AI is Like a Brilliant Intern Who Needs Guardrails

Meta Description: AI is brilliant but imperfect—like an intern who learns fast but needs guidance. Here’s how skills and guardrails catch bugs before they ship.

Everyone’s treating AI like either magic or garbage. Both views are wrong.

AI is like a brilliant intern who learns extremely fast but needs clear guidelines.

Give an intern vague instructions: “Make this better.” You get unpredictable results.

Give them a checklist, examples, and guardrails: “Check these 5 things, follow this pattern, flag if you see X.” You get consistent, high-quality output.

Same with AI. The difference is how you structure the work.

Here’s what we learned after 90 days and 132 AI-assisted tasks.

The Intern Analogy (Because It’s Perfect)

Think about onboarding a new intern:

Week 1: They’re eager but don’t know your codebase, standards, or patterns. Every task needs detailed instructions.

Week 4: They’ve learned your style. You can say “Add logging to this module” and they know where it goes, what format to use, what to log.

Week 12: They anticipate issues. “I added logging but also noticed this error handling was inconsistent, so I fixed that too.”

AI is permanently stuck at Week 4-6. It learns your patterns within a conversation but doesn’t retain context across sessions (unless you build that in).

What This Means:

✅ AI is great at: Following established patterns, applying checklists, doing tedious work consistently ❌ AI is bad at: Novel architecture, strategic decisions, understanding business context beyond the immediate task

The solution: Treat AI like a smart intern. Give it structure, examples, and guardrails.

Real Example: Skills as Guardrails

We use Claude Code’s “skills” feature to create reusable workflows with built-in quality checks.

Skill = A structured prompt with guardrails built in.

Before Skills (Vague Instructions):

Human: “Review this pull request”

AI:

• Reads the code
• Says “looks good” or points out random style issues
• Misses actual bugs (race conditions, security vulnerabilities, logic errors)
• Inconsistent quality depending on how you phrased the request

Result: Unreliable. You still need to do a full manual review.

After Skills (Structured Guardrails):

Skill definition: /review-pr

# Pull Request Review Skill

## Process:
1. Read all changed files
2. Check against this security checklist:
   - [ ] No hardcoded secrets or API keys
   - [ ] User input is validated/sanitized
   - [ ] SQL queries use parameterized statements
   - [ ] Auth/authorization is correct
   - [ ] Error messages don't leak sensitive info

3. Check code quality:
   - [ ] Functions are focused (one responsibility)
   - [ ] No obvious performance issues
   - [ ] Error handling is appropriate
   - [ ] Tests cover critical paths

4. Check for common bugs:
   - [ ] Race conditions in async code
   - [ ] Off-by-one errors in loops
   - [ ] Null/undefined handling
   - [ ] Edge cases (empty arrays, zero values, etc.)

5. Output format:
   **Security Issues:** [List or "None found"]
   **Code Quality:** [List or "Looks good"]
   **Potential Bugs:** [List or "None found"]
   **Recommendation:** [Approve/Request Changes/Needs Discussion]

Human: /review-pr

AI:

• Follows the exact checklist every time
• Catches security issues humans miss (found 3 hardcoded API keys in 90 days)
• Flags race conditions consistently
• Provides structured output you can act on

Result: Reliable, consistent, catches real bugs. Human focuses on architecture and business logic.

The 7 Skills We Use Daily

Here are the skills that save us the most time:

1. /commit - Git Commit with Standards

What it does:

• Runs git status and git diff
• Reads recent commit messages to learn the style
• Drafts a commit message following the repo’s conventions
• Stages relevant files, creates commit
• Never commits secrets or sensitive files

Guardrails:

• Must include “why” not just “what”
• Must be 1-2 sentences (concise)
• Flags files that look sensitive (.env, credentials.json)
• Runs pre-commit hooks, fixes issues if needed

Time saved: ~5 minutes per commit × 50 commits = 4+ hours

2. /review-pr - Pull Request Review

(Detailed above)

Time saved: ~15 minutes per PR × 20 PRs = 5 hours

3. /fix-tests - Test Script Validation

What it does:

