January 12, 202610 MIN READ

Claude Code Best Practices: Security, Performance & Teams

By Learnia Team

Claude Code Best Practices: Security, Performance & Teams

This article is written in English. Our training modules are available in French.

Claude Code is powerful—but power requires responsibility. This guide covers battle-tested practices for security, performance, and team collaboration. Whether you're a solo developer or leading an enterprise team, these patterns will help you use Claude Code effectively and safely.

Security Best Practices

1. Never Trust Unreviewed AI Output

Claude is highly capable, but it can make mistakes. Always review before:

→Committing code to production branches
→Running commands with elevated privileges
→Modifying configuration files
→Accessing production databases

Pattern: Review Gates

# Always create PRs, never push directly
git checkout -b feature/ai-generated
claude -p "Implement the feature"
git add -A && git commit -m "AI-generated implementation"
gh pr create --title "Review: AI Implementation"

2. Configure Strict Permissions

Default to minimal permissions:

// .claude/settings.json
{
  "permissions": {
    "mode": "ask",
    "deny": [
      "Bash(rm:-rf*)",
      "Bash(sudo:*)",
      "Bash(chmod:777*)",
      "Bash(*:*prod*)",
      "Bash(*:*production*)",
      "Edit(.env*)",
      "Edit(**/*.pem)",
      "Edit(**/*secret*)",
      "Read(**/*.pem)",
      "Read(.env*)"
    ],
    "allow": [
      "Read(src/**)",
      "Read(tests/**)",
      "Edit(src/**)",
      "Edit(tests/**)",
      "Bash(npm:test)",
      "Bash(npm:lint)",
      "Bash(git:status)",
      "Bash(git:diff*)"
    ]
  }
}

See Claude Code Permissions: Deny, Allow & Ask Modes Explained.

3. Protect Secrets

Never expose credentials to Claude:

<!-- CLAUDE.md -->
## Security Rules

NEVER:
- Read or display .env files
- Access files in secrets/ directory
- Include API keys, tokens, or passwords in output
- Run commands that expose environment variables

Environment Isolation:

# Create a Claude-specific env without secrets
cat .env | grep -v "API_KEY\|SECRET\|PASSWORD" > .env.claude
export $(cat .env.claude | xargs)
claude

4. Use Sandbox Mode for Untrusted Operations

For autonomous or experimental work:

claude --sandbox

Sandbox mode:

→Restricts filesystem to project directory
→Blocks network access (except localhost)
→Prevents system command execution
→Isolates from host environment

5. Audit Trail

Log all Claude actions:

// .claude/settings.json
{
  "hooks": {
    "PostToolUse": [{
      "matcher": "*",
      "command": "python .claude/hooks/audit.py"
    }]
  }
}

# .claude/hooks/audit.py
import json
import sys
from datetime import datetime

data = json.loads(sys.stdin.read())

log_entry = {
    "timestamp": datetime.now().isoformat(),
    "tool": data.get("tool_name"),
    "input": data.get("tool_input"),
    "user": os.environ.get("USER")
}

with open(".claude/audit.log", "a") as f:
    f.write(json.dumps(log_entry) + "\n")

print(json.dumps({"status": "logged"}))

6. Version Control Everything

Track all Claude-related configuration:

git add .claude/
git add CLAUDE.md
git commit -m "Add Claude Code configuration"

This ensures:

→Consistent settings across team
→Audit trail for config changes
→Easy rollback if needed

Performance Optimization

1. Manage Context Efficiently

Claude's context window is limited. Optimize usage:

Use

/compact

proactively:

> /compact focus on current feature work

Add only relevant directories:

> /add-dir src/auth  # Good: specific
> /add-dir src       # Bad: too broad

Use CLAUDE.md strategically:

<!-- CLAUDE.md - Keep under 1000 words -->
## Project Overview
Brief description only.

## Key Conventions
Only essential patterns.

## Current Focus
What we're working on now.

2. Choose the Right Model

Scenario	Model	Why
Quick questions	`haiku`	Fast, cheap
Code generation	`sonnet`	Balanced
Complex refactoring	`opus`	Most capable
Large codebase analysis	`sonnet` + sub-agents	Parallel processing

> /model haiku
> Quick question: what's the syntax for...?

