
bim-clash-detection
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
- Installs
- 156
- Last 24h
- —
- Trending rank
- —
- Security
- Not audited
Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill bim-clash-detection- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads bim-clash-detection automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
BIM Clash Detection
Business Case
Problem Statement
Coordination issues cause significant rework:
- MEP vs structural conflicts discovered on site
- Late design changes increase costs
- Manual clash review is time-consuming
- No standardized clash categorization
Solution
Automated clash detection and analysis system that identifies conflicts between building systems and provides prioritized resolution recommendations.
Business Value
- Cost savings - Detect issues before construction
- Time reduction - Automated clash identification
- Better coordination - Systematic conflict resolution
- Quality improvement - Fewer field issues
Technical Implementation
import pandas as pd
from datetime import datetime
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import math
class ClashType(Enum):
"""Types of clashes."""
HARD = "hard" # Physical intersection
SOFT = "soft" # Clearance violation
WORKFLOW = "workflow" # Sequencing conflict
DUPLICATE = "duplicate" # Duplicated elements
class ClashStatus(Enum):
"""Clash resolution status."""
NEW = "new"
ACTIVE = "active"
RESOLVED = "resolved"
APPROVED = "approved"
IGNORED = "ignored"
class ClashSeverity(Enum):
"""Clash severity level."""
CRITICAL = "critical"
MAJOR = "major"
MINOR = "minor"
INFO = "info"
class Discipline(Enum):
"""BIM disciplines."""
ARCHITECTURAL = "architectural"
STRUCTURAL = "structural"
MECHANICAL = "mechanical"
ELECTRICAL = "electrical"
PLUMBING = "plumbing"
FIRE_PROTECTION = "fire_protection"
CIVIL = "civil"
@dataclass
class BoundingBox:
"""3D bounding box."""
min_x: float
min_y: float
min_z: float
max_x: float
max_y: float
max_z: float
def intersects(self, other: 'BoundingBox') -> bool:
"""Check if boxes intersect."""
return (self.min_x <= other.max_x and self.max_x >= other.min_x and
self.min_y <= other.max_y and self.max_y >= other.min_y and
self.min_z <= other.max_z and self.max_z >= other.min_z)
def volume(self) -> float:
"""Calculate bounding box volume."""
return ((self.max_x - self.min_x) *
(self.max_y - self.min_y) *
(self.max_z - self.min_z))
def center(self) -> Tuple[float, float, float]:
"""Get center point."""
return (
(self.min_x + self.max_x) / 2,
(self.min_y + self.max_y) / 2,
(self.min_z + self.max_z) / 2
)
@dataclass
class BIMElement:
"""BIM element representation."""
element_id: str
name: str
discipline: Discipline
category: str # e.g., "Duct", "Beam", "Pipe"
level: str
bounding_box: BoundingBox
properties: Dict[str, Any] = field(default_factory=dict)
def distance_to(self, other: 'BIMElement') -> float:
"""Calculate distance between element centers."""
c1 = self.bounding_box.center()
c2 = other.bounding_box.center()
return math.sqrt(
(c2[0] - c1[0])**2 +
(c2[1] - c1[1])**2 +
(c2[2] - c1[2])**2
)
@dataclass
class Clash:
"""Clash between two elements."""
clash_id: str
element_a: BIMElement
element_b: BIMElement
clash_type: ClashType
severity: ClashSeverity
status: ClashStatus
distance: float # Penetration depth (negative) or clearance gap
location: Tuple[float, float, float]
detected_at: datetime
resolved_at: Optional[datetime] = None
assigned_to: Optional[str] = None
notes: str = ""
def to_dict(self) -> Dict[str, Any]:
return {
'clash_id': self.clash_id,
'element_a_id': self.element_a.element_id,
'element_a_name': self.element_a.name,
'element_a_discipline': self.element_a.discipline.value,
'element_b_id': self.element_b.element_id,
'element_b_name': self.element_b.name,
'element_b_discipline': self.element_b.discipline.value,
'clash_type': self.clash_type.value,
'severity': self.severity.value,
'status': self.status.value,
'distance': round(self.distance, 3),
'location_x': self.location[0],
'location_y': self.location[1],
'location_z': self.location[2],
'level': self.element_a.level,
'detected_at': self.detected_at.isoformat(),
'assigned_to': self.assigned_to,
'notes': self.notes
}
@dataclass
class ClashTest:
"""Clash test configuration."""
