Code and dev AI prompts are structured instructions that get models like Claude or GPT to reason about software the way an engineer would. On Prompt Dock they target real tasks: debugging a stack trace, refactoring a messy function, writing unit tests, explaining unfamiliar code, generating regexes, or reviewing a diff for bugs. Because every prompt a member submits is reviewed by a person before it goes live, you avoid the vague one-liners that produce shallow answers. Free and premium prompts sit side by side, and you can reveal any of them and run it live in the Playground against a snippet before you drop it into your workflow.
21 prompts
Describe a bug and paste the function and the framework. Get a single focused failing test that reproduces it deterministically — the right assertion, the right fixture, no flakiness — so you fix the bug, not your guess about the bug.
A phased mega-prompt that turns an AI into a disciplined infrastructure consultant — Built and battle-tested deploying a self-hosted Nextcloud stack on Proxmox, but generalized for any homelab/self-hosting build.
Paste an error stack trace plus the function it points at. Get a plain-English root cause, the smallest diff that fixes it, why that fix works, and the edge cases to watch — no over-engineering, no try/catch band-aids.
Paste a function that grew too big. Get it split into well-named single-responsibility helpers, a slim top-level function that reads like a table of contents, and a why-bullet for each extraction — guaranteed identical behavior, no new dependencies, no algorithm changes.
Paste your staged git diff and get one properly-typed, properly-scoped Conventional Commit message — imperative summary, optional why-body, and a BREAKING CHANGE footer when needed. For devs who want a readable history without agonizing over wording.
Turn architecture notes into a critical-path and failure-dependency map with targeted read-only validation questions.
Build a reviewable backend migration plan that protects state ownership, handles client and lock compatibility, verifies workspace mapping, and defines when recovery is still safe.
Compare IAM policies with required workflows and observed activity to produce a least-privilege review and a safe validation plan.
Paste a pull request diff and intent. Get a severity-ranked review focused only on correctness bugs, security vulnerabilities, and serious performance issues — with line citations and fixes. No style nits, no invented findings, no speculation about code it can't see.
Map the dependencies, cutover risks, access changes, and rollback limits of moving a workload into a separate AWS account.
Reconstruct a restart incident from sanitized events, status, logs, and probe configuration, then rank hypotheses and propose evidence-led containment options.
Describe what to match and what to skip in plain English. Get the tightest correct pattern for your engine, a part-by-part breakdown table, a table of match / no-match test cases, and a ready-to-paste code snippet — assumptions called out, no cargo-cult patterns.
Translate business recovery goals into a dependency-aware architecture comparison, evidence gaps, and a validation plan.
Stress-review a pipeline on paper for cancellation, credential expiry, partial deployment, retries, stale artifacts, and failed cleanup, then produce a prioritized verification plan.
Create an operator-ready rollback decision plan that checks artifact availability, configuration and schema compatibility, short-lived access, and recovery evidence before any change.
Turn sanitized backend and access details into a prioritized state-security review, with scoped identities, version-aware locking, recovery checks, and explicit evidence gaps.
Audit the boundary between a CI workflow and its cloud role, checking actual subject and audience claims, untrusted triggers, environment controls, and resource-level access.
Paste your branch's commit log and the diff stat. Get a reviewer-friendly PR description — a one-line summary, what changed and why, a test plan, and a risk/rollback note — so reviewers get context instead of a wall of 'misc fixes'.
Turn a workload inventory and team constraints into a practical AWS account plan with access boundaries, ownership, costs, and a staged rollout.
Create an isolated restore exercise that measures recovery time, data freshness, application integrity, and gaps without touching production.
Choose startup, readiness, and liveness signals from measured application behavior, with dependency-aware tradeoffs, timing assumptions, and a safe validation plan.
Prompt Dock's Code & Dev category collects them, and each prompt a member submits is reviewed by a person before it publishes. You can reveal the full text, check the recommended model, and run it in the Playground on a real snippet, so you confirm it handles debugging or refactoring the way you need before relying on it.
Claude and GPT are both strong for code; Claude tends to excel at reading large files and explaining logic, GPT at quick generation. Each Code & Dev prompt lists a recommended model, and running it in the Playground lets you compare outputs on your own code before committing to one.
Yes. The category includes prompts built specifically for debugging error messages, refactoring for readability, adding tests, and doing code review. Reveal the prompt, paste your code into the Playground, and run it live to see the fix or explanation on your actual codebase rather than a generic example.