An intelligent assistant designed to parse complex bidding documents and generate structured, formatted analysis reports with original page citations.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bidding-document-parser
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bidding-document-parser using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Bidding Document Parser?
The Bidding Document Parser is a specialized tool within the Openclaw Skills ecosystem designed to streamline the review of procurement and tender documents. It automatically processes PDF, DOCX, and TXT files to identify critical information such as qualification requirements, disqualification triggers, and technical specifications. By utilizing custom Python scripts and precise prompting, it transforms dense legal and technical text into organized reports.
This skill is particularly valuable for bid managers and legal teams who need to ensure compliance without manually scouring hundreds of pages. It goes beyond simple text extraction by maintaining original PDF page numbering, allowing users to cross-reference extracted data with the source material instantly. The final output is a professional DOCX report formatted according to enterprise standards.
Bidding Document Parser Use Cases
- Automatically extracting qualification requirements from massive PDF tender documents.
- Identifying high-risk disqualification clauses or 'knock-out' criteria to avoid bid rejection.
- Summarizing technical requirements and scoring rubrics for project estimation.
- Generating standardized internal review reports in DOCX format for team collaboration.
- Cross-referencing bid requirements with specific page numbers for audit trails.
How Bidding Document Parser Works
- The system receives a bidding document via file upload or path and selects the appropriate parsing method based on the file extension.
- For PDF files, a specialized script using pdfplumber extracts text while preserving page number metadata.
- The skill loads a detailed extraction prompt template and injects the document content for AI analysis.
- It identifies and categorizes information into six key areas: Eligibility, Disqualification, Scoring, Technical, Binding, and Formatting.
- A structured Markdown report is generated as an intermediate step, including project metadata and formatted tables.
- Finally, a conversion script transforms the Markdown into a professional DOCX file featuring corporate branding and standardized typography.
Bidding Document Parser Setup
To use this skill within the Openclaw Skills framework, ensure you have the necessary Python dependencies installed in your environment:
pip install pdfplumber python-docx
Basic usage for PDF extraction:
# Navigate to your workspace
cd "[workspace]"
# Extract text with page numbers
python scripts/extract_pdf_with_pages.py "path/to/tender.pdf"
Generating the final report:
python scripts/md_to_docx.py "analysis_report.md" "final_bidding_report.docx"
Bidding Document Parser Data Schema & Taxonomy
The skill organizes data through a clear directory structure and standardized table schemas:
| Data Category | Description | Key Fields |
|---|---|---|
| Project Info | Header metadata | Project Name, ID, Date, Bidder, Agency |
| Core Categories | 6-part extraction | Qualification, Disqualification, Scoring, Technical, Binding, Formatting |
| Metadata | Source tracking | Original PDF Page Numbers (e.g., P5, P9-10) |
File Taxonomy:
scripts/: Contains Python logic for PDF parsing and DOCX conversion.references/: Stores theextraction_prompt.mdandreport_format.mdtemplates.[workspace]/: The destination for all generated .txt, .md, and .docx outputs.
Bidding Document Parser Advanced Features
- Support for large document processing via segmented parsing and integration.
- Automated LaTeX formula cleaning for cleaner technical specification reports.
- Custom DOCX styling engine with Microsoft YaHei font and branded table headers (#2E5496).
- Integrated Markdown validation and fixing scripts to ensure structural integrity before conversion.
- Smart merging of fragmented information scattered across different document sections into unified tables.
SKILL.md
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