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Sub-skill of xlsx-to-python: Dark Intelligence Archive Generation.
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/vamseeachanta/workspace-hub --skill xlsx-to-python-dark-intelligence-archive-generation명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
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SKILL.md 표시 중
| name | xlsx-to-python-dark-intelligence-archive-generation |
| description | Sub-skill of xlsx-to-python: Dark Intelligence Archive Generation. |
| version | 1.0.0 |
| category | data |
| type | reference |
| scripts_exempt | true |
Convert extraction results to the canonical archive YAML:
def formula_manifest_to_archive(
formula_cells: list[dict],
named_ranges: list[dict],
classification: dict,
category: str,
subcategory: str,
) -> dict:
"""Convert XLSX extraction to dark-intelligence archive YAML."""
name_map = {
f"{nr['sheet']}!{nr['cell_ref']}": nr["name"]
for nr in named_ranges
}
archive = {
"source_type": "excel",
"source_description": f"{category}/{subcategory} calculation",
"extracted_date": datetime.now().strftime("%Y-%m-%d"),
"legal_scan_passed": False, # Must be set after legal scan
"category": category,
"subcategory": subcategory,
"equations": [],
"inputs": [],
"outputs": [],
"worked_examples": [],
"assumptions": [],
"references": [],
}
# Map input cells
for input_ref in classification["inputs"]:
cell = next(
(c for c in formula_cells
if f"{c['sheet']}!{c['ref']}" == input_ref),
None,
)
name = name_map.get(input_ref, input_ref)
archive["inputs"].append({
"name": name,
"symbol": name,
"unit": "", # Must be filled manually or from column headers
"test_value": cell["value"] if cell else None,
})
# Map formula cells as equations
for cell in formula_cells:
cell_id = f"{cell['sheet']}!{cell['ref']}"
if cell_id in classification["inputs"]:
continue
archive["equations"].append({
"name": name_map.get(cell_id, cell["ref"]),
"excel_formula": cell["formula"],
"latex": "", # TODO: formula-to-LaTeX translation
"description": f"Cell {cell['ref']} in {cell['sheet']}",
})
# Map output cells
for output_ref in classification["outputs"]:
cell = next(
(c for c in formula_cells
if f"{c['sheet']}!{c['ref']}" == output_ref),
None,
)
if cell and cell["value"] is not None:
name = name_map.get(output_ref, output_ref)
archive["outputs"].append({
"name": name,
"symbol": name,
"unit": "",
"test_expected": cell["value"],
"tolerance": 1e-6,
})
return archive