OpenClaw

data-anomaly-detector

Detect anomalies and outliers in construction data: unusual costs, schedule variances, productivity spikes. Statistical and ML-based detection methods.

datadrivenconstruction0 来源安装数☆ 0 仓库 Stars

查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

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SkillHub CLI

skillhub install datadrivenconstruction/data-anomaly-detector

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技能说明

Data Anomaly Detector for Construction

Overview

Detect unusual patterns, outliers, and anomalies in construction data. Identify cost overruns, schedule delays, productivity issues, and data quality problems before they impact projects.

Business Case

Construction data often contains anomalies that indicate:

Cost estimate errors or fraud
Schedule logic issues
Productivity problems
Data entry mistakes
Equipment or material issues

Early detection prevents costly corrections and project delays.

Technical Implementation

from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
from enum import Enum
import pandas as pd
import numpy as np
from datetime import datetime
from scipy import stats

class AnomalyType(Enum):
    OUTLIER = "outlier"
    PATTERN_BREAK = "pattern_break"
    MISSING_SEQUENCE = "missing_sequence"
    DUPLICATE = "duplicate"
    IMPOSSIBLE_VALUE = "impossible_value"
    TREND_DEVIATION = "trend_deviation"

class AnomalySeverity(Enum):
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"

@dataclass
class Anomaly:
    id: str
    anomaly_type: AnomalyType
    severity: AnomalySeverity
    field: str
    value: Any
    expected_range: Optional[Tuple[float, float]] = None
    description: str = ""
    row_index: Optional[int] = None
    detection_method: str = ""
    confidence: float = 0.0
    suggested_action: str = ""

@dataclass
class AnomalyReport:
    source: str
    detected_at: datetime
    total_records: int
    anomalies: List[Anomaly]
    summary: Dict[str, int]

class ConstructionAnomalyDetector:
    """Detect anomalies in construction data."""

    # Construction-specific thresholds
    COST_THRESHOLDS = {
        'concrete_per_cy': (200, 800),
        'steel_per_ton': (1500, 4000),
        'labor_per_hour': (25, 150),
        'overhead_percentage': (5, 25),
        'conti...

来源信息

Source: github
Repository: openclaw/skills
Path: skills/datadrivenconstruction/data-anomaly-detector
Version: —
Commit: 4b0ee5bba1c56020029ab581ac186a8a45308ce4
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# Data Anomaly Detector for Construction

## Overview

Detect unusual patterns, outliers, and anomalies in construction data. Identify cost overruns, schedule delays, productivity issues, and data quality problems before they impact projects.

## Business Case

Construction data often contains anomalies that indicate:
- Cost estimate errors or fraud
- Schedule logic issues
- Productivity problems
- Data entry mistakes
- Equipment or material issues

Early detection prevents costly corrections and project delays.

## Technical Implementation

```python
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
from enum import Enum
import pandas as pd
import numpy as np
from datetime import datetime
from scipy import stats

class AnomalyType(Enum):
    OUTLIER = "outlier"
    PATTERN_BREAK = "pattern_break"
    MISSING_SEQUENCE = "missing_sequence"
    DUPLICATE = "duplicate"
    IMPOSSIBLE_VALUE = "impossible_value"
    TREND_DEVIATION = "trend_deviation"

class AnomalySeverity(Enum):
    CRITICAL = "critical"
    HIGH = "high"
    MEDIUM = "medium"
    LOW = "low"

@dataclass
class Anomaly:
    id: str
    anomaly_type: AnomalyType
    severity: AnomalySeverity
    field: str
    value: Any
    expected_range: Optional[Tuple[float, float]] = None
    description: str = ""
    row_index: Optional[int] = None
    detection_method: str = ""
    confidence: float = 0.0
    suggested_action: str = ""

@dataclass
class AnomalyReport:
    source: str
    detected_at: datetime
    total_records: int
    anomalies: List[Anomaly]
    summary: Dict[str, int]

class ConstructionAnomalyDetector:
    """Detect anomalies in construction data."""

    # Construction-specific thresholds
    COST_THRESHOLDS = {
        'concrete_per_cy': (200, 800),
        'steel_per_ton': (1500, 4000),
        'labor_per_hour': (25, 150),
        'overhead_percentage': (5, 25),
        'conti...

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