Discover Testing & security skills, compare source repositories and read the instructions for each workflow.
Create and optimize Dockerfiles with BuildKit, multi-stage builds, advanced caching, and security. Use when: (1) Creating new Dockerfile, (2) Optimizing existing Dockerfile, (3) Reducing image size, (4) Improvi...
Activates tight action-feedback operating mode. Use for any task requiring shell commands, file operations, iterative problem-solving, debugging, or when the user wants efficient execution without verbose expla...
Activates after code changes to suggest or run verification steps. Provides test-after-action patterns for different file types and frameworks.
Activates during long sessions or before context compaction. Manages attention markers and ensures critical information survives memory compression.
Activates when errors occur during execution. Provides self-correction patterns for immediate error fixing without verbose explanations or apologies.
Convert AI fluency into throughput by embedding AI into repeatable workflows with triggers, quality gates, and iteration loops that compound over time.
Assess AI fluency proficiency using 5-level certification criteria (A through E) based on demonstrated capabilities across the 4D Framework dimensions.
Select appropriate human-AI collaboration modes: Automation (AI acts independently), Augmentation (AI assists human work), or Agency (AI takes delegated responsibility).
Recognize and avoid 15 common AI fluency anti-patterns that undermine effectiveness: over-reliance, prompt magic thinking, verification theater, and more.
Control AI output through structured specifications: define roles, scope, format, decision rules, and abstraction levels. Prompts become contracts, not requests.
Apply Anthropic's 4D Framework for AI delegation: Delegation (task selection), Description (instructions), Discernment (verification), and Diligence (iteration).
Turn vague intent into solvable structures with explicit objectives, constraints, success criteria, and clear human vs AI responsibility boundaries.
Scale AI fluency beyond individuals through standards, guardrails, audit trails, and organizational policies that maintain quality and accountability.
Retain epistemic control by validating AI outputs against logic, first principles, and reality. Detect confident nonsense, false completeness, and category errors.
Build accurate mental models of AI behavior: understand context windows, probabilistic generation, hallucination causes, and predict failure modes before they occur.
Use AI as a thinking partner by injecting checklists, reasoning trees, critique loops, and stepwise logic. Delegate sub-reasoning without losing coherence.
Use AI to reshape strategic decisions, not just execution. Identify where AI creates competitive advantage, shifts constraints, and enables new business models.
Develop pre-fluency cognitive habits: recognize when not to use AI, separate thinking from generation, resist automation bias and output authority.
Critical code review methodology. Use when reviewing code, PRs, or changes to enforce rigorous review standards.
Python 3.12+ coding standards and conventions (source of truth). Use when working on Python projects, implementing features, or reviewing code for standards compliance.
Expert Python development guidance for this codebase. Use when writing Python code, implementing features, or following project coding standards and patterns.
Python code review specialist. Use when reviewing Python code, checking for bugs, security issues, or ensuring code quality standards.
Atlassian CLI (acli) for Jira operations. Use when creating tickets, searching issues, updating status, or managing Jira work items from the command line.
Auto-detect and parallelize BMAD story tasks
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