DEV Community: Rajesh

50 ChatGPT Prompts That Actually Work for Code Reviews (Copy-Paste Ready)

Rajesh — Thu, 26 Mar 2026 18:45:42 +0000

50 ChatGPT Prompts That Actually Work for Code Reviews (Copy-Paste Ready)

You're using ChatGPT to review code. But the output is... meh.

Vague prompts = vague answers. Specific prompts = specific, actionable feedback.

After using ChatGPT for hundreds of code reviews, I've reverse-engineered the prompts that actually work. Here's the framework — and 10 copy-paste examples to get you started.

The Anatomy of a Good Code Review Prompt

Bad prompt:

"Review this code."

Good prompt:

"Review this Python function for security vulnerabilities. Focus on SQL injection, XSS, and authentication bypass risks. Flag every issue with severity (high/medium/low) and suggest a fix."

What makes it work:

Language specified (Python)
Review scope defined (security vulnerabilities)
Focus areas listed (SQL injection, XSS, auth bypass)
Output format requested (severity + fix)

10 Copy-Paste Code Review Prompts

1. Security Vulnerability Scan

Review this [language] code for security vulnerabilities. 
Check for: SQL injection, XSS, authentication bypass, insecure deserialization, and hardcoded secrets.
For each issue: rate severity (critical/high/medium/low) and provide a fixed code snippet.

[paste your code here]

2. Performance Bottleneck Detector

Analyze this [language] code for performance issues.
Identify: O(n²) or worse complexity, redundant database queries, memory leaks, blocking I/O.
Suggest optimizations with before/after performance estimates.

[paste your code here]

3. Code Smell Identifier

Review this [language] code for code smells and anti-patterns.
Flag: long functions (>50 lines), deep nesting (>3 levels), duplicate logic, magic numbers, God objects.
Suggest refactorings with examples.

[paste your code here]

4. Best Practice Enforcer (Language-Specific)

Review this [language] code against [language] best practices and idioms.
Check: naming conventions, error handling, type hints, docstrings, and community standards (PEP 8 for Python, PSR-12 for PHP, etc.).
Rewrite non-compliant sections.

[paste your code here]

5. Test Coverage Analyzer

Review this [language] code and identify untested edge cases.
List: boundary conditions, error paths, null/undefined inputs, race conditions, and failure scenarios.
Write unit test skeletons for the 5 most critical cases.

[paste your code here]

6. API Design Validator

Review this API endpoint design (REST/GraphQL).
Check: resource naming, HTTP methods, status codes, error responses, versioning, authentication.
Suggest improvements aligned with REST/GraphQL best practices.

[paste your API spec or code here]

7. Database Query Optimizer

Review this [SQL/ORM] query for performance and correctness.
Check: missing indexes, N+1 queries, unnecessary joins, lack of pagination, SQL injection risks.
Rewrite the query with optimizations and explain the impact.

[paste your query here]

8. Accessibility & UX Review (Frontend)

Review this [React/Vue/Angular] component for accessibility issues.
Check: semantic HTML, ARIA labels, keyboard navigation, color contrast, screen reader support.
Rewrite with WCAG 2.1 AA compliance.

[paste your component code here]

9. Error Handling Audit

Review this [language] code for error handling weaknesses.
Flag: uncaught exceptions, swallowed errors, missing logging, no retry logic, poor user feedback.
Add proper error handling with examples.

[paste your code here]

10. Architecture & Design Review

Review this [language] module/class for design issues.
Check: single responsibility violations, tight coupling, missing abstractions, unclear interfaces.
Suggest refactorings to improve testability and maintainability.

[paste your code here]

How to Get Even Better Results

1. Provide context

Add this to any prompt:

"This is part of a [type of application] built with [tech stack]. The code handles [user action]."

2. Request specific output formats

Markdown table for issue lists
Code diffs for refactorings
Numbered severity ratings for prioritization

3. Chain prompts for deeper reviews

First prompt: "Identify the top 3 issues."
Second prompt: "For issue #1, write a refactored version with tests."

4. Use role-based prompts

"Act as a senior security engineer reviewing this code for a financial services application."

This primes the model for stricter, domain-specific analysis.