• Validates test scripts use full paths (not relative)
• Checks for common errors (missing error handling, hardcoded values)
• Ensures tests are idempotent
• Adds logging to failing tests

Guardrails:

• Must preserve existing test logic
• Cannot change test assertions (only improve infrastructure)
• Flags tests that seem to test multiple things

Time saved: ~20 minutes per test suite × 12 suites = 4 hours

4. /blog-post - Write Blog Posts

What it does:

• Finds next available date automatically
• Applies Alien Brain Trust brand voice (concise, data-driven, no hype)
• Sets proper frontmatter and formatting
• Always sets published: false for review

Guardrails:

• No medical/legal/financial advice
• No clickbait headlines
• Must include data or examples (not generic advice)
• Security considerations required if relevant
• Mark unverified stats with [CITATION NEEDED]

Time saved: ~30 minutes per post × 15 posts = 7.5 hours

5. /refactor - Code Refactoring

What it does:

• Reads all affected files first (full context)
• Identifies patterns to apply
• Proposes changes before implementing
• Maintains exact indentation and style

Guardrails:

• Cannot change behavior (refactor only, not rewrite)
• Must preserve comments and documentation
• Flags if refactor affects more than 10 files (needs human approval)
• Runs tests after refactoring

Time saved: ~45 minutes per refactor × 11 refactors = 8+ hours

6. /security-scan - Security Vulnerability Check

What it does:

• Scans code for OWASP Top 10 vulnerabilities
• Checks for hardcoded secrets
• Validates input sanitization
• Reviews authentication/authorization logic

Guardrails:

• Must cite specific vulnerability type (SQL injection, XSS, etc.)
• Must provide remediation code, not just flag issues
• Cannot approve code with critical vulnerabilities

Time saved: ~30 minutes per scan × 15 scans = 7.5 hours

7. /document - Generate Documentation

What it does:

• Reads code to understand functionality
• Generates API docs, README sections, inline comments
• Follows existing documentation style
• Includes usage examples

Guardrails:

• Must be accurate (no hallucinating functionality)
• Cannot document private/internal APIs unless explicitly requested
• Must include error cases and edge cases
• Flags if generated docs seem incomplete

Time saved: ~25 minutes per doc task × 18 tasks = 7.5 hours

Total time saved from 7 skills: 43+ hours in 90 days

How to Build Effective Skills/Guardrails

Here’s the framework we use:

1. Define the Process (Step-by-Step)

1. Read relevant files
2. Check condition A
3. If A passes, check condition B
4. Output in format X

Why: AI follows explicit steps reliably. Vague instructions produce inconsistent results.

2. Add Checklists for Quality

Before outputting, verify:
- [ ] No hardcoded secrets
- [ ] Tests are included
- [ ] Documentation is updated

Why: Checklists catch common mistakes AI makes (and humans make too).

3. Specify Output Format

Output format:
**Summary:** [One sentence]
**Changes:** [Bulleted list]
**Tests:** [Pass/Fail/Not Run]

Why: Structured output is easier to review and act on than freeform text.

4. Build in Safety Rails

NEVER:
- Commit .env files
- Skip tests
- Remove error handling

ALWAYS:
- Preserve existing comments
- Run tests after changes
- Flag breaking changes for human review

Why: AI will do exactly what you ask, even if it’s a bad idea. Safety rails prevent common disasters.

5. Provide Examples

Good commit message:
"Add rate limiting to API endpoints to prevent abuse"

Bad commit message:
"Update files"

Why: AI learns from examples faster than from abstract rules.

Real Bugs Skills Caught Before Shipping

Here are actual bugs our skills caught in 90 days:

Bug 1: Hardcoded API Key (Caught by /commit)

What happened: Developer added a test file with a real API key for debugging. How skill caught it: /commit skill flags any file containing strings that look like API keys. Output: “Warning: test-api-client.js contains what appears to be an API key. Review before committing.” Result: Key removed, added to .env instead. Would have been a critical leak.

