitation, micro-cracks | Pressure systems, pipes, valves |
| Oil analysis | Wear particles, contamination | Gearboxes, hydraulic systems |
ML Models for RUL (Remaining Useful Life)
| Approach | Method | Data Required |
|---|---|---|
| Statistical | Weibull distribution, exponential degradation | Historical failure times |
| Classical ML | Random Forest, Gradient Boosting on sensor features | Labeled run-to-failure datasets |
| Deep Learning | LSTM, 1D-CNN on raw sensor time series | Large volumes of sensor data |
| Anomaly Detection | Isolation Forest, Autoencoder | Normal operation data only (no failure labels needed) |
Implementation Steps
Phase 1: Select Equipment (criticality analysis)
- Which equipment has highest downtime cost?
- Which has cascading failure effects?
- Prioritize: high cost × high frequency
Phase 2: Install Sensors
- Match sensor type to failure mode (see table above)
- Establish data pipeline: sensor → edge/cloud → storage
Phase 3: Build Baseline
- Collect 3-6 months of normal operation data
- Establish "healthy" patterns
Phase 4: Develop Models
- Start simple: threshold-based alerts (vibration > X = warning)
- Graduate to ML models as data accumulates
- Anomaly detection if you have few/no failure examples
Phase 5: Operationalize
- Integrate alerts into maintenance workflow (CMMS)
- Define response procedures for each alert level
- Measure: reduction in unplanned downtime, maintenance cost savings
Show full SKILL.md (126 more words)Show less
ROI Calculation
Annual Savings = (Unplanned downtime hours reduced × Downtime cost/hour)
+ (Preventive maintenance events avoided × Cost per event)
- (Sensor + infrastructure + model development cost)Output Format
# Predictive Maintenance Plan: {Equipment/Line}
## Equipment Criticality
| Equipment | Downtime Cost/hr | Failure Frequency | Cascading? | Priority |
|-----------|-----------------|-------------------|-----------|---------|
| {name} | ${X} | {X/year} | Y/N | H/M/L |
## Sensor Plan
| Equipment | Failure Mode | Sensor Type | P-F Interval |
|-----------|-------------|-------------|-------------|
| {name} | {mode} | {sensor} | {est. hours/days} |
## Projected ROI
| Metric | Before | After | Savings |
|--------|--------|-------|---------|
| Unplanned downtime | {hrs/year} | {hrs/year} | ${X}/year |
| Maintenance cost | ${X}/year | ${X}/year | ${X}/year |
| Sensor investment | — | ${X} one-time | Payback: {months} |Gotchas
- Start with vibration monitoring: It's the most mature, best-understood predictive technique. 80% of rotating equipment failures can be predicted by vibration analysis alone.
- Data quality > model complexity: A simple threshold alert on clean sensor data outperforms a sophisticated ML model on noisy, incomplete data. Fix data quality first.
- False positives kill adoption: If the model cries wolf too often, maintenance teams ignore it. Tune for high precision (few false alarms) even at the cost of some missed detections early on.
- Cultural change is harder than technology: Shifting from "run to failure" culture requires management buy-in and maintenance team training. Technology alone won't change behavior.
References
- For sensor selection guide by equipment type, see
references/sensor-guide.md - For LSTM-based RUL model tutorial, see
references/rul-tutorial.md
© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 3 other files (references) in mfg-predictive-maintenance of asgard-ai-platform/skills.
- SKILL.md
- examples/sample_scenario.md
- references/rul-tutorial.md
- references/sensor-guide.md
Open the folder on GitHubat commit 4e7f4f8
Compare with similar skills
Mfg Predictive Maintenance next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Mfg Predictive Maintenance this skillasgard-ai-platform/skills | 242 | — | ~1.5k | Automated safety check: Pass | MIT | |
| Senior Data ScientistRaidriar7170/hermes-skilleval | 125 | 5 repos | ~1.4k | Automated safety check: Pass | MIT | |
| Time Series Analytics Useropen-edge-platform/edge-ai-libraries | 171 | — | ~3.1k | Automated safety check: Pass | Apache-2.0 | |
| Aeon Time Series Machine Learningdavila7/claude-code-templates | 33k | 13 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Data Scientistdavila7/claude-code-templates | 33k | 8 repos | ~2.6k | Automated safety check: Pass | MIT | |
| Longbridge Quanthelsome/folio | 271 | 1 repos | ~1.6k | Automated safety check: Pass | MIT |
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Related
Categories
Questions about Mfg Predictive Maintenance
What does Mfg Predictive Maintenance do?
Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework. Mfg Predictive Maintenance is an agent skill from asgard-ai-platform/skills. Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework.
When should I use Mfg Predictive Maintenance?
Mfg Predictive Maintenance fits situations like: the user needs to reduce unplanned downtime; transition from reactive to predictive maintenance; evaluate sensor/IoT investments; estimate equipment failure probability — even if they say machines keep breaking down.
How do I install Mfg Predictive Maintenance in Claude Code?
Run `npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a claude-code`. Or copy the skill folder (mfg-predictive-maintenance in asgard-ai-platform/skills) into .claude/skills/mfg-predictive-maintenance in your project. Claude Code loads it when a task matches its description.
How do I install Mfg Predictive Maintenance in Codex?
Run `npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a codex`. Or copy the skill folder (mfg-predictive-maintenance in asgard-ai-platform/skills) into .agents/skills/mfg-predictive-maintenance in your project. Codex loads it when a task matches its description.
Can I use Mfg Predictive Maintenance in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill mfg-predictive-maintenance -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/mfg-predictive-maintenance, .gemini/skills/mfg-predictive-maintenance, .github/skills/mfg-predictive-maintenance and .opencode/skills/mfg-predictive-maintenance in your project.
What does Mfg Predictive Maintenance need to run?
SKILL.md names no scripts, command-line tools or credentials: Mfg Predictive Maintenance is instructions for the agent only.
Does Mfg Predictive Maintenance access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Mfg Predictive Maintenance safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Mfg Predictive Maintenance use?
Mfg Predictive Maintenance is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Mfg Predictive Maintenance use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 6.7k tokens, read only when the agent opens those files.
What are the alternatives to Mfg Predictive Maintenance?
Skills that share tags, products or a category with Mfg Predictive Maintenance: Senior Data Scientist (Raidriar7170/hermes-skilleval, 125 stars), Time Series Analytics User (open-edge-platform/edge-ai-libraries, 171 stars), Aeon Time Series Machine Learning (davila7/claude-code-templates, 33k stars) and Data Scientist (davila7/claude-code-templates, 33k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Mfg Predictive Maintenance?
asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.
Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.