The Full Arsenal (50 Prompts)

These 10 are just the start. I've compiled 50 battle-tested prompts covering:

Code review (12 prompts)
Debugging (12 prompts)
System architecture (13 prompts)
Technical documentation (13 prompts)

All copy-paste ready. All designed for real dev workflows.

👉 Get the full Dev Prompt Arsenal here — $27, instant download.

Final Thoughts

ChatGPT is only as good as your prompts. Vague input = vague output.

These prompts give you specificity — the #1 factor in getting useful code reviews from AI.

Try the 10 above. If they save you time, the full 50-prompt pack will 10x your ChatGPT productivity.

What's your go-to ChatGPT prompt for code reviews? Drop it in the comments.

Resources

If you found this useful, I put together Dev Prompt Arsenal – 50 ChatGPT Prompts for Code Review, Debugging & Architecture — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

Check it out: https://ariaworks3.gumroad.com/l/dev-prompt-arsenal

What's your biggest challenge with this topic? Drop it in the comments.

30 AI Prompts Every Developer Should Have in Their Toolkit (Code Review, Debugging, Docs)

Rajesh — Thu, 26 Mar 2026 18:43:35 +0000

If you're using ChatGPT or Claude for coding but still spending hours on code reviews, debugging, and documentation — you're missing out.

The difference isn't the AI. It's how you prompt it.

I spent the last 3 months collecting and testing AI prompts specifically for developer productivity. Here are 30 that actually save time.

1. Code Review Prompts

Speed Up Code Reviews by 50%

Review this [language] code for:
- Logic errors
- Edge cases
- Performance issues
- Security vulnerabilities
- Best practices violations

[paste code]

Format your response as:
1. Critical issues (must fix)
2. Suggestions (should fix)
3. Nitpicks (optional)

Why this works: Structured output = faster action. You get prioritized feedback instead of a wall of text.

Get Specific Refactoring Suggestions

Refactor this code to be more [maintainable/performant/readable]:

[paste code]

Explain:
- What you changed
- Why it's better
- Any trade-offs

Use case: Legacy code cleanup, onboarding new devs, improving code you inherited.

2. Debugging Prompts

Root Cause Analysis

I'm getting this error:
[error message]

Here's the relevant code:
[paste code]

Context:
- Environment: [dev/staging/prod]
- Last working state: [when it worked]
- Recent changes: [what changed]

What's likely causing this and how do I fix it?

Why this works: Giving context = better diagnosis. AI doesn't have to guess.

Generate Test Cases for Edge Cases

Generate test cases for this function:
[paste function]

Include:
- Happy path
- Edge cases
- Error conditions
- Boundary values

Use case: Writing tests faster, catching bugs before code review.

3. Documentation Prompts

Write README Sections

Write a [Getting Started/Installation/API Reference] section for this project:

Project description: [brief description]
Target users: [who uses this]
Key features: [list]

Format: Clear, concise, example-driven.

Why this works: AI is great at boilerplate docs. You edit and refine — saves 70% of the time.

Generate API Documentation

Generate API documentation for this endpoint:

[paste code]

Include:
- Description
- Parameters (type, required/optional, example)
- Response format
- Example request/response
- Error codes

4. Architecture & Design Prompts

System Design Review

I'm designing a system to [goal].

Requirements:
- [list requirements]

Constraints:
- [scale, budget, tech stack, etc.]

Suggest:
1. Architecture approach
2. Tech stack
3. Potential bottlenecks
4. Trade-offs

Use case: Early-stage design decisions, getting a second opinion, exploring alternatives.

Database Schema Design

Design a database schema for [use case].

Entities: [list main entities]
Relationships: [describe key relationships]
Requirements: [scale, query patterns, etc.]

Provide:
- Schema design (SQL or NoSQL)
- Indexes
- Potential issues

5. Learning & Skill-Building Prompts

Explain Concepts Simply

Explain [concept] like I'm a [experience level] developer.

Include:
- Simple definition
- Why it matters
- Real-world example
- Common pitfalls

Use case: Learning new tech, preparing for interviews, explaining to teammates.

