What makes Bim Cost Estimation Cwicr notable?

1.2K downloads. Trust evidence available

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agentCLAWHUBUnverified

Bim Cost Estimation Cwicr

Automated cost estimation from BIM models using DDC CWICR database with 55,719 work items. AI classification + vector search for accurate pricing. Skill: Bim Cost Estimation Cwicr Owner: datadrivenconstruction Summary: Automated cost estimation from BIM models using DDC CWICR database with 55,719 work items. AI classification + vector search for accurate pricing. Tags: latest:2.1.0 Version history: v2.1.0 | 2026-02-13T21:08:30.955Z | auto - Added "homepage" and "metadata" fields to the skill manifest with relevant information. - Specified platform compatibility

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

2.1.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 2.1.0release · observed Feb 13, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s1774mv3t1cm8r1kgs9hccdnmn8852nb:bim-cost-estimation-cwicr

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-datadrivenconstruction-bim-cost-estimation-cwicr/snapshot"

Documentation

CLAWHUB

65,315 characters of source documentation, loaded on request.

Extracted files

4 files captured from the source.

SKILL.md

---
name: "bim-cost-estimation-cwicr"
description: "Automated cost estimation from BIM models using DDC CWICR database with 55,719 work items. AI classification + vector search for accurate pricing."
homepage: "https://datadrivenconstruction.io"
metadata: {"openclaw":{"emoji":"🏗️","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"],"env":["OPENAI_API_KEY","QDRANT_URL"]},"primaryEnv":"OPENAI_API_KEY"}}
---

# BIM Cost Estimation with DDC CWICR

Generate accurate cost estimates from BIM models using AI classification and the DDC CWICR construction cost database.

## Business Case

**Problem**: Traditional cost estimation:
- Manual and time-consuming (weeks for detailed estimate)
- Subjective and inconsistent between estimators
- Requires specialized knowledge
- Difficult to update with design changes

**Solution**: Automated BIM-to-cost pipeline:
- Extract quantities directly from model
- AI classifies elements to work items
- Vector search finds matching prices in CWICR
- Complete estimate in hours, not weeks

**ROI**: 80% reduction in estimation time, consistent methodology

## System Architecture

```
┌──────────────────────────────────────────────────────────────────────────┐
│                  BIM TO COST ESTIMATION PIPELINE                          │
├──────────────────────────────────────────────────────────────────────────┤
│                                                                           │
│   ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌─────────────────┐   │
│   │ BIM     │     │ DDC     │     │ AI      │     │ DDC CWICR       │   │
│   │ Model   │────►│Converter│────►│ LLM     │────►│ Vector Search   │   │
│   │.rvt/.ifc│     │         │     │         │     │ (Qdrant)        │   │
│   └─────────┘     └─────────┘     └─────────┘     └─────────────────┘   │
│                        │              │                    │             │
│                        ▼              ▼                    ▼             │
│                   ┌─────────┐    ┌─────────┐         ┌──────────┐       │
│                   │ .xlsx   │    │ Work    │         │ Matched  │       │
│                   │ QTO     │    │ Items   │         │ Rates    │       │
│                   └─────────┘    └─────────┘         └──────────┘       │
│                        │              │                    │             │
│                        └──────────────┼────────────────────┘             │
│                                       ▼                                  │
│                              ┌─────────────────┐                        │
│                              │ COST ESTIMATE   │                        │
│                              │                 │                        │
│                              │ • By element    │                        │
│                              │ • By trade      │                        │
│

_meta.json

{
  "ownerId": "kn75fhjxn1jz5xbgd9ggj0nrtd80q1dz",
  "slug": "bim-cost-estimation-cwicr",
  "version": "2.1.0",
  "publishedAt": 1771016910955
}

instructions.md

You are a BIM-to-cost estimation assistant powered by the DDC CWICR database (55,719 work items across 9 languages). You automate the full pipeline from BIM model to cost estimate using AI classification and vector search.

When the user asks to estimate costs from a BIM model:
1. Guide them through the pipeline stages: BIM export -> QTO -> AI classification -> vector search -> cost calculation
2. Explain the 10-stage pipeline (collect, detect project, generate phases, assign elements, decompose work, vector search, unit mapping, cost calculation, validation, aggregation)
3. Help configure: language/region (EN, DE, RU, ES, FR, AR, HI, PT, ZH), Qdrant connection, OpenAI API for embeddings
4. Present results by trade, phase, and element type

When the user asks about CWICR database:
1. Explain the database structure: 55,719 work items, 27,672 resources, 85 fields per item
2. Help with vector search queries using text-embedding-3-large (3072 dimensions)
3. Show matching results with confidence scores

## Input Format
- BIM model path (.rvt or .ifc) or pre-exported QTO data (.xlsx)
- Target language/region for pricing
- Qdrant URL and API credentials (environment variables)

## Output Format
- Cost estimate by trade (Concrete, Masonry, Steel, MEP, etc.)
- Cost breakdown: labor, material, equipment percentages
- Confidence analysis (high >0.85, medium 0.70-0.85, low <0.70)
- Excel report with Summary, By Trade, and Detail sheets

## Pipeline Stages
| Stage | Description |
|-------|-------------|
| 0 | Collect BIM data from Revit/IFC |
| 1-3 | AI detects project type, generates phases, assigns elements |
| 4 | AI decomposes element types into work items |
| 5 | Vector search matches work items to CWICR rates |
| 6-7 | Unit mapping and cost calculation |
| 8-9 | Aggregation and report generation |

## Constraints
- Network permission required for Qdrant vector database and OpenAI embeddings API
- Filesystem permission required for BIM model reading and Excel export
- subprocess.run() is used solely for invoking the DDC RvtExporter CAD conversion tool
- All API keys must be loaded from environment variables, never hardcoded

claw.json

{
  "name": "bim-cost-estimation-cwicr",
  "version": "2.0.0",
  "description": "Automated cost estimation from BIM models using DDC CWICR database with 55,719 work items. AI classification + vector search for accurate pricing.",
  "author": "datadrivenconstruction",
  "license": "MIT",
  "permissions": ["filesystem", "network"],
  "entry": "instructions.md",
  "tags": ["construction", "BIM", "estimation", "CWICR", "vector-search", "AI", "cost-management"],
  "models": ["claude-*", "gpt-*"],
  "minOpenClawVersion": "0.8.0"
}

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