Campagna SEO QuerySentry
Basata su un autorevole benchmark PostgreSQL, posiziona QuerySentry CLI come un'alternativa snella a Datadog.
Leggi l'approfondimento tecnicoBenchmark sulle Prestazioni Postgres: Sostituire APM Pesanti con QuerySentry CLI#!/usr/bin/env python3
"""
QuerySentry SEO Campaign Generator
Automates developer-targeted content generation and technical SEO funnels for
QuerySentry CLI. Anchored on PostgreSQL pg_stat_statements benchmarking, it builds
high-converting technical campaigns for Dev.to, GitHub, and top developer channels
to position QuerySentry as a lightweight Datadog alternative driving traffic to py-forge.com.
Features:
- Extract and analyze active query patterns via pg_stat_statements metrics.
- Generate terminal-ready ASCII benchmarks comparing QuerySentry against Datadog.
- Build markdown campaign drafts tailored for Dev.to, GitHub, and Medium.
- Embed conversion funnels tracking high-intent technical developer traffic.
- Output structured JSON metrics and automated pull-request content payloads.
Usage:
$ python main.py --target devto --dsn "postgresql://user:pass@localhost:5432/db"
$ python main.py --benchmark-only --output report.json
"""
from dataclasses import dataclass, field
from enum import Enum, auto
from typing import Any, Dict, List, Optional, Tuple
import json
import logging
import os
import sys
import time
logger = logging.getLogger("querysentry.seo")
class TargetChannel(Enum):
"""Supported developer publishing channels for campaign syndication."""
DEVTO = auto()
GITHUB_README = auto()
HASHNODE = auto()
MEDIUM = auto()
@dataclass(frozen=True)
class QueryStatMetrics:
"""Aggregated PostgreSQL query performance profile from pg_stat_statements."""
query_id: int
calls: int
mean_exec_time_ms: float
total_exec_time_ms: float
query_sample: str
class CampaignEngine:
"""Orchestrates query log analysis and technical SEO campaign generation."""
# ... the full file ships with your downloadQuick Start
Questo tool gira con puro Python 3.8+ usando solo la Standard Library. Nessuna dipendenza esterna, nessun pip install.
1 — Esecuzione Diretta e Integrazione Agentica
Lancialo come utility standalone o chiamalo direttamente dai tuoi agenti AI e script:
python main.py2 — Importalo nel Tuo Codice / Progetto
Trascina il singolo file nella cartella del tuo progetto e importalo nativamente:
from main import AssetClassCome lo usano agenti e programmi: Siccome si basa solo sulla Standard Library di Python, i tuoi agenti LLM (LangChain, AutoGen o runtime custom) possono eseguirlo come subprocess isolato o importarlo direttamente — nessun conflitto di dipendenze, nessun virtual environment.