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 clash-resolution-analyzergit 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/clash-resolution-analyzer)<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/clash-resolution-analyzer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/clash-resolution-analyzer/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/clash-resolution-analyzer"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/clash-resolution-analyzer.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 | $0.00030 | $0.03875 |
| Opus 5 | $0.00015 | $0.01937 |
| Sonnet 5 | $0.00006 | $0.00775 |
| Haiku 4.5 | $0.00003 | $0.00387 |
Grade A, and why
clash-resolution-analyzer 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 7d 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:
- clash-resolution-analyzer — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 476 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Clash Resolution Analyzer for Construction
Overview
Analyze clash detection results from BIM coordination. Prioritize clashes by impact, identify patterns, suggest resolutions, assign responsibility, and track resolution progress.
Business Case
Clash resolution analysis enables:
- Efficient Coordination: Focus on critical clashes first
- Pattern Recognition: Fix root causes, not symptoms
- Clear Accountability: Assign responsibility by trade
- Progress Tracking: Monitor resolution status
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional, Tuple
from enum import Enum
from datetime import datetime
from collections import defaultdict
class ClashPriority(Enum):
CRITICAL = 1 # Must resolve before construction
HIGH = 2 # Resolve in next coordination cycle
MEDIUM = 3 # Resolve before trade starts
LOW = 4 # Minor, can resolve in field
class ClashStatus(Enum):
NEW = "new"
ASSIGNED = "assigned"
IN_PROGRESS = "in_progress"
RESOLVED = "resolved"
APPROVED = "approved"
VOID = "void" # Not a real clash
class ResolutionType(Enum):
ROUTE_AROUND = "Route around obstruction"
RAISE_LOWER = "Raise or lower element"
RESIZE = "Resize element"
RELOCATE = "Relocate element"
STRUCTURAL_MOD = "Structural modification required"
DESIGN_CHANGE = "Design change required"
NO_CLASH = "Not a real clash (tolerance)"
SEQUENCE = "Resolve by construction sequence"
@dataclass
class ClashElement:
id: str
name: str
category: str
discipline: str
level: str
system: str
@dataclass
class Clash:
id: str
name: str
element1: ClashElement
element2: ClashElement
location: Tuple[float, float, float]
distance: float # Negative = hard clash, positive = clearance violation
clash_type: str # hard, clearance, duplicate
priority: ClashPriority = ClashPriority.MEDIUM
status: ClashStatus = ClashStatus.NEW
assigned_to: str = ""
resolution_type: Optional[ResolutionType] = None
resolution_notes: str = ""
created_date: datetime = field(default_factory=datetime.now)
resolved_date: Optional[datetime] = None
@dataclass
class ClashPattern:
pattern_type: str
disciplines: Tuple[str, str]
systems: Tuple[str, str]
clash_count: int
example_clashes: List[str]
suggested_resolution: str
root_cause: str
@dataclass
class ClashReport:
report_name: str
total_clashes: int
new_clashes: int
resolved_clashes: int
clashes_by_priority: Dict[str, int]
clashes_by_discipline: Dict[str, int]
clashes_by_status: Dict[str, int]
patterns: List[ClashPattern]
resolution_rate: float
class ClashResolutionAnalyzer:
"""Analyze and manage BIM clash detection results."""
# Discipline priority for resolution responsibility
DISCIPLINE_PRIORITY = {
'Structural': 1,
'Architectural': 2,
'Mechanical': 3,
'Plumbing': 4,
'Electrical': 5,
'Fire Protection': 6,
}
# Common resolution strategies by clash type
RESOLUTION_STRATEGIES = {
('Mechanical', 'Structural'): {
'strategy': ResolutionType.ROUTE_AROUND,
'responsible': 'Mechanical',
'notes': 'MEP typically routes around structure'
},
('Plumbing', 'Structural'): {
'strategy': ResolutionType.ROUTE_AROUND,
'responsible': 'Plumbing',
'notes': 'Coordinate sleeves/penetrations with SE'
},
('Electrical', 'Mechanical'): {
'strategy': ResolutionType.RAISE_LOWER,
'responsible': 'Electrical',
'notes': 'Conduit typically more flexible than ductwork'
},
('Mechanical', 'Mechanical'): {
'strategy': ResolutionType.RESIZE,
'responsible': 'Mechanical',
'notes': 'Review duct sizing and routing options'
},
('Fire Protection', 'Mechanical'): {
'strategy': ResolutionType.ROUTE_AROUND,
'responsible': 'Fire Protection',
'notes': 'Sprinkler typically routes around major duct'
},
}
def __init__(self):
self.clashes: Dict[str, Clash] = {}
self.patterns: List[ClashPattern] = []
self.history: List[Dict] = []
def import_clashes(self, clash_data: List[Dict]) -> int:
"""Import clashes from Navisworks or other clash detection software."""
