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Found 920 Skills
Objective task quality evaluation framework using quantitative KPIs. KPIs are automatically calculated by a hook when task files are modified and saved to TASK-XXX--kpi.json. Use when: reading KPI data for task evaluation, understanding quality metrics, deciding whether to iterate or approve based on data.
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.
Follow this sub-process for code optimization — handle tasks where 'behavior remains unchanged but structure changes' (structure / performance / readability). Shift single-module internal optimization from 'AI random refactoring' to 'first scan to generate a checklist, confirm each item with the user, execute step by step according to the method library, and obtain manual approval for each step'. Trigger scenarios: When the user mentions phrases like 'optimize / refactor / rewrite / split / poor performance / too long code' without any accompanying behavior changes. Do not handle new requirements (route to feature), bugs (route to issue), or cross-module architecture restructuring (route to architecture + decisions).
Ultra-lightweight channel for refactor processes - used when changes are obviously too small to justify the full scan → design → apply three-stage workflow. AI directly identifies 1-3 low-risk optimization points, confirms with the user once, modifies in-place using classic methods, and validates itself by running tests. No scan checklist, no design documentation, no multi-step HUMAN verification required. Trigger scenarios: When the user says "quick refactor", "small refactor", "simply optimize XX function", "modify directly", "skip all those steps", and the scope of changes is clearly limited to a single function/single component, with tests available for self-validation.
Shortcut alias for /superplan. Produce higher-quality code by breaking a feature into small, focused tasks the coding agent can nail one at a time. Works like an engineering team: feature → milestones → ~30-min tasks with specific files, acceptance criteria, and dependencies. Each task runs in a fresh context — narrow scope, full attention, one git commit per task.
Detects code smells and anti-patterns — long methods, large classes, feature envy, data clumps, primitive obsession, dead code, magic numbers, deep nesting, and more. Uses configurable thresholds from .codeprobe-config.json when available. Trigger phrases: "code smells", "smell check", "anti-patterns", "clean code review".
Analyzes code architecture and structure — layer violations, circular dependencies, god objects, anemic domain models, missing boundaries, directory structure issues, and configuration problems. Generates severity-scored findings with fix prompts. Trigger phrases: "architecture review", "structure check", "layer analysis", "god class".
Audits code for SOLID principle violations — Single Responsibility, Open/Closed, Liskov Substitution, Interface Segregation, and Dependency Inversion. Identifies classes and methods that violate these principles and generates fix prompts. Trigger phrases: "SOLID check", "solid review", "SRP violation", "dependency inversion".
Fix knip "Unused exports" violations. Handles all violation categories: test-only exports (extract to new file), dead barrel re-exports (remove from index.ts), and internally-only-used exports (un-export). Use when `npm run knip` reports unused exports.
Code review and audit system with specialized sub-skills covering SOLID principles, security, performance, architecture, error handling, testing, code smells, design patterns, and framework best practices. Generates severity-scored findings with copy-pasteable fix prompts. Strictly read-only — never modifies user code. Use when user says "review", "audit", "code review", "check my code", "security scan", "code smells", "SOLID check".
Comprehensive review of local uncommitted changes using specialized agents with code improvement suggestions
Comprehensive Python development skill covering coding standards, CLI development, linting, testing, debugging, refactoring, code review, auditing, documentation, project planning, and bulk operations. Use when writing, reviewing, refactoring, debugging, or documenting Python code; configuring linters; setting up CLI tools; planning features; performing code audits; or handling bulk operations (10+ files) that need 90%+ token savings.