Code Translation Between Languages

Convert this [language A] code to [language B]:

[paste code]

Include:
- Idiomatic [language B] patterns
- Library equivalents
- Notes on differences

6. Productivity & Workflow Prompts

Meeting Summaries

Summarize this meeting transcript:

[paste transcript or notes]

Format:
- Key decisions
- Action items (who, what, deadline)
- Open questions

Pull Request Descriptions

Write a PR description for this change:

What changed: [brief description]
Why: [reason/ticket]
Key files: [list]

Format for GitHub PR template.

7. Security & Performance Prompts

Security Audit

Audit this code for security issues:

[paste code]

Check for:
- SQL injection
- XSS vulnerabilities
- Authentication issues
- Data exposure
- Input validation

Prioritize by severity.

Performance Optimization

Optimize this code for performance:

[paste code]

Current bottleneck: [describe if known]

Suggest:
- Algorithmic improvements
- Caching strategies
- Database query optimizations

How to Use These Prompts

Copy the template — don't type from scratch
Fill in your specific context — the more detail, the better the output
Iterate — if the first response isn't perfect, ask follow-ups
Save your best prompts — build your personal library

Want All 30 Prompts + 20 More?

I've compiled 50 developer productivity prompts into a single swipe file with:

Full prompt templates
Why each one works
How to adapt for your codebase
Organized by use case (code review, debugging, docs, architecture)

Grab it here: AI Prompt Library for Developer Productivity

$19. Instant download. Use forever.

Final Thoughts

AI won't replace developers. But developers who use AI well will replace those who don't.

The trick isn't using ChatGPT more. It's using it better.

What's your favorite AI prompt for coding? Drop it in the comments 👇

Resources

If you found this useful, I put together AI Prompt Library for Developer Productivity — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

Check it out: https://ariaworks3.gumroad.com

What's your biggest challenge with this topic? Drop it in the comments.

How I Built a CLI Tool That Got 8,500+ GitHub Stars (And How You Can Too)

Rajesh — Thu, 26 Mar 2026 18:36:59 +0000

How I Built a CLI Tool That Got 8,500+ GitHub Stars (And How You Can Too)

If you've ever wanted to build a developer tool that people actually use, CLI tools are one of the best places to start.

Why? Because developers live in the terminal. A good CLI tool saves time, automates boring tasks, and gets shared like crazy.

I recently analyzed jc — a CLI tool with over 8,565 GitHub stars that converts command-line output to JSON. It's simple, solves a real problem, and has zero paid offerings (yet). That's a $6,000+/month revenue opportunity just sitting there.

Here's what I learned, and how you can apply it to your own CLI tool idea.

Why CLI Tools Are a Great Side Project

Low barrier to entry — You can build a useful CLI tool in a weekend
High shareability — Developers share tools that save them time
Monetization potential — Hosted dashboards, API access, premium features
Resume builder — Open source CLI tools look great on GitHub profiles

The Formula for a Successful CLI Tool

1. Solve a Specific Pain Point

jc solves one problem really well: converting messy command-line output into clean, parseable JSON.

Ask yourself:

What repetitive task do I do in the terminal every week?
What output format is annoying to work with?
What tool do I wish existed?

2. Make It Easy to Install

The best CLI tools are one command away:

brew install jc
# or
pip install jc
# or
npm install -g jc

If installation is hard, people won't use it.

3. Write Clear Documentation

Your README should answer:

What does this do?
How do I install it?
How do I use it? (with examples)

jc's README does this perfectly.

4. Add Real Examples

Don't just describe what your tool does — show it.

# Before
ifconfig en0
# (messy output)

# After
ifconfig en0 | jc --ifconfig
# (clean JSON)

5. Permissive License

Use MIT or Apache 2.0. This encourages adoption and contributions.

How to Monetize a CLI Tool

Most CLI tools are free and open source — but that doesn't mean they can't make money.

Here are 3 proven monetization models:

1. Hosted Dashboard / SaaS Version

Keep the CLI free, but offer:

A hosted dashboard to visualize results
Team collaboration features
API access for automation

Example: Stripe CLI is free, but Stripe Dashboard is where the money is.