name: str
discipline_a: Discipline
discipline_b: Discipline
clash_type: ClashType
tolerance: float = 0.0 # Clearance tolerance in meters
enabled: bool = True
class BIMClashDetector:
"""Detect and manage BIM clashes."""
def __init__(self):
self.elements: List[BIMElement] = []
self.clashes: List[Clash] = []
self.clash_tests: List[ClashTest] = []
self._clash_counter = 0
def load_elements(self, elements_df: pd.DataFrame) -> int:
"""Load BIM elements from DataFrame."""
loaded = 0
for _, row in elements_df.iterrows():
element = BIMElement(
element_id=str(row.get('element_id', '')),
name=str(row.get('name', '')),
discipline=Discipline(row.get('discipline', 'architectural')),
category=str(row.get('category', '')),
level=str(row.get('level', '')),
bounding_box=BoundingBox(
min_x=float(row.get('min_x', 0)),
min_y=float(row.get('min_y', 0)),
min_z=float(row.get('min_z', 0)),
max_x=float(row.get('max_x', 0)),
max_y=float(row.get('max_y', 0)),
max_z=float(row.get('max_z', 0))
)
)
self.elements.append(element)
loaded += 1
return loaded
def add_clash_test(self, test: ClashTest):
"""Add clash test configuration."""
self.clash_tests.append(test)
def setup_standard_tests(self):
"""Setup standard MEP coordination tests."""
standard_tests = [
ClashTest("MEP vs Structure", Discipline.MECHANICAL, Discipline.STRUCTURAL, ClashType.HARD),
ClashTest("Electrical vs Structure", Discipline.ELECTRICAL, Discipline.STRUCTURAL, ClashType.HARD),
ClashTest("Plumbing vs Structure", Discipline.PLUMBING, Discipline.STRUCTURAL, ClashType.HARD),
ClashTest("MEP vs MEP", Discipline.MECHANICAL, Discipline.ELECTRICAL, ClashType.HARD),
ClashTest("Duct Clearance", Discipline.MECHANICAL, Discipline.MECHANICAL, ClashType.SOFT, tolerance=0.05),
ClashTest("Fire Protection", Discipline.FIRE_PROTECTION, Discipline.STRUCTURAL, ClashType.HARD),
]
for test in standard_tests:
self.add_clash_test(test)
def run_clash_detection(self) -> List[Clash]:
"""Run all clash tests."""
new_clashes = []
for test in self.clash_tests:
if not test.enabled:
continue
# Filter elements by discipline
elements_a = [e for e in self.elements if e.discipline == test.discipline_a]
elements_b = [e for e in self.elements if e.discipline == test.discipline_b]
# Check all pairs
for elem_a in elements_a:
for elem_b in elements_b:
if elem_a.element_id == elem_b.element_id:
continue
clash = self._check_clash(elem_a, elem_b, test)
if clash:
new_clashes.append(clash)
self.clashes.extend(new_clashes)
return new_clashes
def _check_clash(self, elem_a: BIMElement, elem_b: BIMElement,
test: ClashTest) -> Optional[Clash]:
"""Check if two elements clash."""
# Expand bounding box by tolerance for soft clashes
box_a = elem_a.bounding_box
box_b = elem_b.bounding_box
if test.clash_type == ClashType.SOFT:
# Add clearance tolerance
expanded_a = BoundingBox(
box_a.min_x - test.tolerance, box_a.min_y - test.tolerance, box_a.min_z - test.tolerance,
box_a.max_x + test.tolerance, box_a.max_y + test.tolerance, box_a.max_z + test.tolerance
)
intersects = expanded_a.intersects(box_b)
else:
intersects = box_a.intersects(box_b)
if not intersects:
return None
# Calculate clash point and severity
self._clash_counter += 1
clash_id = f"CLH-{self._clash_counter:05d}"
# Clash location (center of intersection)
location = (
(max(box_a.min_x, box_b.min_x) + min(box_a.max_x, box_b.max_x)) / 2,
(max(box_a.min_y, box_b.min_y) + min(box_a.max_y, box_b.max_y)) / 2,
(max(box_a.min_z, box_b.min_z) + min(box_a.max_z, box_b.max_z)) / 2
)
# Calculate penetration depth
distance = elem_a.distance_to(elem_b)
# Determine severity
if test.clash_type == ClashType.HARD:
severity = ClashSeverity.CRITICAL if distance < 0.1 else ClashSeverity.MAJOR
else:
severity = ClashSeverity.MINOR if distance > test.tolerance else ClashSeverity.MAJOR
return Clash(
clash_id=clash_id,
element_a=elem_a,
element_b=elem_b,
clash_type=test.clash_type,
severity=severity,
status=ClashStatus.NEW,
distance=distance,
location=location,
detected_at=datetime.now()
)
def get_summary(self) -> Dict[str, Any]:
"""Get clash detection summary."""
