Coding // Claude

Best Claude Prompts for Coding (2026)

Updated Sep 28, 2026 · 13 prompts

These are 13 Claude prompts for coding in 2026, built around the work that fills a developer's day: debugging, refactoring, reviewing diffs, writing tests, understanding unfamiliar code, and briefing and checking a coding agent. Each prompt gives Claude a clear role and asks for reasoning, so you get answers you can verify instead of confident guesses.

The common thread is making Claude show its work in a form you can check: a failing test before a fix, a ranked list of causes before a patch, a list of claims against a diff before you merge. Code that arrives with its evidence is fast to review. Code that arrives with only a summary is not.

Chat prompts versus agent prompts

The first eight prompts are written for a chat window, where you paste the code and Claude answers. The last five also work when Claude runs as an agent in your repository, through Claude Code or the Agent SDK, where it reads files and runs commands itself. The two need different prompts. In chat, most of the prompt is context, because Claude only knows what you paste. For an agent, most of the prompt is scope and a definition of done, because the agent can find the context but has no way to know which files are off limits or which command proves the job is finished.

The three checks to put in every agent brief

A command that proves success. Name the test, type check, and build commands and require their real exit status in the final report. Without one, an agent will often decide on its own that a task is done.
A rule for failure. Say what happens when a check fails: stop and report. Agents under pressure to finish will otherwise skip a test, loosen an assertion, or catch and swallow an exception so the run ends green.
A scope boundary. List the files and directories the task may change. The diff audit prompt then flags anything outside it, which is where unrelated breakage hides. Structural rules (layer boundaries, forbidden imports between modules) can also fail the build directly: RepoGuard lints AI-generated code against your architecture rules, so the check does not depend on a reviewer noticing.

These three checks are the practical side of a wider problem, runs that end in a success message while the work is wrong or incomplete. The silent agent failures guide covers the failure modes behind it and has prompts for reviewing agent transcripts at scale.

Two more things break long agent runs, and neither shows up in a short chat; both are handled by the agent harness around the model rather than by the model itself. The first is context compaction: once a session fills the window, older turns are summarized, and the constraint you gave at the start is the first thing to go, so put the scope boundary in a project instruction file rather than only in the brief. The second is prompt injection: an agent that reads a README, an issue, or a fetched page while holding shell access will follow instructions written there, so keep network and write permissions narrow when the task involves untrusted repositories; GitSpawn showed coding agents will run code from a repository they were only asked to inspect. When Boris Cherny ran Claude Code against the Claude app's own codebase, contained changes landed and cross-cutting ones quietly fell apart, which is the same lesson from the other side: scope the brief to what one session can hold. If token cost is the constraint rather than quality, routing harnesses like Grist send easy steps to cheaper models and save the expensive one for the hard calls, and small local models such as LFM2.5-2.6B make it realistic to run several agents on one machine for the routine steps. One instruction-following failure is worth knowing in advance: Claude models tend to over-comment code even when asked not to, a known issue that needs a system-prompt rule rather than an inline request, so put your comment policy in the project instruction file.

When the code in question is an analysis rather than an application, the Claude prompts for data analysis pack covers the SQL and the reasoning and the ChatGPT prompts for data analysis pack covers the pandas, the cleaning, and the charts. The Claude prompts for writing pack has the passes that make a design doc or a postmortem readable. To reuse a prompt across a team, turn it into a template with Prompt Builder.

How to use these prompts

01
Pick the prompt that matches your task
Each prompt below targets one job. Choose the one closest to what you need instead of asking for everything at once.
02
Replace every bracketed placeholder
Swap [like this] for your real context: the product, the audience, the document, the constraints. Context is what separates a usable draft from generic output.
03
Run it, then push back
Read the first output critically and ask for a revision: tighter, more specific, a different angle, or with the weak assumptions named.
04
Save the version that worked
Once a prompt produces the output you want, keep it as a reusable template so the next run starts from your best version, not a blank box.

Root-cause debugger

Find why something breaks

You are a senior engineer. Here is a bug: [describe symptom]. Here is the relevant code and the error: [paste]. Walk through the most likely root causes in order of probability, explain your reasoning, and propose the smallest fix that addresses the actual cause, not the symptom.

