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PR Review Standards
Systematic code reviews with severity-tagged findings and concrete failure examples.
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PR Review Standards
Example session with this skill installed
Review this Python diff for a new user registration endpoint. It handles password hashing and saves to the DB. Check for security and logic errors.
- Read your context and instructions
- Compiled the pr review standards
I have reviewed the registration endpoint diff. The review focused on security and database integrity.
Pull Request Review: User Registration
Verdict: ❌ Request Changes
🔴 Blocking
- Security: Plaintext Password Leak
TheUserobject is being logged before the hashing function is called. This writes plaintext credentials to the application logs.- Example: If a user registers with
password="P@ssword123", the log at line 42 will contain the raw string.
- Example: If a user registers with
- Correctness: Missing Transaction
The user profile and the initial account balance are saved in two separatedb.session.add()calls without a transaction wrapper. If the second save fails, the system is left in an inconsistent state.
🟡 Should-fix
- Performance: Redundant DB Lookup
Line 15 checksUser.query.filter_by(email=email).first()but the subsequent save will naturally trigger a unique constraint error. You can simplify this to a try/except block to save a round-trip to the database.
🟢 Nits
- Readability: The variable name
uon line 22 should benew_userfor clarity.
✨ What's good
Security: Good use of Argon2 for hashing; the salt parameters are correctly configured for current hardware standards.
- Logic: The email validation regex is comprehensive and handles sub-addressing (plus-addressing) correctly.
Next steps
- Wrap the database operations in a
with db.session.begin():block. - Remove the
logger.info(user_data)call on line 42. - Rename the user instance variable to improve maintainability.
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What you get
About this skill
The problem
Pull reviews often oscillate between shallow "looks good to me" approvals and unprioritized walls of nitpicks that frustrate developers. Skimming code leads to missed edge cases, security vulnerabilities, and performance bottlenecks that become technical debt.
What it does
- Evaluates code against a strict, senior-level checklist covering correctness, security, performance, and maintainability.
- Classifies every finding by severity: Blocking, Should-fix, or Nit.
- Provides concrete examples or failure conditions for every non-trivial issue found.
- Identifies specific positive patterns to maintain team morale and trust.
- Infers intent from diffs when PR descriptions are missing to ensure context-aware feedback.
Why this beats prompting it yourself
General-purpose LLMs tend to be overly agreeable or focus on superficial style issues. This skill enforces a disciplined, systematic review process that deliberately checks for N+1 queries, race conditions, and security risks that simple prompts often overlook.
Use cases
- Pre-submission self-review to catch bugs before they reach a human reviewer.
- Second-opinion security audit for sensitive API or database changes.
- Automated first-pass review to filter out obvious logic errors and maintainability issues.
Known limitations
Does not execute code or run test suites directly. Reviews large diffs in chunks and will explicitly state which files were skipped if context limits are reached.
How to install
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