AI Tuning

AI Tuning Skill

Purpose

Optimize AI assistant configurations for maximum effectiveness, including CLAUDE.md, copilot-instructions.md, and MCP server setup.

Triggers

• "improve my CLAUDE.md"
• "better copilot instructions"
• "tune AI for this project"
• "add MCP servers"
• "optimize AI prompts"

Usage

Provide your existing AI configuration files or describe your project, and this skill will generate optimized CLAUDE.md, copilot-instructions.md, and MCP configurations following best practices.

Effective AI Instructions Principles

1. Be Specific: Vague → vague results
2. Show Examples: Code > descriptions
3. State Constraints: What NOT to do
4. Organize Hierarchically: General → specific
5. Include Commands: Quick reference

CLAUDE.md Structure

# CLAUDE.md

## Project Overview
[What, architecture, technologies]

## Project Structure
```
[directory tree]
```

## Build Commands
```bash
# Install
[command]

# Test
[command]

# Lint
[command]
```

## Code Style Requirements
[Formatter, linter, key rules with examples]

## Architecture Guidelines
[Patterns, layer rules]

## Important Patterns
[Code examples]

copilot-instructions.md Structure

# GitHub Copilot Instructions

## Project Context
[Brief description, tech stack]

## Code Generation Guidelines

### [Language] Patterns
[Conventions, type annotations, imports]

### Examples
```[language]
// GOOD
[example]

// AVOID
[counter-example]
```

## Common Patterns
[Reusable code]

## Commands
[Quick reference]

MCP Configuration

{
  "mcpServers": {
    "context7": {
      "command": "npx",
      "args": ["-y", "@context7/mcp-server"]
    },
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "${workspaceFolder}"]
    },
    "memory": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-memory"]
    }
  }
}

Optimization Techniques

1. Context Density

# Instead of verbose:
"We use Python 3.12 as the language.
We use ruff for linting.
Testing is done with pytest."

# Write dense:
"Python 3.12 | ruff (lint+format) | pytest"

2. Example-Driven

# Instead of:
"Use type annotations."

# Write:
"Type annotations required:
```python
def process(items: list[str]) -> dict[str, int]: ...
```"

3. Constraints Section

## Constraints
- Max line length: 100 chars
- No star imports
- Error messages assigned to variables
- All public functions need docstrings

4. Command Quick Reference

| Action | Command |
|--------|---------|
| Test | `uv run pytest` |
| Lint | `uv run ruff check .` |
| Format | `uv run ruff format .` |

Validation

# Check AI files exist
[ -f "CLAUDE.md" ] && echo "✓ CLAUDE.md"
[ -f ".github/copilot-instructions.md" ] && echo "✓ Copilot"
[ -f ".vscode/mcp.json" ] && echo "✓ MCP"

# Check sections in CLAUDE.md
grep "^## " CLAUDE.md

# Check code examples
grep -c '```' CLAUDE.md

Quality Metrics

Pattern Library Development

Include reusable patterns:

# Pattern: Error Handling
def fetch(id: str) -> User:
    """Fetch user by ID.

    Raises:
        UserNotFoundError: If not found.
    """
    result = db.query(User).filter_by(id=id).first()
    if result is None:
        raise UserNotFoundError(f"User {id} not found")
    return result

AI File Audit

echo "=== AI Configuration Audit ==="

for f in CLAUDE.md .github/copilot-instructions.md .vscode/mcp.json; do
  if [ -f "$f" ]; then
    echo "✓ $f exists ($(wc -l < "$f") lines)"
  else
    echo "✗ $f MISSING"
  fi
done

Source: zircote/.github — distributed by TomeVault.