Refactor with intent

Improve code without changing behavior

Refactor the code below for readability and maintainability while preserving behavior exactly. Explain each change and why it helps. Do not introduce new dependencies. Flag anything that looks like a latent bug. Code: [paste]

Adversarial code review

Catch what you missed

Review this diff as a skeptical senior reviewer. Find correctness bugs, edge cases, security issues, and performance traps. For each, give severity, the exact line, and a concrete fix. Be direct; assume I want the hard feedback. Diff: [paste]

Test generator

Cover the important cases

Write unit tests for the function below using [framework]. Cover the happy path, boundary conditions, and failure modes. List the cases first, then the code. Point out any behavior that's untestable as written. Function: [paste]

Explain unfamiliar code

Onboard onto a codebase fast

Explain what this code does at three levels: one-sentence summary, a short paragraph for a teammate, and a line-by-line walkthrough of the tricky parts. Note any assumptions or side effects. Code: [paste]

Design before you build

Plan a change first

I need to implement [feature] in this codebase: [context]. Propose 2 implementation approaches with trade-offs, name the files likely to change, list edge cases to handle, and recommend one. Don't write code yet.

Regex and SQL helper

Get the tricky one-liner right

Write a [regex|SQL query] that does the following: [describe precisely]. Show the expression, explain each part, and give 3 example inputs with expected outputs, including one that should NOT match.

Commit message + PR description

Document the change well

Given this diff, write a concise commit message (imperative subject under 60 chars + short body) and a PR description with: what changed, why, and how to test. Diff: [paste]

Failing test first

Prove the bug before fixing it

Here is a bug report: [describe symptom and steps]. Here is the relevant code: [paste]. Before proposing any fix, write the smallest test that fails because of this bug and explain why it fails. Only after I confirm the test fails for the right reason, propose the fix and show that the same test now passes. Do not change the test to make it pass.

Diagnose a flaky test

Stop re-running CI and hoping

This test passes locally and fails in CI roughly [1 in N] runs: [paste test and the code under test, plus two failing logs if you have them]. List the likely causes in order: shared state between tests, test order dependence, time and time zone assumptions, unawaited async work, network or filesystem dependence, and randomness without a seed. For each, point to the exact line that makes it plausible here and the change that would make the test deterministic.

Brief an agent run before it starts

Give Claude Code a task it can finish correctly

Turn this task into a brief for a coding agent: [describe task]. The brief must state the goal in one sentence, the files it may change and the ones it must not touch, the commands that prove success (tests, type check, lint, build), what to do if a command fails (stop and report, never skip or delete the check), and what the final report must contain: every file changed, every command run with its real exit status, and anything left undone.

Audit an agent's diff against its summary

Catch the work an agent claims but did not do

A coding agent produced this summary: [paste summary] and this diff: [paste diff], plus this command log: [paste]. Check every claim in the summary against the diff and the log. List claims that are supported, claims with no evidence, and changes in the diff the summary does not mention. Flag any test that was deleted, skipped, or loosened, any error in the log that was followed by a success message, and any file changed outside the stated scope.

Plan a risky migration

Change a schema or dependency without an outage

I need to [migrate X to Y, e.g. rename a column used by three services / upgrade a major framework version]. Context: [paste relevant code, versions, deploy setup]. Propose a sequence of small, separately deployable steps where each step is safe to roll back, say what must be true before moving to the next, and name the one step most likely to break production and how I would detect it within minutes.

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Questions

Why use Claude for coding specifically?

Claude is strong at long-context reasoning, careful refactors, and explaining trade-offs, which makes it well suited to reviewing diffs, planning changes, and working across larger files.

How do I get more reliable code from Claude?

Give it the surrounding code and constraints, ask it to plan before writing, and request reasoning for non-obvious choices. For risky changes, ask it to review its own output adversarially.

Should I paste my whole repo?

Paste the relevant files plus enough context to understand interfaces. More signal beats more volume. For repeatable workflows, save the framing as a template in Prompt Builder.

Do these prompts work in Claude Code?

Yes, and the agent-run brief and the diff audit were written for it. In Claude Code the difference is that Claude can read the repo and run commands itself, so you paste less code and spend more words on scope and on what counts as done. Put the parts you repeat on every task (test commands, files never to touch, style rules) in the project's CLAUDE.md so each prompt only has to describe the task.

How do I stop Claude from saying tests pass when they do not?

Ask for evidence rather than conclusions. Require the exact command and its exit status in the final report, tell it that a failing check means stop and report rather than work around, and forbid editing or skipping tests to get a green run. Then check the report against the log with the diff audit prompt. Confident, unverified success reports are the most common way agent runs fail without anyone noticing.

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