excel-to-bim

excel-to-bim is a skill for Claude Code, Codex from datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction. It costs 24 tokens per session (2,656 once invoked), scanned A, original, MIT.

A workflow for sending spreadsheet data back into BIM models, digital building models that store information about building elements. It updates element parameters, properties, and attributes from structured Excel data.

In plain words
What is it for?
Use it to apply bulk updates from Excel, such as text, numbers, true-or-false values, element IDs, classifications, codes, or costs.
Why use it?
It avoids retyping enriched spreadsheet data into the model and can match changes to elements by their IDs.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to apply bulk updates from Excel, such as text, numbers, true-or-false values, element IDs, classifications, codes, or costs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim
Install

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.

Any agent
npx skills add datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction --skill excel-to-bim
Clone the repo
git clone --depth 1 https://github.com/datadrivenconstruction/DDC_Skills_for_AI_Agents_in_Construction

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for excel-to-bim

README.md
[![agentmods](https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim/github.svg)](https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim)
Your own site
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/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.

agentmods 80×15 button for excel-to-bim

Your own site · 80×15
<a href="https://agentmods.dev/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim"><img src="https://agentmods.dev/badge/skills/datadrivenconstruction/ddc_skills_for_ai_agents_in_construction/excel-to-bim.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 24 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,656 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Output Handling · line 293
    Model output is used without validation or sanitization. Unvalidated output injected into downstream contexts (SQL, shell, HTML) enables injection attacks and arbitrary code execution.
    Fix: Validate and sanitize all model output before using it in downstream contexts. Use parameterized queries for SQL, shell quoting for commands, and HTML encoding for web output.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 12d ago against content hash bc26fae3d307, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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)
Origin

Copies of this mod

1 near-identical copy found in the catalogue:

1_DDC_Toolkit/CAD-Converters/excel-to-bim/SKILL.md · 402 lines

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

Read the full file on GitHub · 402 lines

Files

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.

Changes

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.

  1. 12d ago First seen · 402 lines · 24 tokens per session scan A bc26fae3d307

Subscribe to this mod's changes

excel-to-bim 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 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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