Bug 2: Race Condition in Async Code (Caught by /review-pr)

What happened: PR added concurrent file operations without proper locking. How skill caught it: /review-pr checklist includes “Race conditions in async code.” Output: “Potential bug: Multiple async operations modify the same file without coordination. Consider adding a mutex or queue.” Result: Developer added a file lock. Bug would have caused data corruption in production.

Bug 3: SQL Injection Vulnerability (Caught by /security-scan)

What happened: New feature concatenated user input into SQL query. How skill caught it: /security-scan specifically checks for string concatenation in SQL queries. Output: “Security issue: SQL query uses string concatenation with user input. Use parameterized queries instead.” Result: Fixed before merge. Would have been a critical vulnerability.

Bug 4: Missing Error Handling (Caught by /fix-tests)

What happened: Test script assumed network requests would always succeed. How skill caught it: /fix-tests validates error handling in test infrastructure. Output: “Test script makes HTTP requests without try/catch. Will crash on network errors.” Result: Added error handling. Test suite became more reliable.

Bug 5: Incomplete Documentation (Caught by /document)

What happened: AI-generated docs didn’t mention a required environment variable. How skill caught it: /document skill requires documenting all configuration. Output: “Documentation incomplete: CODE_SIGNING_KEY environment variable is required but not documented.” Result: Docs updated. Saved future developer hours of debugging.

Total prevented: 5 production bugs, 1 security vulnerability, multiple hours of debugging

When Skills Work (and When They Don’t)

After 90 days, here’s what we learned:

Skills Excel At:

✅ Enforcing standards - Every commit message follows the same format ✅ Catching common mistakes - Hardcoded secrets, missing tests, SQL injection ✅ Maintaining consistency - Same security checks applied to every PR ✅ Reducing cognitive load - Don’t have to remember 20-item checklists ✅ Onboarding new team members - Skills encode team knowledge

Skills Struggle With:

❌ Novel situations - If the checklist doesn’t cover it, skill won’t catch it ❌ Business logic errors - Skills catch pattern bugs, not domain-specific mistakes ❌ Strategic decisions - “Should we refactor or rewrite?” requires human judgment ❌ Context beyond the code - Skills don’t know your 6-month roadmap ❌ Ambiguous requirements - Skills need clear criteria to evaluate against

The Decision Framework:

Is the task repetitive with clear quality criteria?
├─ YES → Build a skill with checklists
└─ NO → Use AI conversationally, human makes decisions

How to Implement This in Your Workflow

Week 1: Identify Repetitive Tasks

• What do you do 5+ times per week?
• What mistakes do you catch in code review repeatedly?
• What quality checks do you wish you remembered to do?

Week 2: Build Your First Skill

• Pick the most common repetitive task
• Write a step-by-step process
• Add a quality checklist
• Test it on 5 real examples

Week 3: Refine and Expand

• Did the skill catch real issues? Keep it.
• Did it flag false positives? Refine the criteria.
• Build a second skill for the next common task.

Week 4: Systematize

• Document your skills
• Train team on when to use each skill
• Track bugs caught vs. false positives
• Iterate based on results

Timeline: Most teams see measurable quality improvements in 30 days.

The Bottom Line

AI isn’t perfect. It’s not supposed to be.

It’s a brilliant intern who:

• Learns your patterns fast
• Works tirelessly without fatigue
• Follows checklists perfectly
• But needs structure and guardrails

The teams that win with AI aren’t the ones using it for everything.

They’re the ones who encode their standards into reusable skills, use AI to enforce those standards, and focus human attention on strategy, architecture, and novel problems.

That’s the leverage: AI handles the repetitive work perfectly. Humans focus on the creative work.

Your intern doesn’t need to be perfect. They need to be reliably good at the repetitive stuff so you can focus on the hard problems.

That’s exactly what AI is.

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Next in this series: Post 5 covers documentation workflows—the template-driven approach that cut our doc time by 60% and the quality checklist that ensures AI-generated docs don’t suck.

Try this today: Pick one repetitive task you do often (code review, writing commit messages, security checks). Write a 5-item checklist for it. Use AI with that checklist for a week. Track what it catches.