xlsx-to-python-step-6-calculation-block-detection
Sub-skill of xlsx-to-python: Step 6 — Calculation Block Detection.
Best use case
xlsx-to-python-step-6-calculation-block-detection is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of xlsx-to-python: Step 6 — Calculation Block Detection.
Teams using xlsx-to-python-step-6-calculation-block-detection should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/step-6-calculation-block-detection/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How xlsx-to-python-step-6-calculation-block-detection Compares
| Feature / Agent | xlsx-to-python-step-6-calculation-block-detection | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Sub-skill of xlsx-to-python: Step 6 — Calculation Block Detection.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Step 6 — Calculation Block Detection
## Step 6 — Calculation Block Detection
Heuristic to separate calculation regions from data/lookup tables:
```python
def detect_calculation_blocks(ws_formulas, min_formula_ratio=0.3):
"""Identify contiguous regions dominated by formulas."""
blocks = []
current_block = []
for row in ws_formulas.iter_rows():
formula_count = sum(
1 for c in row
if isinstance(c.value, str) and c.value.startswith("=")
)
total = sum(1 for c in row if c.value is not None)
if total > 0 and formula_count / total >= min_formula_ratio:
current_block.append(row[0].row)
else:
if len(current_block) >= 2:
blocks.append((current_block[0], current_block[-1]))
current_block = []
if len(current_block) >= 2:
blocks.append((current_block[0], current_block[-1]))
return blocks
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