AI Prompts for Automated Code Review Loop
Browse the best AI prompts for Automated Code Review Loop. All tested, copy-paste ready, and free to use.
The best copy-paste AI prompts to complete your Automated Code Review Loop from start to finish.
AI Prompts for Automated Code Review Loop
Browse the best AI prompts for Automated Code Review Loop. All tested, copy-paste ready, and free to use.
The best free AI prompts for Automated Code Review Loop, organized by stage. This guide covers design the review loop, write the review prompts, and more, with copy-paste ready prompts for every skill level. Pick your stage, copy a prompt, and get results right away.
Stage 1
Design the Review Loop
A review loop that checks everything checks nothing. You need to decide what the agent will and will not evaluate, what it will block versus suggest, and what format its output should take.
Define what the review agent checks
I want to build an automated code review loop for [YOUR_PROJECT]. Help me define what the review agent should check. Based on our stack ([TECH STACK]) and team size ([TEAM SIZE]), suggest: the top 5 things the agent should always check (e.g. missing tests, lint errors, security issues), the 3 things it should flag as suggestions but not block on, and the 2 things it should explicitly ignore because human judgment is required. Write this as the review scope section of the agent's SKILL.md.
Write the PR review agent definition
Write a Claude Code sub-agent definition for automated PR review in [YOUR_PROJECT]. The agent should: accept the PR diff and the list of changed files as input, run the checks defined in SKILL.md, output a structured review in a specific format, and never suggest changes to files it was not given. Write the full .claude/agents/pr-reviewer.yaml with detailed instructions for each check type.
Design the review output format
Design the structured review output format for the code review agent in [YOUR_PROJECT]. The format should include: an overall verdict (APPROVE, REQUEST_CHANGES, or COMMENT), a severity for each finding (BLOCKING, SUGGESTION, or INFO), the file and line reference for each finding, a plain-language description of the issue, and when relevant, a suggested fix. Write the format specification in JSON schema and write a sample review output for a fictional PR.
Set the review agent's boundaries
Write the boundaries section of the review agent's instructions for [YOUR_PROJECT]. This section defines what the agent must not do: it must not approve a PR it has not fully read, it must not suggest architectural changes beyond the scope of the PR, it must not flag style issues that are not in the linter config, and it must not comment on the same issue twice. Write these as explicit prohibitions in the agent definition.
Write the escalation protocol
Write the escalation protocol for the automated review loop in [YOUR_PROJECT]. When should the review agent escalate to a human reviewer instead of posting a verdict? Define the criteria: PR size threshold (lines changed), detection of security-sensitive file changes, changes to the CI/CD config, and any file in a protected directory list. Write the escalation message format and the GitHub label the agent should add when escalating.
Stage 2
Write the Review Prompts
The quality of the review depends entirely on the quality of the prompt. A vague prompt produces generic comments. A specific prompt produces actionable findings. These prompts help you write review instructions that are detailed enough to be useful and constrained enough to be trustworthy.
Write the test coverage check prompt
Write the test coverage check prompt for the code review agent in [YOUR_PROJECT]. The agent should: identify all new or modified functions in the diff, check whether each has a corresponding test case, flag any function over [LINE THRESHOLD] lines that has no test, and post a specific comment on the PR for each untested function with a suggested test case. Write the prompt and the comment template it should use.
Write the security review prompt
Write the security review prompt for the code review agent in [YOUR_PROJECT]. The agent should check for: SQL injection (parameterized queries only), XSS vulnerabilities in React components, exposed credentials or API keys, unvalidated user input used in file paths or shell commands, and missing authentication checks on new API routes. For each finding, the comment must cite the exact line and explain why it is a risk. Write the full security check prompt.
Write the style and conventions check
Write the style conventions check prompt for [YOUR_PROJECT]. The agent should only flag issues that violate the rules in .eslintrc and the patterns described in AGENTS.md. It must not flag preferences or opinions that are not in those files. For each violation, the comment must reference the specific rule name and a one-line explanation. Write the prompt and the comment format.
Write the PR summary prompt
Write the prompt for the agent to generate the opening summary comment on each PR in [YOUR_PROJECT]. The summary should: describe what the PR does in two sentences, list the files changed grouped by type (feature code, tests, config), state the overall verdict with one sentence of justification, and list any blocking issues as a numbered list. This summary should appear as the first comment the agent posts, before any inline comments.
Write the approval prompt
Write the approval prompt for the code review agent in [YOUR_PROJECT]. The agent should only post an APPROVE verdict if all of the following are true: no blocking issues were found, at least one test was added or modified in the PR, the PR description is not empty, and the PR title follows the conventional commits format. Write the approval prompt and the approval comment template that explains clearly what was checked.
Frequently asked questions
Can an AI agent reliably review pull requests?+
Yes, for the rule-based parts: missing tests, lint errors, undeclared dependencies, obvious security issues, and style violations that contradict the linter config. AI code review is reliable for these because the criteria are explicit and checkable. It is not reliable for architectural judgment calls, product direction questions, or subtle logic that requires full system context. Design the review agent to do the former and escalate the latter to a human.
What should an automated code review agent check first?+
Test coverage is the highest-leverage first check. If the PR adds or modifies functions without corresponding tests, flag it before anything else. A PR with missing tests fails regardless of how well the code looks. After tests, check for lint errors, then security issues in the changed files only. Checking everything at equal priority means the most important issues get buried in noise.
How do I prevent the review agent from being too strict or too lenient?+
Write explicit blocking criteria in the agent definition and treat everything else as a suggestion, never a block. A finding is blocking only if it matches a rule in .eslintrc, a security requirement in SKILL.md, or a missing test for a function over a configurable line threshold. Everything the agent flags that does not meet one of those criteria is a COMMENT, not a REQUEST_CHANGES. The review scope prompt in Stage 1 generates this boundary definition for your project.
Should the review agent approve PRs automatically?+
Only if your team has agreed to it and the approval conditions are very tight. The minimum bar: no blocking issues, at least one test added or changed, PR description not empty, and PR title in conventional commits format. Auto-approval works well for small dependency updates and documentation changes. For feature PRs, a human approval step after the agent review is still the safer default.
How do I connect the code review loop to GitHub?+
Build or configure an MCP connector that exposes get_pr_diff, post_review, and add_label as tools. The review agent calls get_pr_diff to read the changes, evaluates them against its criteria, then calls post_review with its verdict and inline comments. The MCP server setup prompts in this collection cover the GitHub connector implementation in detail.
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