القائمة
Instrumenting system diagnostics, counting requests, and exporting metrics to Prometheus monitoring systems.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/Lord1Egypt/ai-skillforge --skill prometheus-metricsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
Implementing WCAG accessibility guidelines, semantic HTML5, and screen reader ARIA roles.
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | prometheus-metrics |
| description | Instrumenting system diagnostics, counting requests, and exporting metrics to Prometheus monitoring systems. |
| allowed-tools | Read Write Edit Bash |
| license | MIT license |
| metadata | {"skill-author":"Lord1Egypt"} |
Prometheus is a free software ecosystem for monitoring and alerting, relying on pull-based timeseries ingestion.
Use to track system loads, latency distributions, and error rates of running web applications.
from prometheus_client import start_http_server, Counter
import time
# Create a metric to track requests
REQUESTS = Counter('http_requests_total', 'Total HTTP Requests')
def process_request():
REQUESTS.inc()
if __name__ == '__main__':
# Start exporter server on port 8000
start_http_server(8000)
while True:
process_request()
time.sleep(1)
Set up gauge metrics, summary latency buckets, custom registry setups, and alerting rules configuration.