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 jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction --skill excel-to-bimgit clone --depth 1 https://github.com/jdmorag97-rgb/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/excel-to-bim)<a href="https://agentmods.dev/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/excel-to-bim"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/excel-to-bim/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/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/excel-to-bim"><img src="https://agentmods.dev/badge/skills/jdmorag97-rgb/ddc_skills_for_ai_agents_in_construction/excel-to-bim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.02656 |
| Opus 5 | $0.00012 | $0.01328 |
| Sonnet 5 | $0.00005 | $0.00531 |
| Haiku 4.5 | $0.00002 | $0.00266 |
Grade A, and why
excel-to-bim scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(cmd, capture_output=True, text=True) This is a copy
100% identical to excel-to-bim — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 402 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Excel to BIM Update
Business Case
Problem Statement
After extracting BIM data to Excel and enriching it (cost codes, classifications, custom data):
- Changes need to flow back to the BIM model
- Manual re-entry is error-prone
- Updates must match by element ID
Solution
Push Excel data back to BIM models, updating element parameters and properties from spreadsheet changes.
Business Value
- Bi-directional workflow - BIM → Excel → BIM
- Bulk updates - Change thousands of parameters
- Data enrichment - Add classifications, codes, costs
- Consistency - Spreadsheet as single source of truth
Technical Implementation
Workflow
BIM Model (Revit/IFC) → Excel Export → Data Enrichment → Excel Update → BIM Model
Python Implementation
import pandas as pd
from pathlib import Path
from typing import Dict, Any, List, Optional, Tuple
from dataclasses import dataclass, field
from enum import Enum
import json
class UpdateType(Enum):
"""Type of BIM parameter update."""
TEXT = "text"
NUMBER = "number"
BOOLEAN = "boolean"
ELEMENT_ID = "element_id"
@dataclass
class ParameterMapping:
"""Mapping between Excel column and BIM parameter."""
excel_column: str
bim_parameter: str
update_type: UpdateType
transform: Optional[str] = None # Optional transformation
@dataclass
class UpdateResult:
"""Result of single element update."""
element_id: str
parameters_updated: List[str]
success: bool
error: Optional[str] = None
@dataclass
class BatchUpdateResult:
"""Result of batch update operation."""
total_elements: int
updated: int
failed: int
skipped: int
results: List[UpdateResult]
class ExcelToBIMUpdater:
"""Update BIM models from Excel data."""
# Standard ID column names
ID_COLUMNS = ['ElementId', 'GlobalId', 'GUID', 'Id', 'UniqueId']
def __init__(self):
self.mappings: List[ParameterMapping] = []
def add_mapping(self, excel_col: str, bim_param: str,
update_type: UpdateType = UpdateType.TEXT):
"""Add column to parameter mapping."""
self.mappings.append(ParameterMapping(
excel_column=excel_col,
bim_parameter=bim_param,
update_type=update_type
))
def load_excel(self, file_path: str,
sheet_name: str = None) -> pd.DataFrame:
"""Load Excel data for update."""
if sheet_name:
return pd.read_excel(file_path, sheet_name=sheet_name)
return pd.read_excel(file_path)
def detect_id_column(self, df: pd.DataFrame) -> Optional[str]:
"""Detect element ID column in DataFrame."""
for col in self.ID_COLUMNS:
if col in df.columns:
return col
# Case-insensitive check
for df_col in df.columns:
if df_col.lower() == col.lower():
return df_col
return None
def prepare_updates(self, df: pd.DataFrame,
id_column: str = None) -> List[Dict[str, Any]]:
"""Prepare update instructions from DataFrame."""
if id_column is None:
id_column = self.detect_id_column(df)
if id_column is None:
raise ValueError("Cannot detect ID column")
updates = []
for _, row in df.iterrows():
element_id = str(row[id_column])
params = {}
for mapping in self.mappings:
if mapping.excel_column in df.columns:
value = row[mapping.excel_column]
# Convert value based on type
if mapping.update_type == UpdateType.NUMBER:
value = float(value) if pd.notna(value) else 0
elif mapping.update_type == UpdateType.BOOLEAN:
value = bool(value) if pd.notna(value) else False
elif mapping.update_type == UpdateType.TEXT:
value = str(value) if pd.notna(value) else ""
params[mapping.bim_parameter] = value
if params:
updates.append({
'element_id': element_id,
'parameters': params
})
return updates
def generate_dynamo_script(self, updates: List[Dict],
output_path: str) -> str:
"""Generate Dynamo script for Revit updates."""