count = 0
for data in clash_data:
clash = Clash(
id=data.get('id', f'CLH-{count}'),
name=data.get('name', ''),
element1=ClashElement(
id=data.get('element1_id', ''),
name=data.get('element1_name', ''),
category=data.get('element1_category', ''),
discipline=data.get('element1_discipline', ''),
level=data.get('element1_level', ''),
system=data.get('element1_system', '')
),
element2=ClashElement(
id=data.get('element2_id', ''),
name=data.get('element2_name', ''),
category=data.get('element2_category', ''),
discipline=data.get('element2_discipline', ''),
level=data.get('element2_level', ''),
system=data.get('element2_system', '')
),
location=(
data.get('x', 0),
data.get('y', 0),
data.get('z', 0)
),
distance=data.get('distance', 0),
clash_type=data.get('clash_type', 'hard')
)
# Auto-prioritize
clash.priority = self._auto_prioritize(clash)
# Auto-assign
clash.assigned_to = self._auto_assign(clash)
self.clashes[clash.id] = clash
count += 1
return count
def _auto_prioritize(self, clash: Clash) -> ClashPriority:
"""Automatically prioritize clash based on characteristics."""
# Hard clashes with structure are critical
if clash.element1.discipline == 'Structural' or clash.element2.discipline == 'Structural':
if clash.clash_type == 'hard':
return ClashPriority.CRITICAL
# Large penetration clashes
if abs(clash.distance) > 0.1: # More than 100mm overlap
return ClashPriority.HIGH
# MEP-MEP clashes
mep_disciplines = ['Mechanical', 'Electrical', 'Plumbing', 'Fire Protection']
if clash.element1.discipline in mep_disciplines and clash.element2.discipline in mep_disciplines:
return ClashPriority.MEDIUM
# Clearance violations
if clash.clash_type == 'clearance':
return ClashPriority.LOW
return ClashPriority.MEDIUM
def _auto_assign(self, clash: Clash) -> str:
"""Automatically assign responsibility based on discipline priority."""
d1 = clash.element1.discipline
d2 = clash.element2.discipline
# Check for known resolution strategy
key = (d1, d2) if (d1, d2) in self.RESOLUTION_STRATEGIES else (d2, d1)
if key in self.RESOLUTION_STRATEGIES:
return self.RESOLUTION_STRATEGIES[key]['responsible']
# Default to lower priority discipline (typically more flexible)
p1 = self.DISCIPLINE_PRIORITY.get(d1, 10)
p2 = self.DISCIPLINE_PRIORITY.get(d2, 10)
return d2 if p2 > p1 else d1
def analyze_patterns(self) -> List[ClashPattern]:
"""Identify patterns in clashes."""
patterns = []
# Group by discipline pair
discipline_pairs = defaultdict(list)
for clash in self.clashes.values():
pair = tuple(sorted([clash.element1.discipline, clash.element2.discipline]))
discipline_pairs[pair].append(clash)
for (d1, d2), clashes in discipline_pairs.items():
if len(clashes) >= 3: # Pattern threshold
# Further group by system
system_pairs = defaultdict(list)
for clash in clashes:
sys_pair = tuple(sorted([clash.element1.system, clash.element2.system]))
system_pairs[sys_pair].append(clash)
for (s1, s2), sys_clashes in system_pairs.items():
if len(sys_clashes) >= 2:
# Get resolution strategy
key = (d1, d2) if (d1, d2) in self.RESOLUTION_STRATEGIES else (d2, d1)
strategy = self.RESOLUTION_STRATEGIES.get(key, {})
patterns.append(ClashPattern(
pattern_type=f"{d1} vs {d2}",
disciplines=(d1, d2),
systems=(s1, s2),
clash_count=len(sys_clashes),
example_clashes=[c.id for c in sys_clashes[:3]],
suggested_resolution=strategy.get('strategy', ResolutionType.ROUTE_AROUND).value,
root_cause=f"Coordination needed between {s1} and {s2} systems"
))
self.patterns = sorted(patterns, key=lambda p: -p.clash_count)
return self.patterns
def suggest_resolution(self, clash_id: str) -> Dict:
"""Suggest resolution for a specific clash."""
if clash_id not in self.clashes:
return {'error': 'Clash not found'}
clash = self.clashes[clash_id]
d1, d2 = clash.element1.discipline, clash.element2.discipline
# Get strategy
key = (d1, d2) if (d1, d2) in self.RESOLUTION_STRATEGIES else (d2, d1)
strategy = self.RESOLUTION_STRATEGIES.get(key, {})
suggestion = {
'clash_id': clash_id,
'resolution_type': strategy.get('strategy', ResolutionType.ROUTE_AROUND),
'responsible_discipline': strategy.get('responsible', self._auto_assign(clash)),
'notes': strategy.get('notes', 'Review and coordinate'),
'similar_clashes': [],
}
# Find similar clashes
for pattern in self.patterns:
if d1 in pattern.disciplines and d2 in pattern.disciplines:
suggestion['similar_clashes'] = pattern.example_clashes
suggestion['pattern_root_cause'] = pattern.root_cause
break
return suggestion
def update_clash_status(self, clash_id: str, status: ClashStatus,
resolution_type: ResolutionType = None,
notes: str = "") -> bool:
"""Update clash status."""
