Der Prompt
# 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
So nutzt du diesen Prompt
Kopiere den Prompt oben oder klicke einen "Öffnen in"-Button um ihn direkt in deiner bevorzugten KI zu starten. Du kannst den Text dann an deinen Anwendungsfall anpassen — z.B. Platzhalter wie [dein Thema] durch echten Kontext ersetzen.
Welches KI-Modell funktioniert am besten
Claude Opus 4 und Sonnet 4.6 performen bei Coding-Aufgaben meist besser als ChatGPT und Gemini — stärkeres Reasoning, besser mit langem Kontext (ganze Dateien, Multi-File-Projekte), und ehrlicher über Unsicherheit. ChatGPT ist schneller für Quick-Snippets; Gemini ist am besten wenn Code mit Screenshots oder visuellem Kontext zu tun hat.
Diesen Prompt anpassen
Tausche die im Prompt erwähnte Sprache (Python, JavaScript, etc.) gegen deinen Stack. Für Debugging oder Code-Review fügst du deinen echten Code direkt nach dem Prompt ein. Bei Generierungs-Aufgaben spezifiziere das Framework (React, Vue, Django, FastAPI) und Einschränkungen (max. Zeilen, keine externen Libraries, muss async sein).
Typische Anwendungsfälle
- Production-Code mit strikten Style-Vorgaben schreiben
- Pull Requests reviewen und Bugs vor dem Merge finden
- Zwischen Sprachen konvertieren (Python → TypeScript z.B.)
- Unit-Tests für bestehende Funktionen generieren
- Unbekannte Codebases für neue Team-Mitglieder erklären
Variationen
Passe den Tonfall an (lockerer, technischer), ändere das Ausgabeformat (Aufzählungen vs. Absätze) oder füge Einschränkungen hinzu (Wortlimits, Zielgruppe).
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