The following example demonstrates a Skill designed for the N3uron DataOps and IIoT platform. It is intended as a practical starting point that can be adapted, extended, or used as a reference when building Skills tailored to specific business logic, operational workflows, deployment architectures, and module configuration requirements.
This Skill equips an AI Agent with structured knowledge of the N3uron platform, with a strong focus on Tag model creation, configuration, and large-scale deployment workflows. It helps design tag hierarchies, define tag properties, build reusable templates with custom properties, inheritance, and nesting, and generate template definitions or import-ready CSV files from register maps, datasheets, Excel files, PDFs, images, exported tag lists, and other technical sources.
It supports bulk tag creation, template-instance generation, tag import/export workflows, and configuration generation for different N3uron modules and integration protocols. It also covers SQL Client scripting, MQTT custom payloads and parser scripting, REST API Client and REST API Server, the Scripting module, Derived Tags, Historian, WebVision, and MCP Server configuration.
In addition, it supports diagnostics and industrial analysis using logs, historical data, alarm records, module status, tag metadata, and outputs from the N3uron MCP Server.
Skills definition:
A skill is a reusable capability designed for a specific type of task or workflow.
It packages the instructions, context, and any supporting resources needed to perform that task consistently and effectively. Depending on the implementation, a skill may also include reference materials, files, or scripts that support execution.
Rather than being continuously exposed, a skill remains available in the background and is applied when the situation or request matches its intended use.
Overview - Agent Skills
Note:
N3uron SKILL file might receive updates to incorporate new knowledge about the N3uron DataOps & IIoT platform.
Download SKILL file from this link
Upload the SKILL file to Claude Desktop
Upload the SKILL file to Claude Desktop, go to Customize → Skills → Upload a skill.
.png)
You can also build your own skills with Claude’s Skill Builder.
.png)
Install manually from the SKILL file in Claude Code
Install manually from the SKILL file in Claude Code. Extract downloaded .skill file, and copy the extracted skill folder into your Claude Code skills directory:
mkdir -p ~/.claude/skills
cp -R n3uron-iiot-dataops ~/.claude/skills/Upload the SKILL file to ChatGPT
Upload the SKILL file to ChatGPT, go to Profile menu→ Skills → Upload from your computer.
.png)
You can also build your own skills with ChatGPT..png)
Install manually from the SKILL file in Codex
Install manually from the SKILL file in Codex. Extract the downloaded .skill file, and copy the extracted skill folder into your Codex skills directory:
mkdir -p ~/.agents/skills
cp -R n3uron-iiot-dataops ~/.agents/skills/Install manually from the SKILL file in Antigravity
Install manually from the SKILL file in Antigravity. Extract the downloaded .skill file, and copy the extracted skill folder into your Antigravity skills directory:
mkdir -p ~/.gemini/antigravity/skills
cp -R n3uron-iiot-dataops ~/.gemini/antigravity/skills/Upload the SKILL file to MCPJam Inspector
Upload the SKILL file to MCPJam Inspector. Extract the downloaded .skill file, then navigate to the Skills tab in MCPJam Inspector to discover and manage skills. Click the + button and select the extracted skill folder.
.png)
Tip:
Combine N3uron MCP Server Tools with SKILL KnowledgeAfter uploading the SKILL file, connect the AI agent to your N3uron MCP Server. The MCP Server provides access to N3uron-specific tools and context, while the SKILL file adds domain-specific knowledge, instructions, procedures, and best practices to improve the quality and relevance of the AI agent’s responses.
SKILL file structure
The .skill file is a ZIP archive containing the main skill instructions, reference documentation, and supporting scripts.
n3uron-iiot-dataops/
├── SKILL.md ← investigation logic, tool map, platform reference
├── references/
│ ├── custom-tools.md ← custom tool definitions and usage
│ ├── derived-tags.md ← derived tag configuration and logic
│ ├── diagnostics.md ← diagnostic procedures and checklists
│ ├── historian.md ← historian module reference
│ ├── isa95-tag-paths.md ← isa-95 tag hierarchy & path examples
│ ├── module-instances.md ← module instance management
│ ├── protocols.md ← per-module diagnostic checklists
│ ├── redundancy-links-views.md ← redundancy, links and views reference
│ ├── report-template.md ← structured report template
│ ├── scripting-api.md ← scripting API reference
│ ├── security-users.md ← security, users & roles reference
│ ├── tag-model.md ← tag data model, properties & templates
│ ├── template-generator.md ← bulk tag creation workflows & generator
│ ├── web-vision.md ← WebVision Designer/Viewer HMI reference (new)
│ └── webui-architectures.md ← WebUI & platform architectures reference
└── scripts/
└── n3uron_generator.py ← template JSON generatorN3uron Skills Prompt example for Document-Based Template Generation
An example prompt that shows how to use N3uron Skills to generate templates from a technical document. It helps the skill read the document as the main reference, extract only the information that is clearly defined, validate the results, and create clean N3uron templates ready to import. It also includes rules to avoid guesses, unclear data, or incorrectly structured outputs.
