Kubeshark Installer
kubeshark/kubeshark
Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.
Guides design and deployment of mesh sensor networks including data collection, power management, communication protocols, and fleet management Use when the user asks about iot sensor network…
$ npx skills add FerroxLabs/wayland --skill iot-sensor-network -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install FerroxLabs/wayland iot-sensor-network --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/FerroxLabs/wayland.git skills-src && mkdir -p .claude/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .claude/skills/iot-sensor-network && 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 "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .claude/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-networkType 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 FerroxLabs/wayland --skill iot-sensor-network -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install FerroxLabs/wayland iot-sensor-network --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .agents/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .agents/skills/iot-sensor-network && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .agents/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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 FerroxLabs/wayland --skill iot-sensor-network -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install FerroxLabs/wayland iot-sensor-network --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .cursor/skills/iot-sensor-network && 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 "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .cursor/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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/FerroxLabs/wayland.git --path src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network--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 FerroxLabs/wayland --skill iot-sensor-network -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install FerroxLabs/wayland iot-sensor-network --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .gemini/skills/iot-sensor-network && 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 "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .gemini/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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 FerroxLabs/wayland iot-sensor-networkInstalls 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 FerroxLabs/wayland --skill iot-sensor-network -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .github/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .github/skills/iot-sensor-network && 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 "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .github/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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 FerroxLabs/wayland --skill iot-sensor-network -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install FerroxLabs/wayland iot-sensor-network --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/FerroxLabs/wayland.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network .opencode/skills/iot-sensor-network && 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 "iot-sensor-network" agent skill from https://github.com/FerroxLabs/wayland/tree/main/src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network into .opencode/skills/iot-sensor-network/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "iot-sensor-network", 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.
iot-sensor-networkGuides design and deployment of mesh sensor networks including data collection, power management, communication protocols, and fleet management Use when the user asks about iot sensor network…
Iot Sensor Network is an agent skill from FerroxLabs/wayland. Guides design and deployment of mesh sensor networks including data collection, power management, communication protocols, and fleet management Use when the user asks about iot sensor network, related techniques, best practices, or needs guidance in this domain. Do NOT use when the request is outside the scope of iot sensor network or requires a different specialized skill.
Its SKILL.md is about 4.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Deployment. It works with ESP32. The repository describes itself as: Wayland - The AI Agent That Perceives. Reasons. Acts. Evolves. The licence is Apache-2.0.
5 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 4c030c7. It shows what the files ask for, not the result of running them.
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.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python, c and template).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Iot Sensor Network loads about 4.5k tokens when it runs. Until then it costs about 99 tokens; SKILL.md has 661 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.
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.
The full file from FerroxLabs/wayland at commit 4c030c7, republished under its Apache-2.0 licence (© FerroxLabs). 661 words, ~4,539 tokens.
