Quickly launch Claude with custom personas and models for coding tasks. Ideal for developers and operations teams needing fast, interactive model selection.
git clone https://github.com/NTCoding/claude-skillz.gitClaude Skillz provides an interactive launcher for Claude Code that lets you instantly select from 12 pre-built personas—including Super TDD Developer, Claude Code Optimizer, Strategic Architect, and React Developer—paired with your choice of Claude model (Opus, Sonnet, Haiku). Each persona is composed of reusable skills covering TDD workflows, code design principles, debugging strategies, and documentation. The launcher works via simple command shortcuts (e.g., `cl tdd opus`) and detects conflicts with prominent warnings. It also supports OpenCode agent generation, global marketplace installation, and custom persona creation, making it ideal for development teams that need consistent, role-based AI assistance without manual prompt engineering.
[{"step":1,"action":"Open your terminal or Claude desktop app and type the exact prompt template, replacing [PLACEHOLDERS] with your specific needs. For example: 'Launch Claude with a DevOps persona using the Claude 3.5 Haiku model. Set the context to: optimizing Kubernetes resource allocation for a production cluster.'","tip":"Use short, specific context descriptions (e.g., 'debugging a memory leak in a Go microservice') rather than vague ones to get more relevant outputs."},{"step":2,"action":"Specify your constraints or requirements when prompted by Claude. Include technical details like programming language, framework versions, or performance targets (e.g., 'Must support Python 3.11 and Django 5.0').","tip":"The more specific your constraints, the more targeted the AI's responses will be. Mention things like 'legacy system integration' or 'strict GDPR compliance' if relevant."},{"step":3,"action":"Iterate with follow-up prompts to refine the output. For coding tasks, ask for alternative approaches, performance comparisons, or security reviews.","tip":"Use phrases like 'Show me 3 different implementations' or 'Compare the memory usage of these two solutions' to get comparative analysis."},{"step":4,"action":"Copy the generated code snippets directly into your project. Use Claude's built-in diff tool (if available) to compare changes before committing.","tip":"For critical systems, always review the generated code manually and test it in a staging environment before deploying to production."},{"step":5,"action":"Save your most effective persona/model combinations as presets in your Claude settings or as shell aliases for faster future access.","tip":"Create aliases like 'claudecode' for coding tasks or 'claudeops' for DevOps scenarios to save time on repetitive setups."}]
Launch Claude with TDD persona for test-driven development workflows
Switch between specialized personas (optimizer, architect, investigator) mid-session
Generate OpenCode agents using the same persona system prompts
Compose custom personas by combining pre-built skills and frontmatter shortcuts
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/NTCoding/claude-skillzCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Launch Claude with a [PERSONA] focused on [TASK_TYPE] using the [MODEL_NAME] model. Set the context to: '[CONTEXT_DESCRIPTION]'. Begin by asking me for my preferred [LANGUAGE/FRAMEWORK] and any specific [CONSTRAINTS] (e.g., performance, security).
```
Claude is now active with the following configuration:
- Persona: Senior Python Backend Developer
- Model: Claude 3.5 Sonnet
- Context: Optimizing a high-traffic REST API for a fintech startup
How can I assist you today? For example:
- 'I need to refactor this Flask endpoint for better scalability.'
- 'Can you suggest a caching strategy for this endpoint handling 10k RPM?'
- 'Review this code for SQL injection vulnerabilities.'
Current focus: Performance optimization under strict latency requirements (<50ms P99).
```
**Example Interaction:**
User: "I need to refactor this Flask endpoint for better scalability."
AI Response:
```python
# Refactored Flask endpoint with async support and connection pooling
from flask import jsonify
from flask_async import async_route
import asyncpg
from app.db import get_pool
@async_route('/api/transactions/<user_id>')
async def get_transactions(user_id):
pool = await get_pool()
async with pool.acquire() as conn:
transactions = await conn.fetch(
"""
SELECT id, amount, timestamp
FROM transactions
WHERE user_id = $1
ORDER BY timestamp DESC
LIMIT 100
""", user_id
)
return jsonify([dict(tx) for tx in transactions])
```
**Key Improvements:**
1. Async/await for non-blocking I/O
2. Connection pooling to reduce overhead
3. Limited result set to 100 entries
4. Added proper error handling (omitted for brevity)
Would you like me to:
1. Add rate limiting?
2. Implement Redis caching for frequent users?
3. Review the SQL query for optimization opportunities?
```AI assistant built for thoughtful, nuanced conversation
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