affaan-m/ECC

AI Regression Testing provides testing patterns for AI-assisted development, catching blind spots when the same model writes and reviews code. Use it for sandbox-mode API tests and regression coverage after AI code changes.

AI-First Engineering defines process, review, and architecture standards for teams where AI agents produce most implementation output. Use it to set ownership rules, quality gates, and evaluation criteria for agent-generated code.

API Connector Builder builds a new API connector or provider by matching an existing integration pattern in the codebase exactly. Use it when adding a new integration to a project without introducing a second architecture.

API Design Patterns provides conventions and best practices for designing consistent REST APIs. Use it when creating endpoints, reviewing contracts, handling pagination, error responses, status codes, filtering, or versioning.

Accessibility helps design, implement, and audit inclusive digital products using WCAG 2.2 Level AA standards for keyboard, contrast, and screen-reader support. Use it when building or auditing UI that must meet WCAG 2.2 Level AA or reviewing changes for accessibility.

Agent Architecture Audit diagnoses failures in agent and LLM applications by auditing the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Use it when an agent misbehaves and the failing layer is unknown, or before shipping an agent stack.

Agent Eval compares coding agents head-to-head on reproducible tasks, measuring pass rate, cost, time, and consistency. Use it to choose between agents or validate changes with data instead of impressions.

Agent Harness Construction designs and optimizes AI agent action spaces, tool definitions, and observation formatting to improve completion rates. Use it when defining or revising an agent's tool set, action space, or observation format.

Agent Introspection Debugging guides an AI agent through a structured four-phase loop to capture failures, diagnose root causes, apply contained recovery actions, and produce an introspection report. Use it when an agent run fails repeatedly, loops, or drifts and you need a reproducible diagnosis rather than a blind retry.

Agent Payment x402 adds x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Use it when an agent must pay for APIs or services, enforce spending limits, or manage its own wallet.

Agent Self-Evaluation rates an agent's own output on five axes—accuracy, completeness, clarity, actionability, and conciseness—with evidence per criterion. Use it after completing any non-trivial task to catch omissions and surface improvements before the user has to.

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

Agentic Engineering guides agents through eval-first execution, task decomposition, and cost-aware model routing. Use it when planning or executing engineering work that agents will carry out end to end.

Agentic OS codifies a persistent multi-agent operating system architecture for Claude Code. Use it when building a multi-agent workflow with file-based memory, slash commands, and scheduled automation that survives session restarts.

Android Clean Architecture provides patterns for structuring Android and Kotlin Multiplatform projects, covering module boundaries, UseCases, Repositories, and data layer with Room, SQLDelight, and Ktor. Use it when setting up layered architecture in Android or KMP projects.

Angular Developer generates Angular code and provides architectural guidance for Angular projects. Use it for creating projects, components, or services, or for best practices on reactivity, forms, DI, routing, SSR, accessibility, animations, styling, testing, and CLI tooling.

Architecture Decision Records captures architectural decisions made during Claude Code sessions as structured ADRs, recording context, alternatives, and rationale. Use it when significant technical choices are made so future developers understand why the codebase is shaped the way it is.

Article Writing writes articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use it when you want polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.

Automation Audit Ops audits live automations, jobs, hooks, and connectors to identify what is working, broken, redundant, or missing before any fixes are made.

Autonomous Agent Harness turns Claude Code into a self-directing agent system with persistent memory, scheduled tasks, computer use, and task queuing. Use it when continuous autonomous operation, scheduled tasks, or self-directing agent loops are needed.

Autonomous Loops provides patterns and reference implementations for running Claude Code in autonomous loops, from simple sequential pipelines to RFC-driven multi-agent DAG systems. Retained for compatibility; use continuous-agent-loop for new guidance.

Backend Patterns covers backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes. Use when building or reviewing Node.js, Express, or Next.js API routes and their data access.

Benchmark measures performance baselines, detects regressions before and after PRs, and compares stack alternatives for pages, APIs, and builds.

Benchmark Methodology scores competitors across nine weighted dimensions using explicit 1–5 rubrics and a tension plot. Use it after competitive-platform-analysis to produce comparable, evidence-anchored profile cards for a competitive report.

Benchmark Optimization Loop turns vague performance goals into a bounded, measured optimization process. Use it when asked to make something faster, benchmark latency/throughput/cost, or choose the best implementation by repeated measured tests.