if clash_id not in self.clashes:
return False
clash = self.clashes[clash_id]
old_status = clash.status
clash.status = status
if resolution_type:
clash.resolution_type = resolution_type
if notes:
clash.resolution_notes = notes
if status in [ClashStatus.RESOLVED, ClashStatus.APPROVED]:
clash.resolved_date = datetime.now()
# Track history
self.history.append({
'clash_id': clash_id,
'timestamp': datetime.now(),
'old_status': old_status.value,
'new_status': status.value,
'notes': notes
})
return True
def get_clashes_by_discipline(self, discipline: str) -> List[Clash]:
"""Get all clashes assigned to a discipline."""
return [c for c in self.clashes.values() if c.assigned_to == discipline]
def get_clashes_by_level(self, level: str) -> List[Clash]:
"""Get all clashes on a specific level."""
return [c for c in self.clashes.values()
if c.element1.level == level or c.element2.level == level]
def generate_coordination_matrix(self) -> Dict[str, Dict[str, int]]:
"""Generate matrix showing clashes between disciplines."""
matrix = defaultdict(lambda: defaultdict(int))
for clash in self.clashes.values():
d1 = clash.element1.discipline
d2 = clash.element2.discipline
matrix[d1][d2] += 1
if d1 != d2:
matrix[d2][d1] += 1
return dict(matrix)
def generate_report(self) -> ConsistencyReport:
"""Generate comprehensive clash analysis report."""
clashes_by_priority = defaultdict(int)
clashes_by_discipline = defaultdict(int)
clashes_by_status = defaultdict(int)
for clash in self.clashes.values():
clashes_by_priority[clash.priority.name] += 1
clashes_by_discipline[clash.assigned_to] += 1
clashes_by_status[clash.status.value] += 1
resolved = clashes_by_status.get('resolved', 0) + clashes_by_status.get('approved', 0)
resolution_rate = resolved / len(self.clashes) * 100 if self.clashes else 0
return ClashReport(
report_name=f"Clash Report {datetime.now().strftime('%Y-%m-%d')}",
total_clashes=len(self.clashes),
new_clashes=clashes_by_status.get('new', 0),
resolved_clashes=resolved,
clashes_by_priority=dict(clashes_by_priority),
clashes_by_discipline=dict(clashes_by_discipline),
clashes_by_status=dict(clashes_by_status),
patterns=self.patterns,
resolution_rate=resolution_rate
)
def generate_report_markdown(self) -> str:
"""Generate markdown report."""
report = self.generate_report()
lines = ["# Clash Resolution Report", ""]
lines.append(f"**Date:** {datetime.now().strftime('%Y-%m-%d')}")
lines.append(f"**Total Clashes:** {report.total_clashes}")
lines.append(f"**Resolution Rate:** {report.resolution_rate:.1f}%")
lines.append("")
# By status
lines.append("## Status Summary")
for status, count in report.clashes_by_status.items():
lines.append(f"- {status}: {count}")
lines.append("")
# By priority
lines.append("## Priority Breakdown")
for priority, count in sorted(report.clashes_by_priority.items()):
lines.append(f"- {priority}: {count}")
lines.append("")
# By discipline
lines.append("## By Responsible Discipline")
for disc, count in sorted(report.clashes_by_discipline.items(), key=lambda x: -x[1]):
lines.append(f"- {disc}: {count}")
lines.append("")
# Patterns
if report.patterns:
lines.append("## Clash Patterns Identified")
for pattern in report.patterns[:5]:
lines.append(f"\n### {pattern.pattern_type}")
lines.append(f"- **Count:** {pattern.clash_count} clashes")
lines.append(f"- **Systems:** {pattern.systems[0]} vs {pattern.systems[1]}")
lines.append(f"- **Root Cause:** {pattern.root_cause}")
lines.append(f"- **Suggested Resolution:** {pattern.suggested_resolution}")
# Critical clashes
critical = [c for c in self.clashes.values() if c.priority == ClashPriority.CRITICAL and c.status == ClashStatus.NEW]
if critical:
lines.append("\n## Critical Unresolved Clashes")
for clash in critical[:10]:
lines.append(f"- **{clash.id}**: {clash.element1.name} vs {clash.element2.name}")
lines.append(f" - Location: Level {clash.element1.level}")
lines.append(f" - Assigned: {clash.assigned_to}")
return "\n".join(lines)
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.
- 7d ago First seen · 476 lines · 30 tokens per session scan A 773729ac2877
clash-resolution-analyzer is a skill published in the GitHub repository datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction (308 stars, last pushed 20d ago), licensed MIT. It adds 30 tokens to every session and 3,875 once invoked, about $0.0002 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 2026-09-03.
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