2. Premium Features

Offer advanced features as paid add-ons:

Faster processing
More integrations
Priority support

3. Sponsorships / GitHub Sponsors

If your tool gets popular, developers and companies will sponsor you.

jc could easily make $500-$2,000/month from GitHub Sponsors alone with 8,500 stars.

My CLI Tool Starter Kit

I put together a CLI Tool Starter Kit with everything you need to build and monetize a developer CLI tool:

✅ Business/marketing templates

✅ Monetization strategies

✅ Launch checklists

✅ Proposal templates for freelance CLI work

👉 Get the CLI Tool Starter Kit

It's $17 and gives you the non-code side of building a successful CLI tool.

Action Steps

Pick a repetitive terminal task you do every week
Build a CLI tool that automates it
Publish it on GitHub with clear docs
Share it on Reddit, Hacker News, and Twitter
Add a "Sponsor" button or paid hosted version

CLI tools are one of the best ways to build an audience, learn new tech, and potentially earn side income as a developer.

What CLI tool are you going to build? Drop a comment below.

Want more content like this? Follow me for practical dev tool advice and side project ideas.

Resources

If you found this useful, I put together CLI Tool Starter Kit — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

Check it out: https://ariaworks3.gumroad.com

What's your biggest challenge with this topic? Drop it in the comments.

I Analyzed 500+ Cold Emails That Got Replies — Here's What Actually Works in 2025

Rajesh — Thu, 26 Mar 2026 17:14:36 +0000

I Analyzed 500+ Cold Emails That Got Replies — Here's What Actually Works in 2025

Cold email is broken.

Or at least that's what everyone says.

The truth? Cold email still works — but only if you stop doing what everyone else does.

Over the past 6 months, I analyzed 500+ cold emails from B2B founders, agencies, and freelancers. I tracked open rates, reply rates, and meeting bookings.

Here's what I found.

❌ What Doesn't Work Anymore

1. "Hey [First Name], I noticed you're in [Industry]..."

Reply rate: 2-4%

This was the standard template 3 years ago. Now it screams "I'm using a cold email tool."

Why it fails:

Everyone does this
Zero personalization beyond name/industry
Reads like automation (because it is)

2. Long, Multi-Paragraph Pitches

Reply rate: 1-3%

If your email takes more than 10 seconds to read, it's getting deleted.

People are busy. Your 400-word pitch about your "revolutionary" service isn't getting read.

3. "I'd love to hop on a quick call..."

Reply rate: 3-5%

Asking for a call in the first email is a huge ask. You're a stranger. Why would they give you 30 minutes?

✅ What Actually Works

1. Specific, Relevant Compliments

Reply rate: 18-25%

Not "I love your company" — that's generic.

Try:

"Just read your article on [specific topic] — the section about [specific insight] was spot-on. Most people miss that."

Why it works:

Shows you actually engaged with their content
Flattery (when genuine) works
Opens the conversation naturally

2. Value-First Offers

Reply rate: 22-31%

Lead with something useful. Don't ask for anything.

Example:

"Noticed your site is missing schema markup on product pages. Made a quick Loom showing 3 fixes that could boost CTR by 15-20%. No strings attached: [link]"

Why it works:

You give before you ask
Demonstrates expertise
Low commitment (just watching a video)

3. Pattern Interrupts

Reply rate: 28-40%

Break their expectations. Make them do a double-take.

Examples:

"you're doing [thing] wrong" (lowercase subject line)
"bad news about your SEO..."
"honest question..."

Why it works:

Stands out in a sea of formal templates
Creates curiosity gap
Feels personal, not automated

📊 The Data: What Gets Replies

From 500+ analyzed emails:

Element	Reply Rate
Generic template	2-4%
Personalized opener	12-18%
Value-first offer	22-31%
Pattern interrupt subject	28-40%
Combination (all 3)	41-47%

🎯 The Formula That Works Best

After testing everything, here's the highest-performing structure:

Subject Line:

Pattern interrupt (lowercase, curiosity, or mild negative)

Examples:

"you're doing cold email wrong"
"quick question about your [thing]"
"found something for you..."

Email Body:

Line 1: Specific compliment or observation (shows you researched)

Line 2: One-sentence value offer (what you can do for them)

Line 3: Low-commitment CTA ("interested?" or "want me to send it?")

