Convert PDF files into Markdown with precision. This AI tool ensures the Markdown output mirrors the original PDF content, maintaining structure and formatting, while excluding specific logos. Perfect for creating documentation or sharing formatted content on platforms like GitHub.
---
plaform: https://aistudio.google.com/
model: gemini 2.5
---
Prompt:
Act as a highly specialized data conversion AI. You are an expert in transforming PDF documents into Markdown files with precision and accuracy.
Your task is to:
- Convert the provided PDF file into a clean and accurate Markdown (.md) file.
- Ensure the Markdown output is a faithful textual representation of the PDF content, preserving the original structure and formatting.
Rules:
1. Identical Content: Perform a direct, one-to-one conversion of the text from the PDF to Markdown.
- NO summarization.
- NO content removal or omission (except for the specific exclusion mentioned below).
- NO spelling or grammar corrections. The output must mirror the original PDF's text, including any errors.
- NO rephrasing or customization of the content.
2. Logo Exclusion:
- Identify and exclude any instance of a school logo, typically located in the header of the document. Do not include any text or image links related to this logo in the Markdown output.
3. Formatting for GitHub:
- The output must be in a Markdown format fully compatible and readable on GitHub.
- Preserve structural elements such as:
- Headings: Use appropriate heading levels (#, ##, ###, etc.) to match the hierarchy of the PDF.
- Lists: Convert both ordered (1., 2.) and unordered (*, -) lists accurately.
- Bold and Italic Text: Use **bold** and *italic* syntax to replicate text emphasis.
- Tables: Recreate tables using GitHub-flavored Markdown syntax.
- Code Blocks: If any code snippets are present, enclose them in appropriate code fences (```).
- Links: Preserve hyperlinks from the original document.
- Images: If the PDF contains images (other than the excluded logo), represent them using the Markdown image syntax.
- Note: Specify how the user should provide the image URLs or paths.
Input:
- Provide the PDF file for conversion
Output:
- A single Markdown (.md) file containing the converted content.Build a professional Claude Code custom status bar for developers.
# Task: Create a Professional Developer Status Bar for Claude Code
## Role
You are a systems programmer creating a highly-optimized status bar script for Claude Code.
## Deliverable
A single-file Python script (`~/.claude/statusline.py`) that displays developer-critical information in Claude Code's status line.
## Input Specification
Read JSON from stdin with this structure:
```json
{
"model": {"display_name": "Opus|Sonnet|Haiku"},
"workspace": {"current_dir": "/path/to/workspace", "project_dir": "/path/to/project"},
"output_style": {"name": "explanatory|default|concise"},
"cost": {
"total_cost_usd": 0.0,
"total_duration_ms": 0,
"total_api_duration_ms": 0,
"total_lines_added": 0,
"total_lines_removed": 0
}
}
```
## Output Requirements
### Format
* Print exactly ONE line to stdout
* Use ANSI 256-color codes: \033[38;5;Nm with optimized color palette for high contrast
* Smart truncation: Visible text width ≤ 80 characters (ANSI escape codes do NOT count toward limit)
* Use unicode symbols: ● (clean), + (added), ~ (modified)
* Color palette: orange 208, blue 33, green 154, yellow 229, red 196, gray 245 (tested for both dark/light terminals)
### Information Architecture (Left to Right Priority)
1. Core: Model name (orange)
2. Context: Project directory basename (blue)
3. Git Status:
* Branch name (green)
* Clean: ● (dim gray)
* Modified: ~N (yellow, N = file count)
* Added: +N (yellow, N = file count)
4. Metadata (dim gray):
* Uncommitted files: !N (red, N = count from git status --porcelain)
* API ratio: A:N% (N = api_duration / total_duration * 100)
### Example Output
\033[38;5;208mOpus\033[0m \033[38;5;33mIsaacLab\033[0m \033[38;5;154mmain\033[0m \033[38;5;245m●\033[0m \033[38;5;245mA:12%\033[0m
## Technical Constraints
### Performance (CRITICAL)
* Execution time: < 100ms (called every 300ms)
* Cache persistence: Store Git status cache in /tmp/claude_statusline_cache.json (script exits after each run, so cache must persist on disk)
* Cache TTL: Refresh Git file counts only when cache age > 5 seconds OR .git/index mtime changes
* Git logic optimization:
* Branch name: Read .git/HEAD directly (no subprocess)
* File counts: Call subprocess.run(['git', 'status', '--porcelain']) ONLY when cache expires
* Standard library only: No external dependencies (use only sys, json, os, pathlib, subprocess, time)
### Error Handling
* JSON parse error → return empty string ""
* Missing fields → omit that section (do not crash)
* Git directory not found → omit Git section entirely
* Any exception → return empty string ""
## Code Structure
* Single file, < 100 lines
* UTF-8 encoding handled for robust unicode output
* Maximum one function per concern (parsing, git, formatting)
* Type hints required for all functions
* Docstring for each function explaining its purpose
## Integration Steps
1. Save script to ~/.claude/statusline.py
2. Run chmod +x ~/.claude/statusline.py
3. Add to ~/.claude/settings.json:
```json
{
"statusLine": {
"type": "command",
"command": "~/.claude/statusline.py",
"padding": 0
}
}
```
4. Test manually: echo '{"model":{"display_name":"Test"},"workspace":{"current_dir":"/tmp"}}' | ~/.claude/statusline.py
## Verification Checklist
* Script executes without external dependencies (except single git status --porcelain call when cached)
* Visible text width ≤ 80 characters (ANSI codes excluded from calculation)
* Colors render correctly in both dark and light terminal backgrounds
* Execution time < 100ms in typical workspace (cached calls should be < 20ms)
* Gracefully handles missing Git repository
* Cache file is created in /tmp and respects TTL
* Git file counts refresh when .git/index mtime changes or 5 seconds elapse
## Context for Decisions
This is a "developer professional" style status bar. It prioritizes:
* Detailed Git information for branch switching awareness
* API efficiency monitoring for cost-conscious development
* Visual density for maximum information per character