Dark Intelligence Workflow Step 1 Identify

Step 1 — Identify (+4)

Step 1 — Identify

Locate the Excel/file containing engineering calculations.

What to look for:

• Formulas (cell formulas, array formulas)
• Named ranges (often contain key parameters)
• VBA macros (may contain iterative solvers or logic)
• Validation/check sheets (comparison against known answers)
• Input sheets with units and descriptions
• README or documentation tabs

Check doc index for the file:

uv run --no-project python -c "
import json
matches = []
with open('data/document-index/index.jsonl') as f:
    for line in f:
        rec = json.loads(line)
        path_lower = rec.get('path', '').lower()
        if '<filename>' in path_lower or '<category>' in path_lower:
            matches.append(rec)
print(f'Found {len(matches)} matching documents')
for m in matches[:20]:
    print(f\"  {m.get('source', '?'):15s} {m.get('path', '')[:80]}\")
"

Output: file path, description of what the spreadsheet calculates, list of tabs/sheets.

Step 2 — Extract

Pull out generic methodology from the file. Extract each of these:

Tips for Excel formula extraction:

• = formulas: translate operators directly to math notation
• Named ranges: map to variable names in the archive
• IF/AND/OR: translate to conditional logic descriptions
• VLOOKUP/INDEX/MATCH: identify the lookup table data
• Array formulas (Ctrl+Shift+Enter): note array dimensions
• VBA Function: extract algorithm as pseudocode

Step 3 — Sanitize (HARD GATE)

This step is non-negotiable. Extraction cannot proceed without passing.

Run the legal sanity scan on all extracted content:

bash scripts/legal/legal-sanity-scan.sh

Check for and remove:

• Client names, project names, project numbers
• Proprietary labels and internal codenames
• Client infrastructure identifiers (field names, platform names)
• Client-specific file paths or network locations
• Employee names (other than yourself for academic work)

If ANY block-severity violations are found: STOP. Remediate all violations before proceeding to Step 4.

Replace all client-specific references with generic equivalents:

• Project names -> generic descriptive names (e.g. "example_platform")
• Field names -> "field_A", "field_B" or generic descriptions
• Client tool names -> generic equivalents

Step 4 — Archive

Save extracted methodology as structured YAML.

Location: knowledge/dark-intelligence/<category>/<subcategory>/

Filename: dark-intelligence-<descriptive-name>.yaml

Schema:

# dark-intelligence-<name>.yaml
source_type: "excel|python|matlab|fortran"
source_description: "Generic description of what this calculates (no client refs)"
extracted_date: "YYYY-MM-DD"
legal_scan_passed: true
category: "<engineering category>"
subcategory: "<specific topic>"

equations:
  - name: "<equation name>"
    latex: "<LaTeX formula>"
    excel_formula: "<original Excel formula, sanitized>"
    standard: "<standard reference if any>"
    description: "<what it computes>"

inputs:
  - name: "<input name>"
    symbol: "<LaTeX symbol>"
    unit: "<unit>"
    typical_range: [min, max]
    test_value: <value for TDD>

outputs:
  - name: "<output name>"
    symbol: "<LaTeX symbol>"
    unit: "<unit>"
    test_expected: <expected value for TDD>
    tolerance: <acceptable error>

worked_examples:
  - description: "<example problem statement>"
    inputs: {key: value}
    outputs: {key: value}
    use_as_test: true

assumptions:
  - "<assumption 1>"
  - "<assumption 2>"

references:
  - "<standard or textbook reference>"

notes: "<any methodology notes, limitations, applicability>"

Validation: ensure legal_scan_passed: true is present and all fields use generic descriptions free of client identifiers.

Step 5 — Generate TDD Test Data

Convert each worked example from the archive into a pytest test function.

Template:

def test_<calc_name>_from_dark_intelligence():
    """Extracted from legacy calculation — verified against original output."""
    # Arrange — inputs from archive
    <input_name> = <test_value>

    # Act — call the implementation
    result = <function>(<inputs>)

    # Assert — expected output from archive
    assert abs(result - <expected>) < <tolerance>, (
        f"Expected {<expected>}, got {result}"
    )

Rules:

• One test per worked example where use_as_test: true
• Use tolerance from the archive for floating-point comparisons
• Include the source description in the docstring
• Tests MUST fail initially (Red phase of TDD)