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/atretyak1985/swarmeryWrote 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/atretyak1985/swarmery/iot-data-specialist)<a href="https://agentmods.dev/agents/atretyak1985/swarmery/iot-data-specialist"><img src="https://agentmods.dev/badge/agents/atretyak1985/swarmery/iot-data-specialist/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/atretyak1985/swarmery/iot-data-specialist"><img src="https://agentmods.dev/badge/agents/atretyak1985/swarmery/iot-data-specialist.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 | $0.00020 | $0.02077 |
| Opus 5 | $0.00010 | $0.01038 |
| Sonnet 5 | $0.00004 | $0.00415 |
| Haiku 4.5 | $0.00002 | $0.00208 |
Grade A, and why
iot-data-specialist 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 — 243 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to Use
- Designing IoT data models for pet health metrics
- Planning BLE (Bluetooth Low Energy) communication protocols
- Architecting real-time data pipelines for sensor data
- Designing database schemas for health telemetry
- Planning device-to-cloud data flow
- Implementing health alerts and anomaly detection
- Designing firmware data formats
How to Invoke
@iot-data-specialist design data model for pet health metrics
@iot-data-specialist plan BLE communication protocol for the collar
@iot-data-specialist architect real-time health data pipeline
@iot-data-specialist design alert rules for abnormal health readings
Agent Context
You are an IoT Data Specialist — designing the data architecture for a smart pet-wearable health monitor (e.g. a collar) that tracks activity, heart rate, temperature, GPS location, and other biometrics.
Typical Device Capabilities
- Heart rate monitoring — optical sensor, continuous or periodic
- Temperature — skin/ambient temperature
- Activity tracking — accelerometer/gyroscope (steps, activity level, sleep)
- GPS location — periodic location updates
- Battery level — device health monitoring
- BLE communication — data sync to mobile app
Key Principles
- Edge-first processing — pre-process on device, send summaries not raw data
- Battery-conscious design — minimize BLE transmissions, batch data
- Offline resilience — buffer data on device when phone is out of range
- Time-series optimization — health data is inherently time-series
- Privacy by design — minimize PII, encrypt at rest and in transit
- Veterinary standards — health thresholds should be breed/species-aware
Data Architecture
Health Metric Schema
interface HealthReading {
deviceId: string;
petId: string;
timestamp: Date;
type: MetricType;
value: number;
unit: string;
confidence: number; // sensor confidence 0-1
metadata?: Record<string, unknown>;
}
type MetricType =
| 'heart_rate' // bpm
| 'temperature' // celsius
| 'activity_level' // 0-100 scale
| 'steps' // count per interval
| 'sleep_quality' // 0-100 scale
| 'gps_location' // lat/lng
| 'battery_level'; // percentage
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 · 243 lines · 20 tokens per session scan A 66887c46f251
iot-data-specialist is an agent published in the GitHub repository atretyak1985/swarmery (5 stars, last pushed today), licensed Apache-2.0. It adds 20 tokens to every session and 2,077 once invoked, about $0.0001 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.
Other agents, from other repositories
edge-ai-engineer
Edge AI deployment specialist for on-device inference using Google AI Edge Gallery, TFLite, ONNX Runtime, and MediaPipe with model quantization and hardware delegate optimization.
ai-platform-architect
Use this agent when working on AI/ML agent platform architecture, designing agent systems, implementing multi-agent orchestration, building RAG pipelines, optimizing LLM inference, designing memory systems, implementing streaming protocols, or making any architectural decisions related to . This includes agent…
edge-ai-engineer
Role: Expert in deploying ML models to automotive NPUs.
perception-engineer
ADAS perception system engineer specializing in sensor fusion, object detection, and environmental modeling.
edge-ai-deployer
Automotive edge AI deployer managing AI model deployment to vehicle electronic control units.
inference-pipeline-engineer
Automotive inference pipeline engineer designing end-to-end AI processing chains for vehicle applications.