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Skills as Guardrails: How We Use Claude Code Skills to Catch Bugs Before They Ship

• by Alien Brain Trust • AI Learning
Skills as Guardrails: How We Use Claude Code Skills to Catch Bugs Before They Ship

Skills as Guardrails: How We Use Claude Code Skills to Catch Bugs Before They Ship

Meta Description: We built Claude Code skills that catch insecure code, prevent bugs, and enforce patterns—turning every mistake into a permanent guardrail for future work.

Working with AI code generation is powerful but risky. Claude can write hundreds of lines of production code in seconds. But it can also expose API keys, break platform-specific features, or repeat the same mistake we fixed yesterday. We needed guardrails.

Enter Claude Code skills: custom automation patterns that act as guardrails, catching common errors before they ship. Here’s what we’ve built, why we built them, and how they’ve prevented bugs from reaching production.

The Pattern: Make Mistake → Learn → Create Skill → Never Repeat

Every skill we’ve created follows this pattern:

  1. Make a mistake (exposed API key, broken path, failed test)
  2. Fix it manually (understand the root cause)
  3. Create a skill (automate the fix + prevention)
  4. Never repeat the mistake (skill enforces the pattern)

Result: Each mistake becomes a permanent improvement to the development process.

Skill 1: Blog Post Writer (Preventing Publication Errors)

The Mistake:

  • Manual blog post creation was error-prone
  • Inconsistent frontmatter (missing tags, wrong dates)
  • Posts published before review
  • Broken preview links (wrong URL format)

The Skill: blog-post

What It Guards Against:

  1. Date Collisions

    • Automatically finds next available date by scanning existing posts
    • Prevents overwriting existing content
    • Ensures chronological ordering

  2. Incomplete Frontmatter

    • Enforces all required fields (title, description, date, category, tags, author)
    • Defaults published: true for manual review
    • Consistent featured_image path

  3. Incorrect Preview URLs

    • Generates correct localhost URL: http://localhost:4321/ai-learning/YYYY-MM-DD-slug
    • Checks if dev server is running on port 4321
    • Auto-starts server if needed

  4. Brand Voice Violations

    • Enforces ABT voice (concise, authentic, implementation-focused)
    • Security considerations included where relevant
    • Data-driven content (no fluff)

Example Prevention:

# Before (manual creation):
---
title: "My Blog Post"
date: 2026-01-05  ← Already exists! Collision!
published: true   ← Published before review!
tags: []          ← Missing tags
---

# After (skill-enforced):
---
title: "Clear, Specific Title - Lead with Result"
date: 2026-01-07  ← Auto-calculated next date
published: true  ← Safe default
tags: ["automation", "technical", "building-in-public"]  ← Required tags
category: "ai-learning"  ← Enforced category
author: "Alien Brain Trust"  ← Consistent authorship
featured_image: "/blog-placeholder.svg"  ← Correct path
---

Time Saved: 5-10 minutes per post (date checking, frontmatter validation, URL formatting)

Bugs Prevented: 0 date collisions, 0 accidental early publications, 0 broken preview links

Skill 2: Update Linear Issue (Preventing Context Loss)

The Mistake:

  • Manual Linear updates took 2+ minutes per ticket
  • Context switches broke flow state
  • Forgot to update tickets during work
  • Lost track of what was done in long sessions

The Skill: update-linear-issue

What It Guards Against:

  1. Context Loss Across Sessions

    • Every status update documented in Linear
    • Comments capture what was done, not just final state
    • Future sessions can see full history

  2. Inconsistent Ticket Updates

    • Standardized format for status changes
    • Proper GitHub repository links (not broken markdown paths)
    • Automatic linking via Git commit keywords

  3. Environment Variable Failures (Windows)

    • PowerShell wrapper handles Windows User environment
    • Script directory resolution (no path errors)
    • Works from any working directory

Example Prevention:

# Before (manual):
# 1. Stop coding
# 2. Open Linear in browser
# 3. Find SAPB-20 (search, scroll, remember ID)
# 4. Click Edit
# 5. Update status to "In Progress"
# 6. Add comment: "Working on API key guide"
# 7. Save
# 8. Return to code (wait, what was I doing?)
# Time: ~2 minutes + context switch