by_severity = {}
by_discipline = {}
by_status = {}
for clash in self.clashes:
# By severity
sev = clash.severity.value
by_severity[sev] = by_severity.get(sev, 0) + 1
# By discipline pair
pair = f"{clash.element_a.discipline.value} vs {clash.element_b.discipline.value}"
by_discipline[pair] = by_discipline.get(pair, 0) + 1
# By status
stat = clash.status.value
by_status[stat] = by_status.get(stat, 0) + 1
return {
'total_clashes': len(self.clashes),
'by_severity': by_severity,
'by_discipline': by_discipline,
'by_status': by_status,
'elements_checked': len(self.elements),
'tests_run': len([t for t in self.clash_tests if t.enabled])
}
def export_to_dataframe(self) -> pd.DataFrame:
"""Export clashes to DataFrame."""
return pd.DataFrame([c.to_dict() for c in self.clashes])
def resolve_clash(self, clash_id: str, resolution_note: str):
"""Mark clash as resolved."""
for clash in self.clashes:
if clash.clash_id == clash_id:
clash.status = ClashStatus.RESOLVED
clash.resolved_at = datetime.now()
clash.notes = resolution_note
break
def assign_clash(self, clash_id: str, assignee: str):
"""Assign clash to team member."""
for clash in self.clashes:
if clash.clash_id == clash_id:
clash.assigned_to = assignee
clash.status = ClashStatus.ACTIVE
break
Quick Start
# Initialize detector
detector = BIMClashDetector()
# Setup standard MEP tests
detector.setup_standard_tests()
# Load elements from DataFrame
elements_df = pd.read_excel("bim_elements.xlsx")
detector.load_elements(elements_df)
# Run detection
clashes = detector.run_clash_detection()
print(f"Found {len(clashes)} clashes")
# Get summary
summary = detector.get_summary()
print(f"Critical: {summary['by_severity'].get('critical', 0)}")
Common Use Cases
1. MEP Coordination
# Focus on MEP vs Structure
mep_clashes = [c for c in detector.clashes
if c.element_a.discipline in [Discipline.MECHANICAL, Discipline.ELECTRICAL]]
2. Export for Review
df = detector.export_to_dataframe()
df.to_excel("clash_report.xlsx", index=False)
3. Assign to Teams
for clash in detector.clashes:
if clash.element_a.discipline == Discipline.MECHANICAL:
detector.assign_clash(clash.clash_id, "MEP Team")
Resources
- DDC Book: Chapter 2.4 - BIM Coordination
- Reference: ISO 19650 BIM Standards
FAQ
What does the bim-clash-detection skill do?
Detect and analyze geometric clashes in BIM models. Identify MEP, structural, and architectural conflicts before construction.
How do I install bim-clash-detection?
Run npx skills add https://github.com/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction --skill bim-clash-detection in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/bim-clash-detection/ (personal) or .claude/skills/bim-clash-detection/ (project).
Which AI agents can use bim-clash-detection?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is bim-clash-detection safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains bim-clash-detection?
It is published by datadrivenconstruction in datadrivenconstruction/ddc_skills_for_ai_agents_in_construction. It has 156 recorded installs.
More from datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
warranty-tracker
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Track and manage construction warranties. Monitor expiration dates, claims, and manufacturer documentation.
cad-to-data
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Convert CAD/BIM files to structured data. Extract element data from Revit, IFC, DWG, DGN files.
dwg-to-excel
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
drawing-analyzer
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Open the skill to read its SKILL.md instructions.
permit-tracking-automation
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Automate construction permit tracking and management. Monitor application status, track renewal deadlines, manage document requirements, and integrate with municipal systems.
cost-estimation-resource
datadrivenconstruction/ddc_skills_for_ai_agents_in_construction
Calculate construction costs using resource-based method. Estimate project costs from work items, physical resource norms, and current prices.
Popular ai & agents skills
google-agents-cli-publish
google/agents-cli
This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).
google-agents-cli-eval
google/agents-cli
This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).
skill-creator
anthropics/skills
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
codex-pet
prime-skills/runcomfy-agent-skills
Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.
full-output-enforcement
leonxlnx/taste-skill
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
executing-plans
obra/superpowers
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available