Orchestration Workflow
Choose and execute orchestration commands (/solo for straightforward tasks, /spec for planning, /conduct for complex features with SPEC.md). Covers decision tree, sub-agents, validation standards, and best practices. Use when planning complex tasks or understanding orchestration patterns.
performance-profiling
Python performance profiling with cProfile, line_profiler, memory_profiler, and py-spy. Covers profiling workflows, interpreting results, finding bottlenecks, memory leak detection, and optimization strategies. Use when code is slow, debugging performance issues, optimizing for production, or investigating memory usage.
pr-review-common-patterns
Common patterns in PR reviews including false positives, security vulnerabilities, N+1 queries, breaking changes, and edge cases. Use when analyzing code, verifying findings, or understanding typical issues.
pr-review-evidence-formats
Defines what counts as valid evidence in PR reviews including code snippets, execution traces, exploitation scenarios, and test results. Use when validating findings, writing review reports, or verifying claims.
pr-review-standards
Code quality standards for PR review agents including try/except rules, logging patterns, type hints, and verification requirements. Use when reviewing code, spawning PR review agents, or validating code quality.
python-linting
Python linting and type checking using ruff (formatting + linting) and pyright (type checking). Covers common errors, configuration, fixing violations, and when to use noqa. Use when fixing linting errors, configuring ruff/pyright, or understanding Python code quality tools.
python-packaging
Python package management with poetry, pip, and uv including dependency resolution, lock files, version pinning, security audits (pip-audit, safety), virtual environments, and dependency graphs. Use when managing dependencies, resolving version conflicts, setting up new projects, or investigating security vulnerabilities in packages.
Python Style Standards
Python coding standards including line length (80 chars), naming conventions (snake_case, PascalCase), type hints, docstrings, exception handling, and logging patterns. Use when writing new Python code or reviewing code quality.
skill-authoring
Best practices for creating and updating Claude Code skills including YAML frontmatter structure, description patterns for discoverability, content organization, progressive disclosure, and testing strategies. Use when creating new skills or updating existing skills to follow proven patterns.
sqlite-best-practices
SQLite best practices including WAL checkpoint timing for multiprocessing, stateful batch connections for atomicity, and performance configuration. Use when working with SQLite in Python projects requiring concurrent access, multi-table atomicity, or multiprocessing.
test-driven-development
Test-Driven Development workflows including red-green-refactor cycle, test-first implementation, outside-in vs inside-out testing, TDD for debugging, and TDD for refactoring. Use when implementing new features, refactoring existing code, using tests to drive design, or debugging with failing tests.
test-plan-formatting
Format concise, actionable test plans for Jira tickets using existing fptest tools and minimal MongoDB operations
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