.claude/skills/iot-sensor-network/SKILL.md (or your agent's skills folder).You are an expert IoT sensor network architect. You guide developers through mesh networking topologies, data collection pipelines, power management strategies, communication protocol selection, and scalable fleet management for distributed sensor deployments.
Use this skill when:
Do NOT use when:
| Topology | Range | Scalability | Power | Complexity | Reliability | Best For |
|---|---|---|---|---|---|---|
| Star | Short | Low (<20) | Low | Simple | Single point failure | Small indoor |
| Mesh | Extended | High (100+) | Medium | Complex | Self-healing | Large area |
| Tree | Medium | Medium | Medium | Medium | Branch failure | Hierarchical |
| Star-of-Stars | Extended | High | Low-Med | Medium | Gateway redundancy | Multi-room |
| Protocol | Range | Data Rate | Power | Topology | License | Nodes |
|---|---|---|---|---|---|---|
| WiFi | 50m | 54+ Mbps | High | Star | ISM | ~32 |
| BLE Mesh | 30m | 2 Mbps | Very Low | Mesh | ISM | 32K |
| Zigbee | 100m | 250 kbps | Low | Mesh/Star | ISM | 65K |
| Z-Wave | 100m | 100 kbps | Low | Mesh | Licensed | 232 |
| LoRa | 15km | 50 kbps | Very Low | Star | ISM | 1000s |
| Thread | 30m | 250 kbps | Low | Mesh | ISM | 250+ |
| ESP-NOW | 200m | 1 Mbps | Low | Star/Mesh | ISM | 20 |
/* sensor_node.c - Battery-powered sensor node using ESP-NOW */
#include <esp_now.h>
#include <esp_wifi.h>
#include <esp_sleep.h>
#include <nvs_flash.h>
#include <string.h>
#define GATEWAY_MAC {0xAA, 0xBB, 0xCC, 0xDD, 0xEE, 0xFF}
#define SLEEP_DURATION_US (300 * 1000000ULL) /* 5 minutes */
#define SENSOR_PIN ADC1_CHANNEL_0
#define BATTERY_PIN ADC1_CHANNEL_3
typedef struct __attribute__((packed)) {
uint8_t node_id;
uint8_t msg_type; /* 0=data, 1=heartbeat, 2=alert */
uint16_t sequence;
float temperature;
float humidity;
float battery_v;
uint32_t uptime_ms;
} sensor_packet_t;
static uint16_t seq_num = 0;
static uint8_t gateway_mac[] = GATEWAY_MAC;
RTC_DATA_ATTR static uint16_t boot_count = 0;
static void on_data_sent(const uint8_t *mac, esp_now_send_status_t status) {
if (status != ESP_NOW_SEND_SUCCESS) {
/* Store in NVS for retry on next wake */
store_failed_packet();
}
}
static void init_espnow(void) {
wifi_init_config_t cfg = WIFI_INIT_CONFIG_DEFAULT();
esp_wifi_init(&cfg);
esp_wifi_set_mode(WIFI_MODE_STA);
esp_wifi_start();
esp_now_init();
esp_now_register_send_cb(on_data_sent);
esp_now_peer_info_t peer = {
.channel = 1,
.encrypt = false
};
memcpy(peer.peer_addr, gateway_mac, 6);
esp_now_add_peer(&peer);
}
static float read_battery_voltage(void) {
/* Voltage divider: 100K/100K, Vmax = 4.2V -> ADC max ~2.1V */
int raw = adc1_get_raw(BATTERY_PIN);
return (raw / 4095.0f) * 3.3f * 2.0f;
}
void app_main(void) {
boot_count++;
init_espnow();
sensor_packet_t pkt = {
.node_id = CONFIG_NODE_ID,
.msg_type = 0,
.sequence = boot_count,
.temperature = read_temperature_sensor(),
.humidity = read_humidity_sensor(),
.battery_v = read_battery_voltage(),
.uptime_ms = boot_count * (SLEEP_DURATION_US / 1000)
};
/* Retry failed packets from NVS first */
retry_stored_packets();
esp_now_send(gateway_mac, (uint8_t *)&pkt, sizeof(pkt));
vTaskDelay(pdMS_TO_TICKS(100)); /* Wait for send callback */
/* Deep sleep */
esp_wifi_stop();
esp_deep_sleep(SLEEP_DURATION_US);
}#!/usr/bin/env python3
"""Gateway node: collects ESP-NOW data, forwards to MQTT/HTTP."""
import struct
import json
import time
import paho.mqtt.client as mqtt
from collections import deque
from threading import Lock
PACKET_FORMAT = "<BBHfffI" # matches sensor_packet_t
PACKET_SIZE = struct.calcsize(PACKET_FORMAT)
class SensorGateway:
def __init__(self, mqtt_broker: str, mqtt_port: int = 1883):
self.mqtt = mqtt.Client(client_id="sensor-gateway")
self.mqtt.connect(mqtt_broker, mqtt_port)
self.mqtt.loop_start()
self.node_registry = {}
self.buffer = deque(maxlen=1000)
self.lock = Lock()
self.stats = {"received": 0, "forwarded": 0, "errors": 0}
def process_packet(self, raw_data: bytes, rssi: int):
"""Parse and forward a sensor packet."""
if len(raw_data) != PACKET_SIZE:
self.stats["errors"] += 1
return
fields = struct.unpack(PACKET_FORMAT, raw_data)
node_id, msg_type, seq, temp, hum, batt, uptime = fields
payload = {
"node_id": node_id,
"type": ["data", "heartbeat", "alert"][msg_type],
"sequence": seq,
"temperature": round(temp, 2),
"humidity": round(hum, 2),
"battery_v": round(batt, 2),
"uptime_ms": uptime,
"rssi": rssi,
"gateway_ts": time.time()
}
# Update registry
with self.lock:
self.node_registry[node_id] = {
"last_seen": time.time(),
"battery": batt,
"rssi": rssi,
"packets": self.node_registry.get(node_id, {}).get("packets", 0) + 1
}
# Publish to MQTT
topic = f"sensors/node/{node_id}/data"
self.mqtt.publish(topic, json.dumps(payload), qos=1)
self.stats["received"] += 1
self.stats["forwarded"] += 1
# Battery alert
if batt < 3.3:
alert_topic = f"sensors/node/{node_id}/alert"
self.mqtt.publish(alert_topic, json.dumps({
"type": "low_battery",
"voltage": batt,
"node_id": node_id
}), qos=1)
def get_fleet_status(self) -> dict:
"""Return status of all known nodes."""
