realtime-alert-pipeline — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited realtime-alert-pipeline (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
from dataclasses import dataclass, field
from datetime import datetime
from typing import Callable, Optional
import json
@dataclass
class AlertCondition:
name: str
check_fn: Callable # returns True/False
priority: str = "MEDIUM" # HIGH, MEDIUM, LOW
cooldown_minutes: int = 60
last_triggered: Optional[datetime] = None
@dataclass
class AlertRule:
id: str
name: str
conditions: list[AlertCondition]
logic: str = "ALL" # ALL (AND) or ANY (OR)
channels: list[str] = field(default_factory=lambda: ["console"])
message_template: str = ""
class AlertPipeline:
def __init__(self):
self.rules: list[AlertRule] = []
self.triggered_alerts: list[dict] = []
def add_rule(self, rule: AlertRule):
self.rules.append(rule)
def check_all(self, context: dict) -> list[dict]:
"""Check all rules against current market context."""
triggered = []
now = datetime.utcnow()
for rule in self.rules:
results = []
for cond in rule.conditions:
if cond.last_triggered and (now - cond.last_triggered).seconds < cond.cooldown_minutes * 60:
results.append(False)
continue
try:
results.append(cond.check_fn(context))
except:
results.append(False)
fire = all(results) if rule.logic == "ALL" else any(results)
if fire:
alert = {
"rule_id": rule.id, "name": rule.name, "time": now.isoformat(),
"priority": max((c.priority for c in rule.conditions), key=lambda p: {"HIGH": 3, "MEDIUM": 2, "LOW": 1}[p]),
"channels": rule.channels,
"message": rule.message_template.format(**context) if rule.message_template else rule.name,
}
triggered.append(alert)
for cond in rule.conditions:
cond.last_triggered = now
self.triggered_alerts.extend(triggered)
return triggered
def format_for_telegram(self, alert: dict) -> str:
return f"🚨 *{alert['priority']}* — {alert['name']}\n{alert['message']}\n⏰ {alert['time']}"
def format_for_mt5(self, alert: dict) -> str:
return f"Alert(\"{alert['name']}\", \"{alert['message']}\");"
# Preset alert conditions
def price_above(symbol: str, level: float):
return AlertCondition(f"{symbol} > {level}", lambda ctx: ctx.get(f"{symbol}_price", 0) > level, "HIGH")
def rsi_extreme(symbol: str, overbought: float = 70, oversold: float = 30):
return AlertCondition(f"{symbol} RSI extreme",
lambda ctx: ctx.get(f"{symbol}_rsi", 50) > overbought or ctx.get(f"{symbol}_rsi", 50) < oversold, "MEDIUM")
def correlation_shift(pair: str, threshold: float = 0.3):
return AlertCondition(f"{pair} corr shift",
lambda ctx: abs(ctx.get(f"{pair}_corr_deviation", 0)) > threshold, "HIGH")~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.