Use the n3uron-iiot-dataops skill and its template-generator workflow to read the attached SolarEdge SunSpec Modbus PDF first, extract the documented Modbus-over-TCP monitoring map, and generate N3uron Modbus TCP templates as separate JSON templates, each packaged in its own ZIP file.
Goal:
Create independent template outputs for each documented logical element shown in the PDF. Do not merge them into a single template. Generate separate templates for:
- Common
- Inverter model block 101/102/103
- Model 160 Multiple MPPT extension
- Meter 1
- Meter 2
- Meter 3
Source-of-truth rule:
- Read the uploaded PDF first and use it as the only source of truth.
Extraction rules:
- Extract only explicitly documented Modbus-over-TCP monitoring registers.
- Use only confirmed entries: include a register only when all of these are clearly documented in the PDF:
- address
- name
- register/data type
- size/length
- Preserve the documented SunSpec structure and keep tags grouped exactly by the PDF’s own sections/models.
- Treat Common and the inverter model block 101/102/103 as separate mappings.
- Treat inverter models 101, 102, and 103 as one shared template block, because the PDF documents them as one common register table distinguished by `C_SunSpec_DID`, not as three separate tables.
- Treat Model 160 as a separate template.
- Treat Meter 1, Meter 2, and Meter 3 as separate templates.
- Include documented units and access mode only when explicitly stated.
- Include documented scale-factor registers only as standalone documented tags when present in the PDF.
- Do not apply scaling to any value tags.
- Do not convert scale-factor registers into N3uron scaling blocks.
- Do not infer which scale factor applies to a value unless the PDF states it explicitly enough to implement deterministically.
- Even where the PDF describes the general SunSpec scale-factor concept, do not implement scaling logic in the template; leave scaling out entirely because I will handle scaling manually.
- Do not infer missing fields, undocumented gaps, unsupported points, or unstated mappings.
- Omit any ambiguous, contradictory, malformed, duplicated, or inconsistently documented entries.
- If the PDF contains conflicting addresses, broken rows, table corruption, or section-level inconsistencies, exclude those entries rather than guessing.
History rules:
- Enable history for measurement/value tags.
- Disable history for identification/static metadata tags.
- Disable history for standalone scale-factor tags.
Template split requirements:
- Produce one JSON template per documented logical element/section.
- Produce one ZIP file per JSON template.
- Each ZIP must contain exactly one JSON template file and nothing else.
- Do not bundle multiple JSON templates into a single ZIP.
- Use clear deterministic filenames:
- common.zip containing common.json
- inverter_model_101_102_103.zip containing inverter_model_101_102_103.json
- model_160_mppt.zip containing model_160_mppt.json
- meter_1.zip containing meter_1.json
- meter_2.zip containing meter_2.json
- meter_3.zip containing meter_3.json
TDD requirement before final delivery:
Before generating the final ZIP files, perform an internal test-driven validation pass and reject any bad data.
At minimum validate:
1. Every included tag maps to a register explicitly present in the PDF.
2. Every included tag has confirmed address, type, and size/length.
3. No inferred tags are present.
4. No scaling logic has been implemented in any template.
5. Scale-factor registers, if included, remain standalone documented tags only.
6. Each JSON file is structurally valid for N3uron template import.
7. Each template contains only tags belonging to its own logical section.
8. Each ZIP contains exactly one JSON file.
9. Ambiguous or contradictory rows are excluded.
Output constraints:
- Do not return explanations, summaries, markdown, tables, logs, or commentary.
- Do not return intermediate CSVs or preview data.
- Do not return a combined monolithic template ZIP.
- Return only the final ZIP files, one ZIP per template JSON.Customizing and extending it
Add your site topology, tag namespace, and known quirks to SKILL.md so the AI model has permanent context for your deployment.
Extend the protocol checklists in protocols.md with your equipment-specific failure modes.
Add sections to report-template.md for your organization's reporting format.
Document your Custom MCP Tools in the Skill so the model knows when and how to call them — enabling fully bespoke workflows backed by N3uron Scripting.