agent_skills

Generates structured, career-focused modular curriculums for software engineering students. Use when designing new educational modules or tailoring learning journeys to specific career roles.

Generates end-to-end student projects that reinforce specific modular learning objectives. Use to create professional-grade portfolio pieces and assessment tasks for engineering mentees.

Evaluates student code submissions based on conceptual mastery rather than just correctness. Use to provide high-quality educational feedback on architectural patterns and programming logic.

Detects and redacts Personally Identifiable Information (PII) like emails, phone numbers, and credit cards. Use when cleaning logs, datasets, or communications to comply with GDPR/CCPA privacy standards.

Optimize pyproject.toml and resolve complex dependency trees using modern tools like Poetry or uv. Use to modernize Python project management.

Audits agent skill instructions and system prompts for vulnerabilities to prompt hijacking and indirect injection. Use when designing new agent skills or before deploying agents to public environments where users provide untrusted input.

Analyze and optimize pytest suites to improve speed, identify flaky tests, and increase coverage. Use to maintain high-quality, fast-running test pipelines.

Review asynchronous Python code to identify race conditions, deadlocks, and inefficient patterns. Use when working with asyncio, aiohttp, or FastAPI.

Generate and validate environment-based configuration for Python apps using Pydantic or Dynaconf. Use to ensure secure and valid runtime settings.

Design ETL workflows with data validation using tools like Pandas, Dask, or PySpark. Use when building robust data processing systems in Python.

Analyze and resolve Python package dependency conflicts. Use when pip install fails due to version mismatches or circular dependencies.

Enforce Pythonic standards using Black, Isort, and Flake8. Use to ensure consistency across large Python codebases and team environments.

Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.

Plan and execute upgrades for Python libraries, handling breaking changes. Use when performing major version bumps for frameworks like Django or FastAPI.

Identify CPU and memory bottlenecks in Python code using cProfile or memory_profiler. Use to optimize mission-critical Python services.

Design comprehensive Python test suites including unit, integration, and E2E tests. Use when establishing testing patterns for new or existing Python applications.

Automatically add or improve type annotations in legacy Python code. Use to improve code readability, IDE support, and catch type errors early.

Setup and validate Python virtual environments (venv, virtualenv, conda). Use to ensure isolated dependencies and correct Python versions for projects.

Identifies code smells and provides step-by-step refactoring recipes. Use when improving legacy code maintainability or teaching students how to apply Clean Code and SOLID principles.

Scans source code, configuration files, and git history for hardcoded credentials, API keys, and tokens. Use when auditing repositories for security leaks or ensuring sensitive data is not committed to version control.

Identifies and manages execution dependencies between agent skills by analyzing their inputs and outputs. Use when building multi-step agent workflows to ensure skills are executed in the correct order and that all required data is available.

Validates agent skill definitions against agentskills.io and AGENTS.md rules. Use when creating or modifying skills to ensure they are machine-readable and documentation-complete.

Generates a heat-map and metrics report of a repository based on code complexity, lack of tests, and 'TODO/FIXME' density. Use when you need to identify high-risk areas for refactoring or when planning technical debt reduction sprints.