AI agent skills for the flespi IoT and telematics cloud platform. Provides comprehensive reference for the flespi REST API, MQTT broker, device management, and analytics.
/plugin marketplace add flespi-software/skills
/plugin install flespi@flespi-software-skillsThen use the skill:
/flespi:flespi how do I get device messages?
Add to your .vscode/mcp.json:
{
"inputs": [
{
"id": "flespiToken",
"type": "promptString",
"description": "Flespi Token",
"password": true
}
],
"servers": {
"flespi-develop": {
"type": "http",
"url": "https://flespi.io/ai/mcp/develop",
"headers": {
"Authorization": "FlespiToken ${input:flespiToken}"
}
}
}
}For knowledge context, also copy .github/copilot-instructions.md to your project's .github/ directory.
Add to your .cursor/mcp.json:
{
"mcpServers": {
"flespi-develop": {
"url": "https://flespi.io/ai/mcp/develop",
"headers": {
"Authorization": "FlespiToken ${FLESPI_TOKEN}"
}
}
}
}For knowledge context, also copy .cursorrules to your project root.
Add to your .windsurf/mcp.json:
{
"mcpServers": {
"flespi-develop": {
"serverUrl": "https://flespi.io/ai/mcp/develop",
"headers": {
"Authorization": "FlespiToken ${FLESPI_TOKEN}"
}
}
}
}For knowledge context, also copy .windsurfrules to your project root.
Copy AGENTS.md to your project root, or add to your opencode.json:
{
"instructions": ["path/to/skills/flespi/SKILL.md"]
}Copy skills/flespi/SKILL.md into your agent's context or instructions file. The content is plain Markdown and works with any LLM-based coding assistant.
- Platform concepts — data flow, core entities (channels, devices, streams, calculators, plugins, geofences, webhooks, tokens)
- REST API reference — authentication, CRUD, selectors, pagination, response format, error codes, calculate method
- MQTT broker reference — topics, categories, filtering, shared/sticky subscriptions, QoS
- Expression syntax — operators, functions, parameter access, filtering, templating
- Common patterns — device lookup, message retrieval, analytics, stream health, command sending
MIT