TL;DR

  • I had a critical gap in my Claude Code workflow. Everything was optimized except the moment Claude actually writes code. Even with auto-accept enabled, I was gambling that /complete-task would catch violations after many changes.

  • Hooks let you inject real-time quality checks. When Claude Code hooks became available, I could finally add guardrails that run outside Claude’s context but feed results back through JSON decision control to block bad edits.

  • Generic linters don’t know your specific standards. Most people use existing tools, but they don’t enforce your preferences like uv over pip, httpx over requests, or your particular development philosophy.

  • claudex-guard enforces my opinionated standards. Built on top of good tools like ruff and mypy, it adds my specific development approach with real-time blocking when violations are found.

  • The bigger win is workflow simplification. My CLAUDE.local.md became much lighter since detailed enforcement moved to hooks. My /complete-task can focus on bigger picture validation instead of catching basic violations.

  • Working with bleeding edge is tough but valuable. PreToolUse hooks don’t work for context injection like I planned - they’re for approval decisions, not content. But PostToolUse enforcement works great and I can finally enable auto-accept safely.

The Hope and Pray Gap

After months of refining my Claude Code + Desktop workflow, I had context loading optimized, quality commands structured, and systematic validation in place. Everything worked well except for one critical moment: when Claude actually writes code.

Even with all my workflow optimization, there was still this hope and pray gap. I could enable auto-accept edits for maximum speed, but then I was gambling that my /complete-task validation would catch whatever questionable decisions the LLM made after many code changes. The alternative was manually reviewing every edit, which killed the velocity gains from Claude assistance.

The workflow was optimized everywhere except the actual coding moment—the place where things can go sideways fastest.

Hooks: Real-Time Quality Checks

When Claude Code hooks became available, I saw the opportunity to finally close this workflow gap. Hooks let you inject any kind of check—lightweight pattern matching or heavy analysis—that runs outside Claude’s context but feeds results back through JSON responses that can block edits when needed.

The hook system seemed promising: quality enforcement that doesn’t break flow, running in parallel to keep Claude’s context clean, with results that can prevent bad edits from getting through.

The Checker Problem

But hooks are only as good as your checker.

Most people are probably reaching for existing linters or corporate quality tools. ESLint for JavaScript, pylint for Python, maybe some security scanners. These tools are valuable—they catch syntax errors and common anti-patterns that save real debugging time. But they don’t know your specific development philosophy.

Your linter doesn’t know that you prefer httpx over requests, want uv instead of pip, or care about specific project structure patterns. It definitely doesn’t know your workflow conventions or the particular gotchas you’ve learned to avoid in your specific development context.

Corporate tools tend to enforce generic standards rather than your specific development approach, but they serve their purpose in larger organizations.

Building My Own Checker

So I built my own: claudex-guard.

The idea was combining three elements that already worked well independently:

claudex philosophy: The opinionated, language-specific development standards generated through systematic research of authoritative sources

LLM pattern recognition: Using AI to translate those standards into sophisticated code analysis—AST traversal, pattern detection, context-aware violation identification

Real tooling integration: Leveraging existing tools like ruff and mypy for the mechanical fixes while adding the philosophical layer on top

How It Works

claudex-guard operates as a PostToolUse hook that runs after Claude writes code but before you see the results. It combines AST analysis with pattern matching to catch violations that matter to your development approach.

Example violations it catches:

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# Mutable default arguments (classic Python gotcha)
def process_items(items=[]):  # Violation: shared state between calls
    return items

# Banned legacy libraries
import requests  # Violation: use httpx instead (async-first, HTTP/2)

# Old string formatting patterns
message = "Hello %s!" % name  # Violation: use f-strings for readability

What happens when violations are found:

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{
  "decision": "block",
  "reason": "Quality violations found (2 errors):\n• example.py:1 - Mutable default argument in function 'process_items'\n  Fix: Use None default, check inside function\n• example.py:4 - Use httpx instead of requests (async-first, HTTP/2 support)"
}

The violations block the edit through JSON decision control, Claude sees the specific file locations and fix suggestions, and can address the issues immediately.