# Generate Python code for Dynamo
script = '''
# Dynamo Python Script for Revit Parameter Updates
# Generated by DDC Excel-to-BIM
import clr
clr.AddReference('RevitAPI')
clr.AddReference('RevitServices')
from RevitServices.Persistence import DocumentManager
from RevitServices.Transactions import TransactionManager
from Autodesk.Revit.DB import *
doc = DocumentManager.Instance.CurrentDBDocument
# Update data
updates = '''
script += json.dumps(updates, indent=2)
script += '''
# Apply updates
TransactionManager.Instance.EnsureInTransaction(doc)
results = []
for update in updates:
try:
element_id = int(update['element_id'])
element = doc.GetElement(ElementId(element_id))
if element:
for param_name, value in update['parameters'].items():
param = element.LookupParameter(param_name)
if param and not param.IsReadOnly:
if isinstance(value, (int, float)):
param.Set(float(value))
elif isinstance(value, bool):
param.Set(1 if value else 0)
else:
param.Set(str(value))
results.append({'id': element_id, 'status': 'success'})
else:
results.append({'id': element_id, 'status': 'not found'})
except Exception as e:
results.append({'id': update['element_id'], 'status': str(e)})
TransactionManager.Instance.TransactionTaskDone()
OUT = results
'''
with open(output_path, 'w') as f:
f.write(script)
return output_path
def generate_ifc_updates(self, updates: List[Dict],
original_ifc: str,
output_ifc: str) -> str:
"""Generate updated IFC file (requires IfcOpenShell)."""
try:
import ifcopenshell
except ImportError:
raise ImportError("IfcOpenShell required for IFC updates")
ifc = ifcopenshell.open(original_ifc)
for update in updates:
guid = update['element_id']
# Find element by GUID
element = ifc.by_guid(guid)
if not element:
continue
# Update properties
for param_name, value in update['parameters'].items():
# This is simplified - actual IFC property handling is more complex
# Would need to find/create property sets and properties
pass
ifc.write(output_ifc)
return output_ifc
def generate_update_report(self, original_df: pd.DataFrame,
updates: List[Dict],
output_path: str) -> str:
"""Generate report of planned updates."""
report_data = []
for update in updates:
for param, value in update['parameters'].items():
report_data.append({
'element_id': update['element_id'],
'parameter': param,
'new_value': value
})
report_df = pd.DataFrame(report_data)
report_df.to_excel(output_path, index=False)
return output_path
class RevitExcelUpdater(ExcelToBIMUpdater):
"""Specialized updater for Revit via ImportExcelToRevit."""
def __init__(self, tool_path: str = "ImportExcelToRevit.exe"):
super().__init__()
self.tool_path = Path(tool_path)
def update_revit(self, excel_file: str,
rvt_file: str,
sheet_name: str = "Elements") -> BatchUpdateResult:
"""Update Revit file from Excel using CLI tool."""
import subprocess
# This assumes ImportExcelToRevit CLI tool
cmd = [
str(self.tool_path),
rvt_file,
excel_file,
sheet_name
]
result = subprocess.run(cmd, capture_output=True, text=True)
# Parse results (format depends on tool output)
if result.returncode == 0:
return BatchUpdateResult(
total_elements=0, # Would parse from output
updated=0,
failed=0,
skipped=0,
results=[]
)
else:
raise RuntimeError(f"Update failed: {result.stderr}")
class DataEnrichmentWorkflow:
"""Complete workflow for data enrichment and update."""
def __init__(self):
self.updater = ExcelToBIMUpdater()
def enrich_and_update(self, original_excel: str,
enrichment_excel: str,
merge_column: str) -> pd.DataFrame:
"""Merge enrichment data with original export."""
original = pd.read_excel(original_excel)
enrichment = pd.read_excel(enrichment_excel)
# Merge on specified column
merged = original.merge(enrichment, on=merge_column, how='left',
suffixes=('', '_enriched'))
return merged
def create_classification_mapping(self, df: pd.DataFrame,
type_column: str,
classification_file: str) -> pd.DataFrame:
"""Map BIM types to classification codes."""
classifications = pd.read_excel(classification_file)
# Fuzzy matching could be added here
merged = df.merge(classifications,
left_on=type_column,
right_on='type_description',
how='left')
return merged
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 · 402 lines · 24 tokens per session scan A bc26fae3d307
excel-to-bim is a skill published in the GitHub repository jdmorag97-rgb/DDC_Skills_for_AI_Agents_in_Construction (2 stars, last pushed 6mo ago), licensed MIT. It adds 24 tokens to every session and 2,656 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). It is 100% identical to excel-to-bim, differing in 0 lines, and is treated as a copy.
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