> /model opus
> Refactor the entire authentication system

3. Leverage Sub-Agents for Large Tasks

Don't overload a single context:

> Use sub-agents to analyze each module:
  - Agent 1: src/auth
  - Agent 2: src/api
  - Agent 3: src/components
  Synthesize findings after.

See Claude Code Sub-Agents: Orchestrating Complex Tasks.

4. Cache Expensive Operations

For automation scripts:

import { createHash } from "crypto";
import { readFileSync, writeFileSync, existsSync } from "fs";

function getCached(prompt: string) {
  const hash = createHash("md5").update(prompt).digest("hex");
  const cachePath = `.claude/cache/${hash}.json`;
  
  if (existsSync(cachePath)) {
    return JSON.parse(readFileSync(cachePath, "utf-8"));
  }
  
  return null;
}

function setCache(prompt: string, result: any) {
  const hash = createHash("md5").update(prompt).digest("hex");
  writeFileSync(`.claude/cache/${hash}.json`, JSON.stringify(result));
}

5. Batch Similar Operations

Instead of:

> Add tests to file1.ts
> Add tests to file2.ts
> Add tests to file3.ts

Do:

> Add tests to file1.ts, file2.ts, and file3.ts

Or use parallel processing:

> Use sub-agents to add tests to each file in src/services/

6. Use Skills for Repeated Tasks

Save and reuse patterns:

> Save this deployment process as a skill

Saved skill: deploy-staging
Next time just say: "deploy to staging"

See Agent Skills in Claude Code: Extend Claude's Capabilities.

Team Collaboration

1. Standardize CLAUDE.md

Create a template for all projects:

<!-- .claude/templates/CLAUDE.md -->
# Project: [NAME]

## Overview
[Brief description]

## Tech Stack
- Framework: 
- Language:
- Database:

## Conventions
### File Structure
[Describe structure]

### Naming
- Components: PascalCase
- Functions: camelCase
- Files: kebab-case

### Code Style
[Link to style guide]

## Current Sprint
[Active work focus]

## Off-Limits
[Things Claude should never do]

2. Share Custom Commands

Create team-wide commands:

<!-- .claude/commands/team/code-review.md -->
---
description: Team standard code review
---

Review this code following our team standards in CONTRIBUTING.md.

Check for:
1. Style guide compliance
2. Test coverage requirements
3. Documentation requirements
4. Security checklist items

Format output as a PR review comment.

Commit to repo:

git add .claude/commands/
git commit -m "Add team Claude commands"

3. Define Team Permissions

Project-level permission baseline:

// .claude/settings.json (committed)
{
  "permissions": {
    "deny": [
      "Bash(npm:publish*)",
      "Bash(git:push --force*)",
      "Bash(git:push -f*)",
      "Edit(.env*)",
      "Edit(**/production/**)"
    ]
  }
}

Individual overrides in user settings only.

4. Document AI Contributions

Use consistent commit messages:

git commit -m "feat: add user profile [AI-assisted]"
git commit -m "fix: resolve auth bug [AI-generated]"

Or use Co-authored-by:

git commit -m "feat: new feature

Co-authored-by: Claude <claude@anthropic.com>"

5. Code Review AI Output

Establish review requirements:

# .github/CODEOWNERS
# AI-generated code requires senior review
*.ai-generated.* @senior-devs

Or use labels:

# .github/workflows/ai-review.yml
- name: Flag AI-generated PRs
  if: contains(github.event.pull_request.body, 'AI-generated')
  run: gh pr edit --add-label "needs-senior-review"

6. Maintain Skill Library

Central repository of team skills:

development/

→
```
create-component.yaml
```
→
```
create-endpoint.yaml
```
→
```
create-test.yaml
```

devops/

→
```
deploy-staging.yaml
```
→
```
deploy-production.yaml
```

review/

→
```
code-review.yaml
```
→
```
security-review.yaml
```

Install in projects:

git submodule add git@github.com:org/team-claude-skills.git .claude/team-skills

Production Deployment

1. CI/CD Integration

Safe automation pattern:

# .github/workflows/claude-review.yml
name: AI Review

on:
  pull_request:
    types: [opened, synchronize]

jobs:
  review:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      pull-requests: write
    
    steps:
      - uses: actions/checkout@v4
      
      - name: AI Code Review
        run: |
          claude --print --dangerously-skip-permissions \
            -p "Review changes for issues" > review.md
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
      
      - name: Post Review
        run: gh pr comment --body-file review.md

See Claude Code GitHub Actions: AI-Powered CI/CD Automation.