# After (skill):
powershell -File update-issue.ps1 SAPB-20 -Status "In Progress" -Comment "✅ Completed Windows DPAPI implementation"
# Time: 5 seconds, no context switch

Bugs Prevented:

  • 0 broken markdown links in Linear (all GitHub URLs)
  • 0 “wait, did I update that ticket?” moments
  • 0 lost context across sessions

Time Saved: 10-20 minutes per session (5-10 updates × 2 min each)

Skill 3: Fix Test Scripts (Preventing Path Errors)

The Mistake:

  • Test scripts used relative paths
  • Failed when run from different working directories
  • Hard to debug (“works on my machine”)
  • Wasted time re-running failed tests

The Skill: fix-test-scripts

What It Guards Against:

  1. Relative Path Failures

    • Enforces __dirname for all file operations
    • Validates paths exist before operations
    • Full paths in error messages (easy debugging)

  2. Missing Error Handling

    • Validates required files exist
    • Checks CSV structure before processing
    • Clear error messages with fix instructions

  3. Platform-Specific Failures

    • Handles Windows vs. Unix path separators
    • Tests work from any working directory
    • PowerShell and Bash compatible

Example Prevention:

// Before (broken):
const testCases = fs.readFileSync('test-cases.csv', 'utf8');
// ❌ Fails if run from parent directory
// ❌ Fails if run from different drive
// ❌ Error: "ENOENT: no such file or directory"

// After (skill-enforced):
const testCasesPath = path.join(__dirname, 'test-cases.csv');
if (!fs.existsSync(testCasesPath)) {
  console.error(`❌ Test cases file not found: ${testCasesPath}`);
  console.error(`   Expected location: ${__dirname}`);
  process.exit(1);
}
const testCases = fs.readFileSync(testCasesPath, 'utf8');
// ✅ Works from any directory
// ✅ Clear error with full path
// ✅ Validates before reading

Bugs Prevented:

  • 0 path resolution errors in test scripts
  • 0 “file not found” failures
  • 0 silent failures (all errors caught early)

Time Saved: 15-30 minutes per test session (fewer failed runs)

The Skills We’ve Built (and Why)

1. blog-post

Purpose: Write blog posts with correct dates, frontmatter, and brand voice Prevents: Date collisions, incomplete frontmatter, accidental early publication Usage: /blog-post [topic/title]

2. update-linear-issue

Purpose: Update Linear tickets from command line Prevents: Context loss, manual ticket updates, broken links Usage: Direct API calls or Git commit keywords

3. fix-test-scripts

Purpose: Validate and fix test script path handling Prevents: Relative path failures, missing error handling, platform-specific bugs Usage: /fix-test-scripts

How Skills Catch Insecure Code

Example 1: Exposed API Keys

Before we created the credential manager, Claude occasionally suggested:

// ❌ INSECURE - Claude might suggest this:
const apiKey = "sk-ant-api03-your-actual-key-here";

Now, the blog-post and update-linear-issue skills enforce:

// ✅ SECURE - Skills enforce this pattern:
const { getApiKey } = require('./credential-manager');
const apiKey = getApiKey(); // From Windows Credential Manager

How the skill prevents it:

  • Credential manager pattern documented in skills
  • All example code uses secure storage
  • No plaintext keys in any skill-generated code

Example 2: Unsafe File Operations

Before fix-test-scripts skill:

// ❌ PROBLEMATIC - No validation:
fs.writeFileSync('results.csv', data);

After fix-test-scripts enforcement:

// ✅ SAFE - Validated paths:
const resultsPath = path.join(__dirname, 'test-results', 'results.csv');
const resultsDir = path.dirname(resultsPath);
if (!fs.existsSync(resultsDir)) {
  fs.mkdirSync(resultsDir, { recursive: true });
}
fs.writeFileSync(resultsPath, data, { mode: 0o644 }); // Proper permissions

How the skill prevents it:

  • Enforces directory existence checks
  • Validates write permissions
  • Uses explicit file modes (Unix permissions)

How Skills Catch Buggy Code

Example 1: PowerShell Environment Variables

The mistake: Assuming Node.js inherits PowerShell environment variables on Windows.

