Battle-tested automation playbooks to eliminate manual work and scale your impact.
Systems for better output, not just faster output.
206 workflows · 57 Beginner · 94 Intermediate · 55 Advanced
Showing 17 of 17 workflows
Result: A diagnosed and fixed bug with a clear explanation of the root cause and a test confirming the fix.
Cut your debugging time in half with a structured AI-assisted process for identifying, understanding, and fixing elusive bugs.
Result: A developer-reviewed API specification, prototype repository, inspected test results, endpoint documentation and a restricted staging deployment record, with unresolved security and production-readiness work explicitly listed.
Develop a small REST API prototype with a reviewed specification, endpoint code, test results and a restricted staging deployment. Treat three to four hours as an experienced developer's planning estimate for narrow scope, not a promise of production readiness; security review and hardening take additional work.
Result: A test suite with 60%+ coverage on your core functions, with unit and integration tests running in CI.
Add meaningful test coverage to legacy or untested code in a single focused session, without reading every line first.
Result: A working Chrome extension installed in developer mode, with core functionality running and ready for testing.
Go from idea to a working, installable Chrome extension in an afternoon, no prior extension experience required.
Result: A GitHub Actions workflow that automatically reviews every PR with AI feedback, style checks, and a plain-English summary of changes.
Set up an automated code review pipeline that catches issues, enforces style, and summarizes changes before a human reviewer ever opens the PR.
Result: A working mobile app prototype with core screens, navigation, and basic data handling, ready for user testing.
Turn hand-drawn or digital wireframes into a working mobile app prototype with real navigation and data, no native development experience needed.
Result: A deployed Next.js 14 app with working auth, a Postgres database, and a basic protected dashboard route.
Bootstrap a production-ready Next.js app with authentication, a connected database, and deployment configured, skipping the setup grind entirely.
Result: A working, deployed web application with a database, authentication, and custom logic, built in a single session without writing a line of code.
Go from a product idea to a live, working web application using AI builders, no engineering background required. This workflow covers planning, building, iterating, and deploying.
Result: A repeatable local automation that executes a defined technical task on demand and pushes a summary or notification to your team, at effectively zero marginal cost per run.
Set up a local coding agent that handles repetitive technical work, file cleanup, log parsing, batch renaming, using free open-weight models instead of a metered frontier API, then chain the output into a free automation platform so results land where your team actually looks.
Result: Every pull request gets an automated first-pass review and static analysis scan before a human merges it.
Catch issues before a human reviewer ever looks at the pull request.
Result: A running list of known vulnerabilities in your dependencies, triaged by severity, with patches already opened as pull requests.
Get enterprise-grade dependency security coverage in about an hour a month.
Result: A searchable internal portal documenting every service, API, and runbook you own.
Stand up a self-service documentation hub for your own codebase in about two hours.
Result: A fully scaffolded, implemented, and QA-checked feature ready for a final human review.
Move from feature request to merged pull request in about half a day.
Result: A repeatable habit of testing AI-generated code inside a disposable cloud sandbox first, with your local machine and files never directly exposed to code you haven't reviewed yet.
Give an AI assistant's generated scripts somewhere safe to run before they ever touch your own machine, using a disposable cloud sandbox instead of a permissions system you'd have to build yourself.
Result: A working script or chatbot that answers from real, current search results with sources attached, instead of guessing from the model's training data alone.
Add current, cited information to a small AI project using one search API call instead of assembling your own scraping and parsing stack.
Result: A structured, branded documentation site generated from your actual code and comments, with a draft good enough to edit rather than a blank page to fill.
Get a real documentation site out of a repo nobody ever wrote docs for, using an AI agent that reads your code and drafts the pages instead of you writing them from scratch.
Result: A CI pipeline where a failing test automatically gets matched to the code change that likely caused it, with a suggested fix attached to the pull request for a human to approve or reject, instead of an engineer starting the investigation from zero.
Stop losing time to a red CI run with no clear cause. Correlate failing tests with the code change that broke them automatically, and get a suggested fix a human reviews before it ever merges.