Whether building a small application or managing a large-scale software project, development workflows can quickly become complex and time-consuming. Software skills provide AI agents with specialized capabilities to write code, analyze projects, debug issues, and support technical workflows more efficiently. By using these skills, AI agents can handle repetitive development tasks, improve consistency, and assist with more reliable software creation. In this article, we explore software development skills that enhance AI agents' capabilities across modern development workflows.
What are AI software skills?
AI software skills are task-specific configurations that improve how AI agents assist with software engineering workflows. They guide AI agents through activities such as coding, debugging, testing, security reviews, API development, and deployment tasks. These skills help standardize development processes, reduce repetitive work, and support developers in managing complex technical projects.
Ready-to-use software skills in Kimi
Building software requires handling tasks like coding, testing, deployment, and security checks. Kimi's AI skills provide ready-to-use solutions that help developers analyze code, automate workflows, and improve efficiency across the software development lifecycle. Below are software development skills available in Kimi.
| Skill name | Description |
|---|---|
| code-mentor | AI programming tutor offering interactive lessons, code reviews, debugging help, algorithm practice, and project guidance for Python and JavaScript. |
| code-to-diagram | Analyzes codebases and automatically generates architecture diagrams, flowcharts, and module maps using AST parsing for Python, JavaScript, TypeScript, Go, and Java. |
| code-vuln-audit | Scans code for security issues, including dependency vulnerabilities, secret leaks, and OWASP anti-patterns such as SQL injection, XSS, and command injection. |
| conventional-commit-gen | Analyzes git diffs to generate commit messages following the Conventional Commits specification, with automatic scope detection and commit type inference. |
| dev-guide-generator | Generates complete technical tutorials covering prerequisites, environment setup, core steps, troubleshooting, and a final cheat sheet. |
| gitlab-cli-skills | Guides users through GitLab CLI operations with command references and workflow examples for merge requests, issues, CI/CD pipelines, repositories, and authentication. |
| http-load-profiler | Runs stepped HTTP load tests that ramp up concurrency to measure p50, p90, and p99 latency, detect performance limits, and recommend optimal concurrency. |
| incident-review-guide | Produces blameless postmortems and incident reviews based on SRE best practices, with structured timelines, root cause analysis, action items, and lessons learned. |
| kubectl | Executes kubectl commands to manage Kubernetes clusters, including querying pods and deployments, deploying applications, debugging containers, and monitoring cluster health. |
| log-error-digest | Analyzes log files to troubleshoot errors, identify peak error periods, and produce error clustering, frequency statistics, and time distribution reports across JSON, syslog, and Nginx formats. |
| pipeline-blueprint | Provides CI/CD best practices and pipeline templates for GitHub Actions and GitLab CI, recommending configurations based on project type such as frontend, backend, full-stack, or monorepo. |
| repo-audit | Performs deep analysis of Git history to identify frequently changed hotspot files, analyze code ownership by contributor, and scan for leaked secrets. |
| route-to-openapi | Generates RESTful API documentation in OpenAPI 3.0 or Swagger format by scanning route definitions in Flask, FastAPI, Express, Gin, and other frameworks. |
| secure-code-review | Systematically reviews code for SQL injection, XSS, SSRF, broken access control, cryptographic failures, and other OWASP Top 10 vulnerabilities, with remediation guidance. |
| api-shape-explorer | Generates multiple radically different interface designs for a software module using parallel sub-agents, helping teams explore and compare API design options. |
| cross-examine | Stress tests a software plan or design by interviewing the user about each branch of the decision tree until reaching a shared understanding. |
| deep-module-refactor | Explores a codebase to find architectural improvement opportunities, focusing on making the code more testable by deepening shallow modules. |
| test-driven-dev | Guides test-driven development with the red-green refactor loop for building features or fixing bugs with integration tests and test-first development. |
| localization-toolkit | Sets up, audits, and enforces internationalization and localization in React and TypeScript codebases, covering framework configuration, string extraction, locale coverage, and key parity validation. |
| terraform-deploy-pitfalls | Covers operational traps for Terraform provisioners, multi-environment isolation, and zero deployment reliability, including timing races, SSH conflicts, DNS duplication, and bootstrap gaps. |
| test-suite-architect | Establishes comprehensive QA testing processes for software projects, covering test strategies, test cases following Google Testing Standards, test plan execution, and bug tracking. |
How to start using built-in software skills in Kimi?