# ❌ BROKEN - Doesn't work:
$env:LINEAR_API_KEY = "your-key"
node update-linear-issue.js SAPB-20
# Error: LINEAR_API_KEY is undefined in Node.js

The fix (now enforced by update-linear-issue skill):

# ✅ WORKS - Explicitly load from User environment:
$env:LINEAR_API_KEY = [System.Environment]::GetEnvironmentVariable('LINEAR_API_KEY', 'User')
& node update-linear-issue.js @args

How the skill prevents it:

  • PowerShell wrapper handles environment loading
  • Documented in skill SKILL.md
  • Example code enforces the pattern

Example 2: Script Path Resolution

The mistake: Assuming relative paths work from any directory.

# ❌ BROKEN - Only works from 00-Project-Management/:
& node update-linear-issue.js @args

The fix (now enforced):

# ✅ WORKS - Resolves script directory:
$scriptDir = Split-Path -Parent $MyInvocation.MyCommand.Path
& node (Join-Path $scriptDir "update-linear-issue.js") @args

How the skill prevents it:

  • All skill scripts use explicit directory resolution
  • Pattern documented in skill templates
  • Claude reuses the pattern in new code

The Compounding Effect

Skills build on each other:

  1. blog-post skill uses Linear integration pattern from update-linear-issue
  2. update-linear-issue uses path resolution pattern from fix-test-scripts
  3. fix-test-scripts uses secure credential pattern from credential-manager

Each skill reinforces patterns from previous skills. The more skills we build, the more guardrails Claude has.

Current Stats:

  • 3 skills built (blog-post, update-linear-issue, fix-test-scripts)
  • 0 repeated mistakes (every fixed bug becomes permanent prevention)
  • ~50 minutes saved per session (accumulated time savings)

Lessons Learned

1. Skills Are Institutional Memory

Without skills:

  • Fix a bug today, repeat it tomorrow
  • Different implementations of the same pattern
  • “Did we solve this before?”

With skills:

  • Fix a bug once, never repeat
  • Consistent implementation across all code
  • “The skill handles this”

Lesson: Every solved problem should become a skill.

2. Skills Enforce Security by Default

Before credential-manager skill:

  • Reminder needed: “Don’t hardcode API keys”
  • Manual review: “Check for exposed secrets”
  • Hope: “Claude won’t make that mistake”

After credential-manager skill:

  • Automatic: All API access uses secure storage
  • Template: Skills provide secure code examples
  • Guaranteed: Pattern is enforced in all new code

Lesson: Security should be the default, not a reminder.

3. Skills Scale AI Reliability

One conversation: Claude is smart, makes occasional mistakes Ten conversations: Patterns start to emerge One hundred conversations with skills: Claude becomes consistent, reliable, predictable

Formula:

  • AI + No Skills = Powerful but unreliable
  • AI + Skills = Powerful AND reliable

Lesson: Skills are how you scale AI from “helpful” to “dependable.”

4. Good Skills Have Three Parts

  1. Prevention - Stop the mistake before it happens
  2. Detection - Catch the mistake if it slips through
  3. Correction - Fix the mistake with clear instructions

Example (fix-test-scripts):

  • Prevention: Enforce __dirname in all path operations
  • Detection: Check if files exist before operations
  • Correction: Error messages include full paths and fix instructions

Lesson: Skills should prevent, detect, AND correct.

What’s Next: Skills Roadmap

Planned Skills:

  1. auto-test-retest - Run retests only for prompts that previously failed
  2. validate-commit - Check commits for common errors before pushing
  3. security-review - Scan code for common vulnerabilities before merging
  4. deploy-check - Verify deployment requirements before release

Goal: Every common task should have a skill. Every repeated mistake should become permanent prevention.

Bottom Line

Skills are guardrails. They turn AI from “mostly helpful” to “reliably excellent.” Every mistake we make becomes a permanent improvement.

The Pattern:

  • Make mistake → Fix it → Create skill → Never repeat

Current Stats:

  • 3 skills built
  • 0 repeated mistakes
  • ~50 minutes saved per session
  • Countless bugs prevented

Next Step: What mistake will you turn into a skill today?

Next post: How we use Linear to keep track of 37 issues across 4 milestones—preventing confusion in long AI conversations.