now = time.time()
with self.lock:
status = {}
for nid, info in self.node_registry.items():
age = now - info["last_seen"]
status[nid] = {
**info,
"status": "online" if age < 600 else "offline",
"last_seen_ago": int(age)
}
return status| Component | Active | Sleep | Duty Cycle | Average |
|---|---|---|---|---|
| ESP32 (WiFi TX) | 240 mA | 10 uA | 0.1% | 0.25 mA |
| BME280 sensor | 1 mA | 0.1 uA | 0.1% | 0.001 mA |
| Voltage regulator | 5 mA | 5 mA | 100% | 5 mA |
| Total | ~5.25 mA | |||
| 18650 (3000mAh) | ~24 days |
/* Power-optimized wake cycle */
#include <esp_sleep.h>
#include <esp_pm.h>
#include <driver/rtc_io.h> ./* Use RTC memory to persist across deep sleep */
RTC_DATA_ATTR static int failed_sends = 0;
RTC_DATA_ATTR static float last_temp = 0;
/* Adaptive sleep: longer intervals when data is stable */
static uint64_t calculate_sleep_duration(float current_temp) {
float delta = fabsf(current_temp - last_temp);
last_temp = current_temp;
if (delta > 2.0f) return 60 * 1000000ULL; /* 1 min if changing fast */
if (delta > 0.5f) return 300 * 1000000ULL; /* 5 min if moderate */
return 900 * 1000000ULL; /* 15 min if stable */
}
/* Disable unused peripherals before sleep */
static void prepare_for_sleep(void) {
esp_wifi_stop();
adc_power_release();
/* Hold GPIO states during sleep if needed */
rtc_gpio_hold_en(GPIO_NUM_25); /* Keep LED off */
/* Configure wake sources */
esp_sleep_enable_timer_wakeup(calculate_sleep_duration(last_temp));
esp_sleep_enable_ext0_wakeup(GPIO_NUM_33, 0); /* Wake on button press */
/* Isolate unused GPIO to prevent leakage */
esp_sleep_pd_config(ESP_PD_DOMAIN_RTC_PERIPH, ESP_PD_OPTION_OFF);
}def calculate_solar_panel(
avg_current_ma: float,
battery_mah: float,
peak_sun_hours: float = 4.0,
panel_efficiency: float = 0.7,
days_autonomy: int = 3
) -> dict:
"""Calculate minimum solar panel wattage for a sensor node."""
daily_consumption_mah = avg_current_ma * 24
daily_consumption_wh = daily_consumption_mah * 3.7 / 1000 # Assuming 3.7V LiPo
# Panel must generate enough for daily use + charge losses
required_generation_wh = daily_consumption_wh / panel_efficiency
panel_watts = required_generation_wh / peak_sun_hours
# Battery must last through autonomy period
min_battery_mah = daily_consumption_mah * days_autonomy
return {
"daily_consumption_mah": round(daily_consumption_mah, 1),
"daily_consumption_wh": round(daily_consumption_wh, 3),
"min_panel_watts": round(panel_watts, 2),
"recommended_panel_watts": round(panel_watts * 1.5, 2), # 50% margin
"min_battery_mah": round(min_battery_mah),
"battery_adequate": battery_mah >= min_battery_mah
}class EdgeProcessor:
"""Process sensor data locally before transmitting."""
def __init__(self, window_size: int = 12):
self.window = []
self.window_size = window_size
self.last_sent = None
self.threshold = 1.0 # Only send if delta > threshold
def add_reading(self, value: float) -> dict | None:
"""Add reading, return packet only if transmission warranted."""
