👁️ Code Review

Convert Markdown text to DOCX, PPTX, XLSX, PDF, PNG, HTML, IPYNB, MD, CSV, JSON, JSONL, XML files, and extract code blocks in Markdown to Python, Bash,JS and etc files.

Prevent Terraform/OpenTofu hallucinations by diagnosing and fixing failure modes: identity churn, secret exposure, blast-radius mistakes, CI drift, and compliance gate gaps. Use when generating, reviewing, refactoring, or migrating IaC and when building delivery/testing pipelines.

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Build stateful AI agents and agentic workflows with LangGraph in Python. Covers tool-using agents with LLM-tool loops, branching workflows, conversation memory, human-in-the-loop oversight, and production monitoring. Use when - (1) building agents that use tools and loop until task complete, (2) creating multi-step workflows with conditional branches, (3) adding persistence/memory across turns with checkpointers, (4) implementing human approval with interrupt(), (5) debugging via time-travel or LangSmith. Covers StateGraph, nodes, edges, add_conditional_edges, MessagesState, thread_id, Command objects, and ToolMessage handling. Examples include chatbots, calculator agents, and structured workflows.

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Build type-safe LLM applications with DSPy.rb - Ruby's programmatic prompt framework with signatures, modules, agents, and optimization

Build and extend brownfield features in an existing codebase. Use when a request requires understanding module dependencies, researching the latest official docs for current tech stacks/APIs/external dependencies, and implementing tested changes in a mature project. For multi-module changes, produce a PRD and obtain explicit user approval before coding.

Read code to identify edge cases, write tests that cover those edge cases, and fix the implementation if tests expose failures. Use when a user asks to add edge-case tests, harden behavior, or validate that existing code handles unusual inputs or error paths (e.g., empty data, nulls, boundaries, invalid formats, timeouts, retries).

Multi-model agent implementation workflow for software development. Orchestrates research, evaluation, design baseline, implementation, RCA, structured decomposition, constraint discovery, model selection, and agent-driven Stage 3 codemap exploration across external AI models (GPT, GLM, Claude). Use when implementing features through a structured multi-phase pipeline with worktrees, dynamic scheduling, and SQLite-backed agent coordination.

Auto-split mixed changes into logical commit batches with validated Conventional Commit messages. MUST BE USED for ANY git add, git commit, or git push operation.

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Analyze, plan, review, and optimize any codebase across 4 modes: Discovery (understand architecture and risks), Review (validate changes and detect breaking changes), Optimization (find bottlenecks and vulnerabilities), Implementation Planning (generate step-by-step guidance). Works with React, Django, Rails, Go, Rust, and 30+ frameworks. Use when analyzing codebase structure, assessing feature safety, finding security issues, planning implementations, or discovering performance problems.

Use before you execute and edit any codes related to React Framework and developing ecosystem.

Use when addressing PR review feedback, re-requesting reviews, handling GitHub review bot re-reviews (Codex, Copilot), or managing GitHub notifications. Do not use for creating new PRs or general development.

Use when reviewing an agent's plan, work output, or completion claim on behalf of the user. Evaluates against established rules, known error patterns, and quality standards. Do not use for direct implementation work.

Use when setting up or configuring code quality tools (formatters, linters, type checkers, dependency scanners) for a repository. Also use when adding visual accessibility automation or security baseline scanning. Do not use for general coding or when tools are already configured.

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Perform security audits on code changes, diffs, or branches to find high-confidence exploitable vulnerabilities. Use when asked to "audit security", "review for vulnerabilities", "security scan", "check for security issues", "audit this PR", "review these changes for security", or "find vulnerabilities in diff". Distinct from security-review (which provides secure coding patterns/checklists) — this skill actively audits code changes using a structured methodology with false positive filtering. Includes Python scripts for GitHub Action CI integration and PR evaluation.

Use when generating Python code for web applications, APIs, or handling user input - prevents OWASP Top 10 vulnerabilities in Django, Flask, FastAPI