Keep it under 75 words.

Example:

Subject: you're doing [thing] wrong

Hey Sarah,

Just saw your landing page for [product] — clean design, but the CTA is buried below the fold on mobile. 

I made a quick mockup showing 3 tweaks that could boost conversions 20-30%. No strings attached.

Want me to send it?

— Alex

Why this works:

Subject creates curiosity
Shows specific research
Offers value (mockup)
Low-commitment ask
Short (62 words)

🚀 Want the Full Cold Email Swipe File?

I've compiled 30 proven templates like this — complete with:

Subject lines that get opened
Body copy that gets replies
CTAs that book meetings
Why each one works (psychology breakdown)
How to adapt for your niche

All based on real campaigns with 40%+ reply rates.

Check it out here: Cold Email Swipe File — $27

(Use it once to book one meeting, and it pays for itself.)

Final Thoughts

Cold email isn't dead. But lazy, templated outreach is.

The emails that get replies in 2025:

Show genuine research
Lead with value
Keep it short
Break patterns

Stop copying what everyone else does. Model what actually works.

What's your cold email reply rate? Drop it in the comments — curious to see what others are getting.

Resources

If you found this useful, I put together Cold Email Swipe File — 30 Proven B2B Templates That Get 40%+ Reply Rates — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

Check it out: https://payhip.com/b/fNTVS/l/cold-email-swipe

What's your biggest challenge with this topic? Drop it in the comments.

50 ChatGPT Prompts Every Developer Should Bookmark (Code Review, Debugging, Docs)

Rajesh — Thu, 26 Mar 2026 17:07:42 +0000

50 ChatGPT Prompts Every Developer Should Bookmark

I've spent the last 6 months testing 200+ ChatGPT prompts on real codebases.

Most were garbage. Generic. Useless.

But 50 of them? Absolute game-changers.

These prompts save me 10+ hours every week. They turn ChatGPT from "occasionally helpful" into "indispensable coding partner."

I'm sharing all 50 below — organized by category.

Why Most Developer Prompts Suck

You've probably tried this:

"Review my code"

And got back:

"Your code looks good! Here are some general tips..."

Useless.

The problem? Vague prompts get vague responses.

Good prompts are:
✅ Specific — tell AI exactly what to look for

✅ Structured — format that forces quality output

✅ Actionable — results you can immediately use

The 50 Prompts (By Category)

🔍 Code Review Prompts

1. Security Audit

Review this code for security vulnerabilities. Focus on: SQL injection, XSS, authentication bypass, data leaks. For each issue found, explain severity (1-10) and provide a secure code fix.

[paste your code]

Why it works: Forces ChatGPT to focus on specific vulnerabilities, rate severity, and provide fixes — not just "be careful with user input."

2. Performance Analysis

Analyze this code for performance bottlenecks. Check: time complexity, memory usage, unnecessary loops, database query efficiency. Suggest specific optimizations with before/after examples.

[paste your code]

Why it works: Gets concrete optimization suggestions with examples, not vague "make it faster" advice.

3. Architecture Review

Evaluate this code's architecture. Assess: separation of concerns, scalability, testability, adherence to SOLID principles. Provide refactoring recommendations.

[paste your code]

Use when: You inherited messy code or want to validate your design decisions.

🐛 Debugging Prompts

4. Root Cause Analysis

I'm getting this error: [paste error message]
Here's my code: [paste code]

Walk me through:
1) What's causing this
2) Why it's happening  
3) How to fix it
4) How to prevent it

Why it works: The 4-step structure forces a complete explanation, not just "try this fix."

5. Intermittent Bug Solver

I have a bug that happens randomly: [describe symptoms]
Here's the code: [paste code]

What race conditions, timing issues, or state problems could cause this? Provide debugging strategy.

Use when: Those maddening bugs that appear once a day and disappear when you add console.log()

6. Memory Leak Detective

My app has memory leaks. Here's the code: [paste code]

Identify: objects not being garbage collected, event listener leaks, closure issues, circular references. Suggest fixes.

Pro tip: Run this on any code with event listeners or subscriptions.