2. Rate Limiting

Prevent runaway costs:

import Bottleneck from "bottleneck";

const limiter = new Bottleneck({
  reservoir: 1000,           // Daily limit
  reservoirRefreshAmount: 1000,
  reservoirRefreshInterval: 24 * 60 * 60 * 1000,
  maxConcurrent: 5
});

export async function claudeRequest(prompt: string) {
  return limiter.schedule(() => callClaudeAPI(prompt));
}

3. Cost Monitoring

Track usage:

// Track every API call
async function trackedCall(prompt: string) {
  const start = Date.now();
  const response = await client.messages.create({...});
  
  await metrics.record({
    inputTokens: response.usage.input_tokens,
    outputTokens: response.usage.output_tokens,
    duration: Date.now() - start,
    model: response.model,
    cost: calculateCost(response.usage)
  });
  
  return response;
}

4. Fallback Strategies

Handle API failures gracefully:

async function resilientCall(prompt: string) {
  try {
    return await callClaude(prompt, "opus");
  } catch (error) {
    if (error.status === 529) { // Overloaded
      console.log("Falling back to sonnet");
      return await callClaude(prompt, "sonnet");
    }
    throw error;
  }
}

5. Monitoring & Alerting

// Monitor key metrics
const metrics = {
  apiErrors: new Counter("claude_api_errors"),
  latency: new Histogram("claude_latency_ms"),
  tokenUsage: new Counter("claude_tokens"),
  costAccumulated: new Gauge("claude_cost_usd")
};

// Alert on anomalies
if (metrics.apiErrors.rate(1h) > 10) {
  alert("High Claude API error rate");
}

if (metrics.costAccumulated.value > DAILY_BUDGET) {
  alert("Claude daily budget exceeded");
  disableAutoReviews();
}

Prompt Engineering Tips

1. Be Specific

❌ "Fix the bug"
✅ "Fix the null pointer exception in src/auth/login.ts:45 
    where user.email can be undefined after failed OAuth"

2. Provide Context

❌ "Add validation"
✅ "Add Zod validation to the user registration endpoint.
    We use Zod for all input validation in this project.
    See src/api/products/route.ts for an example pattern."

3. Specify Output Format

> Return the analysis as JSON:
  {
    "issues": [{"file": "string", "line": number, "severity": "high|medium|low", "message": "string"}],
    "summary": "string"
  }

4. Use Constraints

> Refactor this function with these constraints:
  - Keep the public API unchanged
  - Don't add new dependencies
  - Maintain O(n) time complexity
  - Keep the implementation under 50 lines

5. Iterate

Start broad, then refine:

> Analyze this codebase for performance issues
[Review output]
> Focus on the database queries you identified. 
  Show me the specific N+1 query problems.
[Review output]
> Generate fixes for the top 3 issues

Common Pitfalls to Avoid

1. Over-Automation

Don't: Automate everything without oversight Do: Keep humans in the loop for important decisions

2. Blind Trust

Don't: Accept all AI suggestions without review Do: Treat AI output as first draft that needs review

3. Context Overload

Don't: Add entire codebase to context Do: Focus on relevant files and directories

4. Vague Prompts

Don't: "Make it better" Do: "Improve performance by reducing API calls in the render cycle"

5. Ignoring Errors

Don't: Retry failed operations blindly Do: Analyze failures and adjust approach

6. Skipping Testing

Don't: Deploy AI-generated code without tests Do: Always verify with automated and manual testing

Checklist: Production Readiness

Before using Claude Code in production:

Key Takeaways

→
Security first: Configure strict permissions and protect secrets.
→
Optimize context: Be selective about what Claude sees.
→
Standardize for teams: Share configurations, commands, and skills.
→
Review everything: AI output is a first draft, not final code.
→
Monitor in production: Track usage, costs, and errors.

Master Responsible AI Development

These best practices are part of a broader approach to responsible AI use. Learn the full framework in our ethics module.

In our Module 8 — Safety & Ethics, you'll learn:

→Comprehensive AI safety frameworks
→Ethical considerations in AI development
→Risk assessment and mitigation
→Building trustworthy AI systems

→ Explore Module 8: Safety & Ethics

GO DEEPER

Module 8 — Ethics, Security & Compliance

Navigate AI risks, prompt injection, and responsible usage.

Explorer le Module