cross-doc-consistency-checker

name	cross-doc-consistency-checker
description	Uses knowledge graphs and adversarial multi-model reviews to enforce absolute numerical and logical consistency across interdependent Markdown and Excel files. Use when revising multi-file project proposals, financial models, or architecture docs.
disable-model-invocation	true

Cross-Document Consistency Checker (Knowledge Graph approach)

Context & Purpose

When dealing with complex project proposals that span multiple files (e.g., Markdown narratives, requirement specs, financial Excel models), a small change in scope (e.g., cutting a budget item) can create cascading contradictions.

This skill defines a "Knowledge Graph (KG) & Script-Driven Adversarial Review" methodology to guarantee 100% numerical and logical consistency across documents.

Methodology: The 4-Step DBTL (Design-Build-Test-Learn) for Docs

1. Design: Establish the "Knowledge Network" (The Single Source of Truth)

Before making ad-hoc edits, define the immutable anchor points:

• CapEx/Budget Constraints: What is the hard total? (e.g., 5,100万)
• Scope Constraints: What modules are in-house? What is outsourced?
• Timeline/Phase: Single-phase vs Multi-phase? (e.g., 18-month one-time build vs Phase 1/2/3).
• Revenue/Utilization: What is the Y1 capacity? When does it ramp?

Document this KG (Knowledge Graph) explicitly in your scratchpad. Every edit must trace back to these roots.

2. Build: Script-Driven Cascading Updates

Never rely on manual LLM find-and-replace for numbers scattered across 100-page documents.

• Write robust Python scripts using re (regex) for Markdown and openpyxl for Excel.
• For Excel, read formulas, recalculate dynamically, and overwrite specific cells without destroying the structure.
• For Markdown, target headers and bullet points accurately.

3. Test: Adversarial Multi-Model Review

Use independent subagents (via /multi-model-review or parallel Task calls) to scrutinize the work.

• Prompt them specifically to look for "ghosts" (e.g., an outsourced item still described as if we bought the equipment, or leftover "Phase II" budgets).
• Ask them to sum the numbers manually and verify they hit the KG constraint (e.g., summing M0 to M11 = 3700).

4. Learn: Root-Cause Cleanup

If the adversarial reviewers find a contradiction:

• Do not just patch the line. Trace the root cause in the Knowledge Network.
• (e.g., If M5 is 1000W but the breakdown sums to 1500W, rewrite the breakdown line explicitly).
• Re-run the python validation script until it returns exit code 0 with NO warnings.

Example Tools to Use

• Python: re.sub(r'old_pattern', 'new_pattern', text)
• OpenPyXL: ws.cell(row=x, col=y, value=new_val)
• Subagents: Launch Task with model: "composer-2.5-fast" and prompt: "Act as an adversarial reviewer... Check for these specific forbidden words..."

Implementation Script

See scripts/verify_kg.py for a template of how to encode the knowledge graph validation into a Python assertion suite.