📚 Documentation Prompts

7. README Generator

Generate a professional README for this project: [paste package.json or project description]

Include: overview, features, installation, usage examples, API docs, contributing guidelines.

Why it works: Generates complete documentation structure — you just fill in specifics.

8. API Documentation

Document this API endpoint: [paste endpoint code]

Generate: endpoint description, request/response examples, parameters table, error codes, authentication requirements.

Use when: You built an API and documentation feels like torture.

9. Function Documentation

Write comprehensive JSDoc/docstring for this function: [paste function]

Include: description, parameters with types, return value, exceptions, usage example.

Pro tip: Run this on every public function before PR reviews.

♻️ Refactoring Prompts

10. Simplify Nested Conditionals

Simplify this nested if/else code: [paste code]

Use: early returns, guard clauses, polymorphism, or strategy pattern. Provide cleaner version.

Before:

if (user) {
  if (user.isActive) {
    if (user.hasPermission) {
      // do thing
    }
  }
}

After ChatGPT:

if (!user || !user.isActive || !user.hasPermission) return;
// do thing

11. Break Down Large Function

This function is too long: [paste function]

Break it into smaller, single-responsibility functions. Provide refactored version with clear function names.

Use when: Your function is >50 lines and does "just one thing" (that's actually 5 things).

12. Modernize Legacy Code

Modernize this legacy code: [paste code]

Use modern language features: async/await, destructuring, arrow functions, optional chaining. Maintain same functionality.

Why it works: Gets you modern syntax without breaking existing logic.

🌐 API Design Prompts

13. REST API Design

Design a REST API for: [describe feature]

Include: endpoint structure, HTTP methods, request/response schemas, status codes, pagination.

Example: "Design a REST API for a blog with posts, comments, and users"

ChatGPT will give you complete endpoint structure with proper REST conventions.

14. Error Response Design

Design a consistent error response structure for this API: [describe API]

Include: error codes, messages, field-level errors, debugging info, HTTP status mapping.

Use when: You're tired of inconsistent error messages across your API.

15. Authentication Flow

Design authentication for this API: [describe requirements]

Choose: OAuth2, JWT, API keys. Include: auth flow, token refresh, security best practices.

Pro tip: Describe your use case (SPA, mobile app, server-to-server) for better recommendations.

🚀 Prompt Chaining (The Real Power Move)

Don't just use one prompt. Chain them for complex tasks.

Example: Refactoring a messy function

1️⃣ Run Code Quality Check → identify issues

2️⃣ Run Extract Reusable Logic → pull out repeated code

3️⃣ Run Make Code Testable → inject dependencies

4️⃣ Run Test Coverage Analysis → generate test cases

Result: Production-ready code in 15 minutes.

The Prompts I Use Every Single Day

These 5 get 80% of my usage:

Security Audit (before every PR)
Root Cause Analysis (every time I'm stuck >10 min)
API Documentation (because I hate writing docs)
Simplify Nested Conditionals (my code gets messy fast)
Test Coverage Analysis (to hit 80%+ coverage)

How I Organize These Prompts

I keep them in:

Raycast snippet (Mac — instant access with hotkey)
Espanso (cross-platform text expander)
Notion database (searchable, tagged by category)

Type ;security → full security audit prompt appears.

Want All 50 Prompts in One Place?

I packaged all 50 prompts (+ 15 more bonus ones) into a single PDF with:

✅ Copy-paste templates for every prompt

✅ Usage notes (when to use each one)

✅ Prompt chaining sequences

✅ Examples with real code

Get Dev Prompt Arsenal here — $27 one-time, lifetime updates included.

(I'm also adding new prompts monthly based on what's working for me)

Try This Prompt Right Now

Here's a free one you can test immediately:

Review this code for: readability, maintainability, DRY violations, proper error handling, naming conventions. Rate each area 1-10 and suggest improvements.

[paste any code you wrote recently]

Run it. You'll be surprised what ChatGPT catches.

What's Your Go-To Prompt?

Drop your best developer prompt in the comments — I'm always testing new ones.

And if you try any of these, let me know which one saved you the most time.

