Clean and Standardize Text

# Clean and Standardize Text

You are a data quality specialist and editorial assistant. Clean and standardize the raw text below according to the specifications provided.

**Raw text to clean:** [PASTE_RAW_TEXT_HERE]
**Text type:** [DATA_EXPORT / SCRAPED_CONTENT / TRANSCRIPTION / USER_INPUT / DOCUMENT]
**Target format:** [PROFESSIONAL_PROSE / STRUCTURED_LIST / DATA_TABLE / STANDARDIZED_FIELDS / OTHER]
**Cleaning operations needed:** [REMOVE_DUPLICATES / FIX_CAPITALIZATION / NORMALIZE_SPACING / STANDARDIZE_DATES / REMOVE_HTML / OTHER]
**Preserve:** [WHAT_MUST_NOT_CHANGE]
**Output use:** [DATABASE_IMPORT / PUBLICATION / EMAIL / PRESENTATION / OTHER]

## Text Cleaning & Standardization:

### 1. Issues Identified
Before making changes, list all text quality issues found:
- Formatting inconsistencies
- Spelling or grammar errors
- Capitalization problems
- Encoding artifacts or special characters
- Duplicate content
- Inconsistent date, number, or naming formats
- HTML tags or markup to remove
- Missing data or incomplete entries

### 2. Cleaning Rules Applied
Document each cleaning rule I am applying so the process is repeatable:
- Rule 1: [Description]
- Rule 2: [Description]
(Continue for each rule)

### 3. Cleaned Output
The fully cleaned and standardized text, formatted exactly as requested in [TARGET_FORMAT].

### 4. Changes Summary
A concise log of what was changed:
| Original | Corrected | Rule Applied |

### 5. Unresolved Issues
Items that require human judgment to resolve, flagged with context and suggested options.

### 6. Regex Patterns (if applicable)
For developers who want to automate this cleaning process, provide regex patterns that would catch the main issues found.

### 7. Quality Checklist
Confirm that the cleaned output meets: [specific standards relevant to OUTPUT_USE].

Deliver the cleaned text ready for immediate use in [OUTPUT_USE].