backend-code-review

langgenius/dify · updated May 23, 2026

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$npx skills add https://github.com/langgenius/dify --skill backend-code-review
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summary

Use this skill whenever the user asks to review, analyze, or improve backend code (e.g., .py) under the api/ directory. Supports the following review modes:

skill.md

Backend Code Review

When to use this skill

Use this skill whenever the user asks to review, analyze, or improve backend code (e.g., .py) under the api/ directory. Supports the following review modes:

  • Pending-change review: when the user asks to review current changes (inspect staged/working-tree files slated for commit to get the changes).
  • Code snippets review: when the user pastes code snippets (e.g., a function/class/module excerpt) into the chat and asks for a review.
  • File-focused review: when the user points to specific files and asks for a review of those files (one file or a small, explicit set of files, e.g., api/..., api/app.py).

Do NOT use this skill when:

  • The request is about frontend code or UI (e.g., .tsx, .ts, .js, web/).
  • The user is not asking for a review/analysis/improvement of backend code.
  • The scope is not under api/ (unless the user explicitly asks to review backend-related changes outside api/).

How to use this skill

Follow these steps when using this skill:

  1. Identify the review mode (pending-change vs snippet vs file-focused) based on the user’s input. Keep the scope tight: review only what the user provided or explicitly referenced.
  2. Follow the rules defined in Checklist to perform the review. If no Checklist rule matches, apply General Review Rules as a fallback to perform the best-effort review.
  3. Compose the final output strictly follow the Required Output Format.

Notes when using this skill:

  • Always include actionable fixes or suggestions (including possible code snippets).
  • Use best-effort File:Line references when a file path and line numbers are available; otherwise, use the most specific identifier you can.

Checklist

  • db schema design: if the review scope includes code/files under api/models/ or api/migrations/, follow references/db-schema-rule.md to perform the review
  • architecture: if the review scope involves controller/service/core-domain/libs/model layering, dependency direction, or moving responsibilities across modules, follow references/architecture-rule.md to perform the review
  • repositories abstraction: if the review scope contains table/model operations (e.g., select(...), session.execute(...), joins, CRUD) and is not under api/repositories, api/core/repositories, or api/extensions/*/repositories/, follow references/repositories-rule.md to perform the review
  • sqlalchemy patterns: if the review scope involves SQLAlchemy session/query usage, db transaction/crud usage, or raw SQL usage, follow references/sqlalchemy-rule.md to perform the review

General Review Rules

1. Security Review

Check for:

  • SQL injection vulnerabilities
  • Server-Side Request Forgery (SSRF)
  • Command injection
  • Insecure deserialization
  • Hardcoded secrets/credentials
  • Improper authentication/authorization
  • Insecure direct object references

2. Performance Review

Check for:

  • N+1 queries
  • Missing database indexes
  • Memory leaks
  • Blocking operations in async code
  • Missing caching opportunities

3. Code Quality Review

Check for:

  • Code forward compatibility
  • Code duplication (DRY violations)
  • Functions doing too much (SRP violations)
  • Deep nesting / complex conditionals
  • Magic numbers/strings
  • Poor naming
  • Missing error handling
  • Incomplete type coverage

4. Testing Review

Check for:

  • Missing test coverage for new code
  • Tests that don't test behavior
  • Flaky test patterns
  • Missing edge cases

Required Output Format

When this skill invoked, the response must exactly follow one of the two templates:

Template A (any findings)

# Code Review Summary

Found <X> critical issues need to be fixed:

## 🔴 Critical (Must Fix)

### 1. <brief description of the issue>

FilePath: <path> line <line>
<relevant code snippet or pointer>

#### Explanation

<detailed explanation and references of the issue>

#### Suggested Fix

1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)

---
... (repeat for each critical issue) ...

Found <Y> suggestions for improvement:

## 🟡 Suggestions (Should Consider)

### 1. <brief description of the suggestion>

FilePath: <path> line <line>
<relevant code snippet or pointer>

#### Explanation

<detailed explanation and references of the suggestion>

#### Suggested Fix

1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)

---
... (repeat for each suggestion) ...

Found <Z> optional nits:

## 🟢 Nits (Optional)
### 1. <brief description of the nit>

FilePath: <path> line <line>
<relevant code snippet or pointer>

#### Explanation

<explanation and references of the optional nit>

#### Suggested Fix

- <minor suggestions>

---
... (repeat for each nits) ...

## ✅ What's Good

- <Positive feedback on good patterns>
  • If there are no critical issues or suggestions or option nits or good points, just omit that section.
  • If the issue number is more than 10, summarize as "Found 10+ critical issues/suggestions/optional nits" and only output the first 10 items.
  • Don't compress the blank lines between sections; keep them as-is for readability.
  • If there is any issue requires code changes, append a brief follow-up question to ask whether the user wants to apply the fix(es) after the structured output. For example: "Would you like me to use the Suggested fix(es) to address these issues?"

Template B (no issues)

## Code Review Summary
✅ No issues found.
how to use backend-code-review

How to use backend-code-review on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add backend-code-review
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/langgenius/dify --skill backend-code-review

The skills CLI fetches backend-code-review from GitHub repository langgenius/dify and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/backend-code-review

Reload or restart Cursor to activate backend-code-review. Access the skill through slash commands (e.g., /backend-code-review) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use When

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid When

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.764 reviews
  • Noor Malhotra· Dec 24, 2024

    Keeps context tight: backend-code-review is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aisha Gill· Dec 20, 2024

    backend-code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aisha Ghosh· Dec 16, 2024

    backend-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Diego Flores· Dec 16, 2024

    backend-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ganesh Mohane· Dec 4, 2024

    Useful defaults in backend-code-review — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Nov 23, 2024

    backend-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Anaya Yang· Nov 23, 2024

    backend-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Diego Singh· Nov 15, 2024

    Registry listing for backend-code-review matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Aarav Bansal· Nov 15, 2024

    I recommend backend-code-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Zaid Abbas· Nov 11, 2024

    backend-code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

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