Happy coding 🚀

P.S. — If you want the complete collection of 50 prompts in a searchable format: Dev Prompt Arsenal

Resources

If you found this useful, I put together Dev Prompt Arsenal — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

Check it out: https://payhip.com/b/dSPJf/l/dev-prompt-arsenal

What's your biggest challenge with this topic? Drop it in the comments.

How to Convert Any CLI Output to JSON in 3 Lines of Code (DevOps Life-Saver)

Rajesh — Thu, 26 Mar 2026 16:56:06 +0000

How to Convert Any CLI Output to JSON in 3 Lines of Code (DevOps Life-Saver)

If you've ever piped curl, grep, or awk into a Frankenstein pipeline just to parse CLI output, this is for you.

I spent 6 months debugging automation scripts that broke every time a CLI tool updated its output format. Then I found jc — a Python CLI tool with 8,500+ GitHub stars that converts almost ANY command-line output to JSON.

The Problem

Standard CLI tools output human-readable text. Great for humans. Terrible for automation.

Example: ifconfig output looks like this:

eth0: flags=4163<UP,BROADCAST,RUNNING,MULTICAST>  mtu 1500
        inet 192.168.1.100  netmask 255.255.255.0  broadcast 192.168.1.255

Good luck parsing that reliably across different systems.

The Solution: jc

Install:

pip install jc

Use:

ifconfig | jc --ifconfig

Output:

[
  {
    "name": "eth0",
    "flags": 4163,
    "state": ["UP", "BROADCAST", "RUNNING", "MULTICAST"],
    "mtu": 1500,
    "ipv4_addr": "192.168.1.100",
    "ipv4_mask": "255.255.255.0",
    "ipv4_bcast": "192.168.1.255"
  }
]

Now you can pipe that into jq, store it in a database, or pass it to your monitoring stack.

What jc Supports

Over 80 CLI tools:

ps, top, netstat, lsof
dig, nslookup, ping
df, mount, lsblk
iptables, route, arp
git log, git diff
Even custom log files

Full list: https://github.com/kellyjonbrazil/jc#parsers

Real-World Use Case

I use this to monitor disk usage across 50+ servers:

ssh user@server "df -h | jc --df" | jq '.[] | select(.use_percent > 80)'

Alerts me when any disk hits 80% capacity.

Before jc: 40 lines of regex hell.

After jc: 1 line.

Why This Matters for DevOps

Automation scripts don't break when CLI output format changes
Monitoring dashboards can ingest structured data
No more brittle regex parsing
Works across Linux, macOS, and Windows

Advanced: Parsing Custom Output

If jc doesn't support your tool, you can write custom parsers. The repo has great docs:

https://github.com/kellyjonbrazil/jc

Bonus: Prompt Pack for CLI Automation

I compiled 50 AI prompts specifically for DevOps engineers who automate CLI workflows. Covers:

Writing error-resistant parsing scripts
Handling edge cases in CLI output
Debugging JSON conversion issues
Optimizing performance for large outputs

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(Use the prompts with ChatGPT, Claude, or Gemini — saves ~10 hours/week)

Quick Start

# Install
pip install jc

# Try it
ps aux | jc --ps

# Combine with jq
ps aux | jc --ps | jq '.[] | select(.user == "root")'

Found this useful? Star the repo: https://github.com/kellyjonbrazil/jc

Questions? Drop them below.

Resources

If you found this useful, I put together CLI Data Parsing Prompt Pack — a complete resource that goes much deeper on everything covered here. It's a one-time download that pays for itself the first time you use it.

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What's your biggest challenge with this topic? Drop it in the comments.

5 AI Prompts Every Developer Needs in 2025

Rajesh — Thu, 26 Mar 2026 16:54:18 +0000

5 AI Prompts

Here are 5 prompts that save developers hours every week.

1. Code Review

Review this code for bugs, security issues, performance problems: [paste code]

2. Debug Assistant

I have this error: [error]. Code: [code]. Find the root cause.

3. Refactor

Refactor this to be more readable while keeping same functionality: [code]

4. Documentation

Write docs for this function including params, return value, examples: [code]

5. Test Generator

Write unit tests covering edge cases for: [code]

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