The Workflow Simplification

With claudex-guard running, I can finally enable auto-accept edits and maintain velocity without sacrificing quality. But the bigger win is how this simplifies the entire workflow.

Before claudex-guard: My CLAUDE.local.md file had detailed Python rules and reminders that Claude would often forget or ignore due to context limitations. My /complete-task command spent time catching basic violations that should have been prevented.

After claudex-guard: CLAUDE.local.md focuses on high-level workflow philosophy and critical environment setup. Other workflow commands can focus on bigger picture concerns instead of granular enforcement.

The bash enforcer insight: Reading through my research, I realized we need separate enforcers for different contexts. It’s not appropriate for a Python guard to enforce uv install instead of pip - that’s shell command context, not Python code context. Hence the planned bash-guard for shell scripts and command enforcement.

The hook handles the detailed checking in the right context, so everything else in my workflow can be lighter weight and more focused on what actually requires human attention.

More Than Generic Linting

What makes this approach different is adding your specific preferences on top of solid existing tools. claudex-guard builds on tools like ruff and mypy, but it enforces your particular development approach rather than generic standards.

The bigger benefit: this enforcement lets me strip down my other workflow components. My CLAUDE.local.md file went from thousands of lines of detailed Python rules to just high-level workflow philosophy. My /complete-task command can focus on bigger picture validation instead of catching syntax details. The hook handles the granular enforcement, so everything else can be lighter weight.

Early Results

I’m still validating the approach, but early signs seem positive:

  • Fewer /complete-task failures due to quality issues
  • More consistent adherence to development patterns
  • Reduced cognitive overhead from manual code review
  • Maintained development velocity with improved confidence

The goal was eliminating that vulnerable moment in my workflow, and the lightweight real-time enforcement appears to be working.

The Reality: Bleeding Edge Challenges

The current system enforces existing standards through PostToolUse hooks, which work great for real-time quality control. But I originally planned a learning loop that would inject violation patterns before Claude starts coding.

The challenge: PreToolUse hooks aren’t designed for context injection - they’re for approval/blocking decisions. Claude doesn’t see the hook output, so automatic context injection isn’t possible with current Claude Code hook architecture.

What works now: claudex-guard does create violation memory files (logs for analysis, memory files for manual context), but the automatic learning loop has to wait for hook evolution.

Being on the bleeding edge: Working with Claude Code hooks means dealing with constantly changing capabilities. It’s tough, but these kinds of guardrails will let us increase velocity significantly as the hook system matures.

These would transform claudex-guard from a quality checker into an adaptive development assistant that actually learns your patterns.

Extending the Approach

The architecture is designed to support multiple languages. The planned router system will handle JavaScript/TypeScript with ESLint integration, Rust with clippy, Go with standard toolchain. The pattern is consistent: take your opinionated standards, translate them into code analysis, integrate with existing tools.

The key insight is that hooks enable personalized guardrails rather than generic quality checks. You’re not limited to what existing tools think you should care about—you can enforce what you actually care about based on your evolved development approach.

Conclusion

The hook ecosystem is only as good as the checkers you build for it. Good linters like ruff and eslint catch important problems, but they miss the philosophical layer that makes code your code rather than just correct code.

claudex-guard is my experiment in building personalized guardrails through Claude Code hooks. Early results suggest the approach works: real-time enforcement that understands your standards, integrated with tools that handle the mechanical fixes, running outside Claude’s context to keep things fast.

Your Claude Code workflow probably has similar gaps—places where you’re hoping the LLM makes good choices rather than enforcing the choices you’ve already made. Hooks provide the infrastructure to close those gaps; you just need checkers that actually know your standards.

The law is in your own hands now. What guardrails will you build?

References

  1. claudex-guard - Automated code quality enforcement for AI-assisted development workflows

  2. Claude Code Hooks Documentation - Official documentation for the hook system that enables this approach

  3. Beyond Prompting: Why LLMs Break Down on Well-Architected Code - Previous exploration of cognitive architecture in AI development

  4. Claude Code Memory Optimization - Earlier work on context management and workflow efficiency