Tabular Review Lawvable

lawve-ai/tabular-review-lawvable

Guide to analyze multiple documents (PDF, DOCX) against user-defined columns and produce a structured Excel output with citations. Use when the user wants to: (1) Extract specific information from multiple documents into a table, (2) Compare clauses or provisions across contracts, (3) Create a document review matrix with source citations. Triggers on: 'tabular review', 'document matrix', 'extract from documents', 'compare across documents', 'review multiple contracts'.

10k tokens

context cost

the whole folder, loaded on every use

files

instructions only

copies elsewhere

how many repositories repackaged it

616

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/lawve-ai/awesome-legal-skills --skill tabular-review-lawvable

What comes with it

34 863 bytes besides the instruction

LICENSE.txt

README.md

What it tells the agent to use

found in the instruction text

Glob reads your files

Task spawns other agents

The instruction itself

12 sections, as written by the author

Tabular Review

Extract structured data from multiple documents into an Excel matrix with citations.

Required Skills

pdf - For reading PDF documents
docx - For reading Word documents
xlsx - For creating the Excel output

Workflow

Step 1: Gather User Requirements

Use AskUserQuestion to collect:

Document folder path - Where are the documents?
Output filename - Name for the Excel file
Columns to extract - What information to pull from each document

Example column definitions:

- Parties: Names of all parties to the agreement
- Effective Date: When the agreement becomes effective
- Term: Duration of the agreement
- Governing Law: Jurisdiction for disputes

Step 2: Discover Documents

Use Glob to find all documents:

Glob(pattern: "**/*.pdf", path: "<folder>")
Glob(pattern: "**/*.docx", path: "<folder>")

Step 3: Process Documents in Parallel

Launch background agents to process documents concurrently. Each agent:

Reads assigned documents using pdf or docx skill
Extracts values for each column
Captures page/paragraph citations
Returns structured JSON

Launch agents:

Task(
  prompt: "<agent_prompt>",
  subagent_type: "general-purpose",
  run_in_background: true
)

Agent prompt template:

You are processing documents for a tabular review.

DOCUMENTS TO PROCESS:
<list of document paths>

COLUMNS TO EXTRACT:
<column definitions>

For each document:
1. Read the document using the pdf skill (for .pdf) or docx skill (for .docx)
2. Extract the requested information for each column
3. Note the page number (PDF) or section (DOCX) where you found the information
4. Include a brief quote (30-50 chars) showing the source text

Return your results as JSON:
{
  "results": [
    {
      "document": "<filename>",
      "path": "<absolute_path>",
      "extractions": [
        {
          "column": "<column_name>",
          "value": "<extracted_value>",
          "page": <page_number>,
          "quote": "<brief_context_quote>"
        }
      ]
    }
  ]
}

If you cannot find information for a column, set value to "Not found" and explain in the quote field.

Distribution strategy:

For N documents and M agents, each agent processes ceil(N/M) documents
Default: 10 agents maximum
Adjust based on document count

Step 4: Collect Results

Wait for all background agents to complete:

TaskOutput(task_id: "<agent_id>", block: true)

Aggregate all results into a single array of document extractions.

Step 5: Generate Excel Output

Invoke the xlsx skill to create the output file:

Create an Excel workbook at <output_path>:

SHEET 1: "Document Review"
- Header row: Document | <Column1> | <Column2> | ...
- Data rows: One row per document

For each extraction cell:
- Cell value: The extracted text
- Cell hyperlink: file://<document_path>#page=<N> (for PDFs)
- Cell comment: "Page <N>: '<quote>'"

SHEET 2: "Summary"
- Total documents: <count>
- Documents processed: <count>
- Extraction date: <today>

JSON Schema

Extraction result format:

{
  "document": "Contract_ABC.pdf",
  "path": "/path/to/Contract_ABC.pdf",
  "extractions": [
    {
      "column": "Parties",
      "value": "Acme Corp and Beta Inc",
      "page": 1,
      "quote": "entered into between Acme Corp and Beta Inc"
    },
    {
      "column": "Effective Date",
      "value": "January 15, 2025",
      "page": 1,
      "quote": "effective as of January 15, 2025"
    }
  ]
}

Excel Output Format

Cell with citation:

Value: "Acme Corp and Beta Inc"
Hyperlink: file:///path/to/Contract_ABC.pdf#page=1
Comment: Page 1: "entered into between Acme Corp and Beta Inc"

Color coding (optional):

Green: Value found with high confidence
Yellow: Value found but uncertain
Red: Value not found

Error Handling

| Scenario | Action |

|----------|--------|

| Document unreadable | Log error, mark row as failed, continue |

| Column not found | Set value to "Not found", explain in comment |

| Agent timeout | Collect partial results, note incomplete |

| Missing skill | Prompt user to install required skill |

Example Usage

User: I want to do a tabular review of my contracts

Claude: [Uses AskUserQuestion]
  - What folder contains your documents?
  - What should I name the output Excel file?
  - What columns do you want to extract?

User: ~/Contracts, review.xlsx, Parties/Date/Term/Governing Law

Claude: [Discovers 15 documents via Glob]
Claude: [Launches 5 background agents, 3 docs each]
Claude: [Collects results via TaskOutput]
Claude: [Creates review.xlsx via xlsx skill]

Output: review.xlsx with 15 rows, 4 columns, hyperlinks and citations

How to use it

Copy the folder

Take lawve-ai/tabular-review-lawvable from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.