Kimi allows you to quickly apply built-in skills to different development tasks. Choose a skill, enter your requirements, and let Kimi use AI to assist with coding, analysis, debugging, documentation, and other software workflows. Follow these steps to get started.
Step 1: Input a skill command
Open Kimi and type the command of the software skill you want to use. For example, enter /secure-code-review for security checks.
Step 2: Start your task
After selecting a skill, explain your requirements in the chat. For example, with /secure-code-review, you can ask Kimi to analyze your code for security risks and suggest fixes.
Example prompt:
Kimi's AI processes your instructions, examines the provided code, and generates a detailed security review with possible issues and improvement suggestions using the selected software skill.
Step 3: Review your output
Check the generated results, refine the response if needed, and use the completed output in your development workflow.
Add additional open-source software skills to your toolkit
Software skills can cover a wide range of development tasks, but you can expand your toolkit with additional skills for specialized workflows. These AI skills help with areas like product planning, coding assistance, testing, automation, and technical documentation, making development processes more efficient and structured. Here are AI software skills that can further enhance your development workflow.
| skill-name | Description | URL |
|---|---|---|
| agent-postmortem-skill | Verification skill that forces coding agents to prove work with evidence before claiming completion. Enforces intent snapshot, evidence collection (git status, diff, command outputs), verification check, and postmortem output with verdict and risks. | https://github.com/plus8bit/agent-postmortem-skill |
| alibaba-java-coding-guidelines-skill | Agent skill for applying Alibaba Java Coding Guidelines to Java, Spring, MyBatis, Maven, MySQL, SQL, logging, exception handling, and security validation. Includes multi-language validation scripts (Python, Node.js, Go). | https://github.com/ns3154/alibaba-java-coding-guidelines-skill |
| litprog-skill | Literate programming skills for agent harnesses. Transforms codebases into literate programs with .lit.md files weaving prose, Mermaid diagrams, LaTeX math, and syntax-highlighted code. Includes tangle/untangle scripts and reverse-sync engine. | https://github.com/tlehman/litprog-skill |
| ios-agent-skill | Production-ready iOS SwiftUI guidance for AI coding agents. Covers SwiftUI best practices, UI patterns, and iOS development workflows with cross-platform support for 15+ AI platforms. | https://github.com/Nagarjuna2997/ios-agent-skill |
| tsbs-benchmark-agent-skill | Runs end-to-end TSBS (Time Series Benchmark Suite) benchmarks against QuestDB in Docker. Automates prerequisites, data generation, loading, query execution, and cleanup for performance testing. | https://github.com/questdb/tsbs-benchmark-agent-skill |
| devops-sre-skills | Agent-ready SKILL.md library for DevOps and SRE work with safe-by-default runbooks. Covers Kubernetes, AWS, Terraform, ArgoCD, and incident response with read-only procedures and verification guidance. | https://github.com/bregman-arie/devops-sre-skills |
| aws-devops-agent-skills | Custom skills for AWS DevOps Agent, including EKS resilience checker, Well-Architected Framework review, and China Region multi-account DevOps scenarios. Includes 10 skills for routing, incident triage, and cross-account management. | https://github.com/aws-samples/sample-skills-for-AWS-Devops-agent |
| software-architecture-skills | Platform-neutral software architecture skill pack for exploring architectural options, documenting tradeoffs, defining boundaries, and writing reusable architecture artifacts. Includes ADR writer, architecture option generator, risk assessor, availability strategy reviewer, component boundary reviewer, and service decomposition advisor. | https://github.com/45ck/software-architecture-skills |
| swift-architecture-skill | Skill for Swift architecture design and implementation patterns covering MVP, MVVM, MVI, TCA, Clean Architecture, VIPER, Coordinator, and Reactive. Each architecture includes a dedicated playbook with overview, patterns, anti-pattern fixes, testing strategy, and PR checklist. | https://github.com/efremidze/swift-architecture-skill |
| superstar-engineer-skill | Pushes coding agents to operate like a 10x Principal/Staff-Level Software Engineer. Simplifies aggressively, reasons from constraints, surfaces tradeoffs, plans for failure, and delivers the simplest defensible solution. Covers architecture decisions, system design, production reliability, performance bottlenecks, and root-cause debugging. | https://github.com/inevolin/superstar-engineer-skill |
How to use open-source AI skills in Kimi?
