Source: https://github.com/aipoch/medical-research-skills

When to Use

• You need to merge multiple CSV/Excel files into a single dataset and align columns.
• You need to clean tabular data (normalize headers, deduplicate rows, resolve conflicts) before downstream use.
• You need to perform data analysis/statistics on CSV/Excel (summaries, distributions, group-by metrics).
• You need to add or edit formulas in an Excel workbook (including applying formulas across ranges).
• You need to apply Excel formatting (including conditional formatting), generate charts, or force formula recalculation.

Key Features

• CSV/Excel merge & cleaning: combine files, normalize column names, deduplicate, and resolve conflicts.
• CSV/Excel analysis: compute descriptive statistics and analysis reports.
• Excel-only formula operations: create/edit formulas and apply them to specified ranges.
• Excel-only formatting: apply cell styles and conditional formatting rules.
• Excel-only visualization: build charts from worksheet ranges.
• Excel-only recalculation: set workbook to full recalculation (recalc flag) to ensure formulas update.

Dependencies

• Python 3.x
• Project Python dependencies are defined by the repository environment (e.g., requirements.txt / lockfile if present).
  (No explicit versions were provided in the source document.)

Example Usage

The following commands assume you are in the repository root and have a Python environment available.

1) Merge files (CSV/Excel)

python scripts/merge_files.py

2) Analyze data (CSV/Excel)

python scripts/analyze_data.py

3) Apply formulas (Excel only)

python scripts/apply_formulas.py

4) Apply formatting (Excel only)

python scripts/apply_formatting.py

5) Build charts (Excel only)

python scripts/build_charts.py

6) Force workbook recalculation (Excel only)

python scripts/recalc_workbook.py

Implementation Details

• Workflow

  1. Confirm inputs/outputs: file paths, file formats (CSV vs Excel), worksheet names, and target ranges.
  2. Choose the task type: merge, analysis, formula, formatting, chart, or recalculation.
  3. Run the corresponding script and configure parameters in CONFIG (as used by the scripts).
  4. Produce output files and any generated reports.

• Task boundaries

  • CSV/Excel supported: merging/cleaning, data analysis.
  • Excel only: formula creation/editing, formatting, chart visualization, and recalculation.

• Key parameters to clarify (priority)

  • Input type: CSV or Excel; single file or multiple files.
  • Worksheet names and cell ranges to operate on (Excel).
  • Whether formulas/formatting/charts must preserve original styles.
  • Desired output format: CSV / Excel / JSON / Parquet.

• Standards / constraints

  • Python file I/O must explicitly specify encoding='utf-8'.
  • json.dump(...) must set ensure_ascii=False.

• Reference documentation (optional)

  • Column name matching & normalization: references/column-matching.md
  • Deduplication & conflict resolution: references/dedup-conflict.md
  • Large files & performance: references/large-files.md
  • Formula design & ranges: references/formulas.md
  • Formatting & conditional formatting: references/formatting.md
  • Data analysis & statistics: references/analysis.md
  • Charts & visualization: references/visualization.md
  • Formula recalculation: references/recalc.md

When Not to Use

• Do not proceed when required input files, identifiers, parameters, or context are missing — ask the user to provide them first.
• Do not assume capabilities beyond this skill's declared scope when the user requests external operations or inferences.
• Do not proceed without user confirmation when overwriting existing results, executing high-cost batch operations, or expanding task scope.

Required Inputs

Output Contract

• Primary output: Structured result or target file aligned with this skill's objective.
• Optional output: Intermediate check notes, issue list, supplementary suggestions, or generated file paths.
• Format requirement: Unless the user specifies otherwise, prefer stable, reviewable Markdown or JSON; if the skill's bundled script requires a fixed format, use that format.
• If partially complete: Must explicitly mark as PARTIAL and state which steps are completed and which remain.

Failure Handling

• Missing critical input: Explicitly state which fields, files, or identifiers are missing and pause.
• Script, template, or resource execution failure: Report the failing step, likely cause, and recovery suggestions — do not silently degrade.
• Partial completion only: Return the verified portion first, then list remaining blockers and suggested next steps.

User Checkpoints

• Before executing batch processing, overwriting files, long-running searches, or multi-stage generation, confirm scope and output format with the user.
• Before proceeding when a key judgment is ambiguous, evidence is insufficient, or the workflow is entering the next stage, confirm with the user.

Input Validation

This skill accepts requests that match the documented purpose of spreadsheet-ops and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

spreadsheet-ops only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Quick Validation

• Check that key scripts, templates, or reference file paths this skill depends on exist.
• Check that the final output contains the core fields, sections, or files specified for this task.
• Check that results clearly mark assumptions, limitations, and incomplete items.