IoT Payload Architect & Telemetry Synthesizer
Turn your AI into an industrial IoT engineer. Auto-scan codebases and generate physically realistic sensor telemetry across MQTT, HTTP, and WebSockets with anomaly injection.
$9.99
Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+21 moreClaude CodeCursorCodex CLIMuseDots
Manus
Grok
Grok Bot+18 agentsIoT Payload Architect & Telemetry Synthesizer
Example session with this skill installed
Scan this project to find our telemetry ingest endpoints, then generate 5 simulated turbine payloads and send them directly to our webhook with an anomaly spike on step 3.
- Read your context and instructions
- Compiled the iot payload architect
Summary
Scanned the workspace, detected active endpoints in docker-compose.yml and .env, and streamed 5 physically constrained turbine telemetry payloads to the local webhook endpoint with a vibration anomaly injected on step 3.
| Step | Device ID | Vibration (mm/s) | Bearing Temp (°C) | Status | Ingest Result | Latency |
|---|---|---|---|---|---|---|
| 0 | turb-alpha-01 | 1.42 (Normal) | 68.3 | NORMAL | HTTP 200 OK | 14.2ms |
| 1 | turb-alpha-01 | 1.48 (Normal) | 68.7 | NORMAL | HTTP 200 OK | 12.8ms |
| 2 | turb-alpha-01 | 1.55 (Normal) | 69.1 | NORMAL | HTTP 200 OK | 13.5ms |
| 3 | turb-alpha-01 | 6.82 (Spike Anomaly) | 96.4 (°C Trip) | FAULT | HTTP 200 OK | 15.1ms |
| 4 | turb-alpha-01 | 1.46 (Recovered) | 70.2 | NORMAL | HTTP 200 OK | 13.0ms |
Sample Transmitted Payload (Step 3 - Injected Fault):
{
"deviceId": "turb-alpha-01",
"facility": "substation-east",
"timestamp": "2026-11-11T02:30:00.000Z",
"sequenceNumber": 3,
"status": "FAULT",
"metrics": {
"windSpeedMs": 13.4,
"rotationSpeedRpm": 1604.2,
"vibrationMmPerSec": 6.82,
"bearingTempC": 96.4,
"activePowerKw": 1840.5,
"oilPressureBar": 4.15,
"generatorEfficiencyPercent": 93.8,
"ambientTempC": 28.5
},
"diagnostics": {
"runtimeHours": 4328.5,
"alarmCode": 2
}
}
Next steps:
- Verify that your alerting engine triggered an alert on step 3 for threshold
vibrationMmPerSec > 4.5. - To run continuously as a background stream to your MQTT broker, run:
python scripts/payload_generator.py -b turbine --swarm 3 -n 0 -i 1.0 --mqtt-host localhost --mqtt-topic plant/sensors/telemetry
Connects securely to your tools. The creator never sees your data.
About this skill
Testing IoT ingestion pipelines (ThingsBoard, EMQX, AWS IoT Core, Kafka, or custom backend dashboards) requires realistic physical sensor behavior—not static dummy numbers.
IoT Payload Architect equips your AI coding agent (Claude Code, Cursor, Copilot, Hermes, Antigravity) with industrial telemetry engineering expertise:
Key Capabilities:
- Codebase Auto-Scanner: Agent can scan your repository (
--scan) to auto-discover active MQTT brokers, REST APIs, and WebSocket endpoints. - Direct Dispatch (Tembak Langsung): Directly stream simulated telemetry into your live endpoints with real-time latency and status logging (zero external dependencies required!).
- 4 Industrial Blueprints:
- Industrial Machinery & Wind Turbines (HERA Spec): RPM, vibration (ISO 10816), bearing temp, active power kW, oil pressure.
- Autonomous UAV / Drone Fleet: GPS drift trajectory, 6-DoF attitude, LiPo discharge curve, RSSI link quality.
- ESG & Environmental Carbon: CO2, particulate matter (PM2.5/PM10), VOC index, cumulative energy, and carbon equivalence.
- Smart Grid & Power Distribution: 3-phase voltage/current, frequency, power factor, THD, transformer thermal dynamics.
- Controlled Anomaly Injection: Inject spikes, drift, stuck-at, flatlines, or oscillations to stress-test your backend alerting rules.
- Swarm Mode: Simulate 1 to 1000 concurrent virtual devices with unique IDs and staggered phases.
- Bonus Standalone Generator: Includes
payload_generator.pywith zero pip dependencies, ready to pipe into your broker or API.
Included in this package:
SKILL.md(Agent instruction standard v1.0.0)- Ready-to-use JSON schemas & templates (Turbine, Drone, Carbon, Smart Grid)
- Standalone Python Generator CLI
- Quick-start integration guide
Requires Python for the generator script. Direct dispatch requires network access to the target broker or endpoint. Swarm scale is limited by local hardware.
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before any payment.
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- Software
Python 3.8+
- Optional
- paho-mqtt
Zero pip dependencies required for standard usage. Built-in HTTP client and raw socket MQTT publisher are self-contained in Python standard library.
Permissions
Reads workspace configuration files (.env, docker-compose) to detect endpoints and sends simulated telemetry packets over local network.