Open-source AI skills allow you to add new capabilities to Kimi by installing skills from public repositories. You can provide a skill URL, let Kimi configure it automatically, and then use the added functionality for different workflows such as development and automation. Follow these steps to use these open-source skills in Kimi:
Step 1: Enter a prompt
Open Kimi and provide a prompt that includes the GitHub URL of the open-source skill you want to add.
Example prompt:
Step 2: Let AI install the skill automatically
Kimi processes the provided repository link, downloads the required files, and configures the skill so it can be used within your workspace.
Step 3: Use the skill
After installation, enable the skill in Kimi and start using it by entering relevant instructions. You can ask the skill to perform tasks based on its purpose, such as reviewing code, generating documentation, analyzing data, or improving workflows.
Build advanced custom software skills for your workflows
Kimi lets you create personalized skills by transforming your own resources into reusable AI tools. You can add coding guidelines, project documents, technical references, or workflow instructions to build skills that match your development needs. Follow these steps to create your custom software skills in Kimi.
Step 1: Access the document to the skills tool
Open Kimi Skills and choose "Document to skills" to start creating your custom software development skill.
Step 2: Upload the files
Upload your software-related materials, such as coding standards, API documentation, project requirements, testing guidelines, or technical notes. Kimi analyzes these files and converts them into a structured skill that can assist with similar development tasks.
Step 3: Create and use your skills
After processing, your custom software skill is ready to use for tasks like code reviews, documentation generation, debugging support, or workflow automation.
You can also edit the skill anytime or download it as a file to reuse in other projects.
Advanced tips to use software development skills
Using software development skills effectively requires more than applying predefined workflows. Advanced usage involves customizing skills, combining multiple workflows, and continuously improving how AI agents support development tasks.
Customize skills with project-specific standards
Adapt software development skills to match your project requirements, including coding conventions, architecture guidelines, testing rules, and documentation formats. Customized skills help AI agents produce outputs that better align with your team's development practices.
Combine multiple skills for complex workflows
Software development often involves multiple connected tasks, such as planning, coding, testing, debugging, and documentation. Combining different skills allows AI agents to handle broader workflows instead of completing isolated tasks.
Provide detailed context before running skills
Give AI agents relevant information, such as project goals, codebase structure, technical requirements, and expected outputs. Clear context helps skills generate more accurate results and reduces unnecessary revisions.
Refine skills based on development feedback
Review AI-generated results and identify areas for improvement. Update skill instructions, requirements, or workflows based on recurring issues to make AI-assisted development more reliable over time.
Create reusable skills for recurring engineering tasks
Turn repetitive development processes into reusable skills, such as code review checklists, testing workflows, API documentation generation, or bug analysis processes. Reusable skills improve consistency and save time across projects.
Conclusion
AI is changing how developers approach software creation by making technical tasks faster and more manageable. With the right software skills, teams can improve workflows, solve problems efficiently, and adapt to changing development needs. Exploring these AI capabilities can help developers build better solutions with less effort. Try Kimi to discover, create, and apply AI-powered skills for your software projects. Start using Kimi today to enhance your development workflow.