self.window.append(value)
if len(self.window) > self.window_size:
self.window.pop(0)
if len(self.window) < self.window_size:
return None # Still collecting
stats = {
"mean": sum(self.window) / len(self.window),
"min": min(self.window),
"max": max(self.window),
"range": max(self.window) - min(self.window),
"samples": len(self.window)
}
# Only transmit on significant change
if self.last_sent is not None:
if abs(stats["mean"] - self.last_sent) < self.threshold:
return None
self.last_sent = stats["mean"]
self.window.clear()
return statsfrom influxdb_client import InfluxDBClient, Point, WritePrecision
from datetime import datetime
class SensorStorage:
def __init__(self, url: str, token: str, org: str, bucket: str):
self.client = InfluxDBClient(url=url, token=token, org=org)
self.write_api = self.client.write_api()
self.query_api = self.client.query_api()
self.bucket = bucket
self.org = org
def store_reading(self, node_id: int, measurement: str,
fields: dict, tags: dict = None):
point = Point(measurement)
point.tag("node_id", str(node_id))
if tags:
for k, v in tags.items():
point.tag(k, str(v))
for k, v in fields.items():
point.field(k, float(v))
point.time(datetime.utcnow(), WritePrecision.MS)
self.write_api.write(bucket=self.bucket, record=point)
def query_node_history(self, node_id: int, hours: int = 24) -> list:
query = f'''
from(bucket: "{self.bucket}")
|> range(start: -{hours}h)
|> filter(fn: (r) => r["node_id"] == "{node_id}")
|> aggregateWindow(every: 5m, fn: mean, createEmpty: false)
|> yield(name: "mean")
'''
result = self.query_api.query(query, org=self.org)
return [
{"time": record.get_time(), "value": record.get_value()}
for table in result for record in table.records
]import time
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class NodeHealth:
node_id: int
last_seen: float = 0
battery_voltage: float = 0
rssi: int = 0
packet_count: int = 0
error_count: int = 0
expected_interval: int = 300 # seconds
firmware_version: str = ""
@property
def is_online(self) -> bool:
return (time.time() - self.last_seen) < (self.expected_interval * 2)
@property
def battery_percent(self) -> int:
# LiPo discharge curve approximation
if self.battery_voltage >= 4.2: return 100
if self.battery_voltage <= 3.0: return 0
return int((self.battery_voltage - 3.0) / 1.2 * 100)
@property
def packet_loss_rate(self) -> float:
if self.packet_count == 0: return 0
return self.error_count / (self.packet_count + self.error_count)
class FleetManager:
def __init__(self):
self.nodes: dict[int, NodeHealth] = {}
def update_node(self, node_id: int, **kwargs):
if node_id not in self.nodes:
self.nodes[node_id] = NodeHealth(node_id=node_id)
node = self.nodes[node_id]
for key, value in kwargs.items():
if hasattr(node, key):
setattr(node, key, value)
node.last_seen = time.time()
node.packet_count += 1
def get_alerts(self) -> list[dict]:
alerts = []
for nid, node in self.nodes.items():
if not node.is_online:
alerts.append({"node": nid, "type": "offline",
"since": node.last_seen})
if node.battery_percent < 20:
alerts.append({"node": nid, "type": "low_battery",
"percent": node.battery_percent})
if node.packet_loss_rate > 0.1:
alerts.append({"node": nid, "type": "high_packet_loss",
"rate": node.packet_loss_rate})
return alerts| Mistake | Impact | Solution |
|---|---|---|
| No packet sequencing | Undetected data loss | Include sequence numbers |
| WiFi for battery nodes | Days instead of months | Use ESP-NOW, BLE, or LoRa |
| No local buffering | Data loss on connectivity gaps | NVS/flash ring buffer |
| Fixed sample rates | Wasted power on stable data | Adaptive sampling |
| No OTA update path | Manual firmware updates forever | Plan OTA from day one |
| Ignoring clock drift | Misaligned time-series data | NTP sync at gateway, relative timestamps at nodes |
| No encryption | Data interception, spoofing | AES-128 at minimum for ESP-NOW |
## Iot Sensor Network Analysis
### Assessment
[Key findings and observations]
### Recommendations
1. [Primary recommendation]
2. [Secondary recommendation]
3. [Additional suggestions]
### Action Items
- [ ] [First action step]
- [ ] [Second action step]
- [ ] [Follow-up task]Input: "Help me with iot sensor network for my current situation"
Output:
Based on your situation, here is a structured approach to iot sensor network:
© FerroxLabs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network of FerroxLabs/wayland.
