Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill bim-clash-detectiongit clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_ConstructionWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-clash-detection)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-clash-detection"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-clash-detection/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-clash-detection"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/bim-clash-detection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00028 | $0.03114 |
| Opus 5 | $0.00014 | $0.01557 |
| Sonnet 5 | $0.00006 | $0.00623 |
| Haiku 4.5 | $0.00003 | $0.00311 |
Grade A, and why
bim-clash-detection scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- bim-clash-detection — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 401 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 401 lines · 28 tokens per session scan A 5abe027f0db2
bim-clash-detection is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (310 stars, last pushed 20d ago), licensed MIT. It adds 28 tokens to every session and 3,114 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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