affaan-m

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

Facilitates building MCP servers using Node/TypeScript SDK with tools, resources, and validation for efficient server management.

Transforms product requirements into clear capability plans, outlining constraints and implementation details for effective project execution.

Automates verification processes for Laravel projects, ensuring environment checks, linting, tests, and deployment readiness.

Facilitates evidence-based automation audits and workflows, identifying active, broken, or redundant tasks before making fixes.

Transforms single-line goals into step-by-step project plans for multi-agent workflows, enabling efficient execution and collaboration.

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.

Build a source-derived writing style profile from real posts, essays, launch notes, docs, or site copy, then reuse that profile across content, outreach, and social workflows. Use when the user wants voice consistency without generic AI writing tropes.

Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).

Ensures digital products meet WCAG 2.2 standards for accessibility, enhancing usability for all users, including those with disabilities.

Automates regression testing for AI-assisted development, ensuring consistent API behavior in sandbox and production environments.

Tracks user interface state changes to identify bugs in button interactions and shared state management.

Facilitates production-ready machine learning workflows with data contracts, reproducible training, and robust model evaluation and monitoring.

Automates the verification process for Quarkus projects, ensuring builds, tests, and security scans are completed before deployment.

Compares coding agents like Claude Code and Aider on custom tasks, measuring pass rates, costs, time, and consistency.

Builds new API connectors by matching existing integration patterns, ensuring seamless integration without reinventing architecture.

Captures architectural decisions during coding sessions, creating structured records for future reference and understanding.

Transforms Claude Code into a fully autonomous agent system with persistent memory and scheduled tasks for continuous operation.

Measures performance baselines and detects regressions before and after PRs, ensuring optimal application performance.

Monitors deployed URLs for regression issues after deployments, merges, or dependency upgrades, ensuring application stability.