Open the folder on GitHubat commit 4c030c7
Iot Sensor Network 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 |
|---|---|---|---|---|---|---|
| Iot Sensor Network this skillFerroxLabs/wayland | 608 | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | |
| Kubeshark Installerkubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| GreptimeDB Dev Docker ImageGreptimeTeam/greptimedb | 6.7k | — | ~4k | Automated safety check: Notes | Apache-2.0 | |
| KubeSphere ServiceMesh Managerkubesphere/kubesphere | 17k | — | ~2.4k | Automated safety check: Pass | Custom licence | |
| Vercelremotion-dev/remotion | 62k | — | ~1.2k | Automated safety check: Pass | Custom licence | |
| AWS Cdk Developmentzxkane/aws-skills | 367 | 2 repos | ~2.5k | Automated safety check: Pass | MIT |
kubeshark/kubeshark
Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.
GreptimeTeam/greptimedb
Packages a locally built GreptimeDB debug binary into a development-only Docker image for local-cluster testing, with an optional push to a dev registry.
kubesphere/kubesphere
Installs, checks and troubleshoots the KubeSphere ServiceMesh extension (Istio, Kiali, Jaeger), including grayscale release, sidecar injection, topology and tracing issues.
remotion-dev/remotion
Set up a Codex monitor for Vercel deployments and preview URLs.
zxkane/aws-skills
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python.
maslennikov-ig/claude-code-orchestrator-kit
Comprehensive DevOps skill for CI/CD, infrastructure automation, containerization, and cloud platforms (AWS, GCP, Azure). Includes pipeline setup…
FerroxLabs/wayland
Install, start, connect, and troubleshoot visualization companion projects for Aion/OpenClaw, with Star-Office-UI as the default recommendation.
FerroxLabs/wayland
OpenClaw usage expert: Helps you install, deploy, configure, and use OpenClaw personal AI assistant.
FerroxLabs/wayland
Set up TVControl end to end: install the connector, start TradingView Desktop with its control port open, load a watchlist export, add the indicators they use, and leave a working chart.
FerroxLabs/wayland
End-to-end guide for designing, running, and analyzing A/B tests including experiment design, statistical significance, sample size calculation, common pitfalls, and advanced testing patterns.
FerroxLabs/wayland
Complete academic writing guide covering thesis and dissertation structure, journal article format using IMRaD, literature review methodology, citation management, the peer review process, and…
FerroxLabs/wayland
Web accessibility expertise covering WCAG 2.2 conformance, audit methodology, ARIA patterns, keyboard navigation, screen reader testing, focus management, form accessibility, and automated vs manual…
Works with
Categories
Guides design and deployment of mesh sensor networks including data collection, power management, communication protocols, and fleet management Use when the user asks about iot sensor network…. Iot Sensor Network is an agent skill from FerroxLabs/wayland. Guides design and deployment of mesh sensor networks including data collection, power management, communication protocols, and fleet management Use when the user asks about iot sensor network, related techniques, best practices, or needs guidance in this domain.
Iot Sensor Network fits situations like: the user asks about iot sensor network; related techniques; needs guidance in this domain; the request is outside the scope of iot sensor network.
Run `npx skills add FerroxLabs/wayland --skill iot-sensor-network -a claude-code`. Or copy the skill folder (src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network in FerroxLabs/wayland) into .claude/skills/iot-sensor-network in your project. Claude Code loads it when a task matches its description.
Run `npx skills add FerroxLabs/wayland --skill iot-sensor-network -a codex`. Or copy the skill folder (src/process/resources/skills-library/bodies/skills/emerging-tech/iot-sensor-network in FerroxLabs/wayland) into .agents/skills/iot-sensor-network in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add FerroxLabs/wayland --skill iot-sensor-network -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/iot-sensor-network, .gemini/skills/iot-sensor-network, .github/skills/iot-sensor-network and .opencode/skills/iot-sensor-network in your project.
SKILL.md names no scripts, command-line tools or credentials: Iot Sensor Network is instructions for the agent only. Our summary lists: Python 3.
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.
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.
Iot Sensor Network is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.5k tokens (SKILL.md is roughly 18k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Iot Sensor Network: Kubeshark Installer (kubeshark/kubeshark, 12k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars) and Vercel (remotion-dev/remotion, 62k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
FerroxLabs (a GitHub user) maintains it in FerroxLabs/wayland, which has 608 GitHub stars. The repository holds 1,194 skills in this directory. The repository was last updated on October 6, 2026.
Source: FerroxLabs/wayland on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.