NightmareNet

Use after generating code, after accepting AI suggestions, or when reviewing AI-written modules. Also use when code "works" but feels brittle, when error handling seems thin, when you notice orphaned resources or missing cleanup, or when the agent says "done" but you suspect hidden debt. Catches the specific failure patterns AI agents produce that humans wouldn't.

Generate architecture diagrams, dependency maps, and system documentation directly from your codebase. Reads source files, traces dependencies, identifies boundaries, and outputs Mermaid/PlantUML/C4 diagrams. Use when asked to document architecture, visualize dependencies, generate system diagrams, create C4 models, map service boundaries, understand a codebase structure, or produce technical documentation from existing code.

Generate a CONTEXT.md shared vocabulary for any project. Reduces agent verbosity by 50-75%, improves variable naming, makes conversations more precise. Interviews the user about domain terms, then produces a structured glossary the agent references every session. Use when starting a new project, onboarding to unfamiliar code, the agent is being too verbose, or conversations have too much jargon confusion. Also use when user says "build context", "shared language", "define terms", or "the agent doesn't understand my project vocabulary".

Enforce schema discipline: queries-first design, index-every-filter, safe migrations, verify-at-scale thinking. Prevents the common failure of designing schemas for demos that break in production. Use when creating tables, planning migrations, designing data models, or when user mentions database, schema, migration, indexing, multi-tenant.

Use when making UI/frontend changes in Cursor's Design Mode, when the user selects elements visually, draws annotations, or uses voice commands to describe changes. Also use when editing components from the integrated browser, when multi-selecting elements to match styles, when making iterative visual refinements, or when the agent receives visual context (screenshots, element selections, DOM references) alongside a change request.

Build resilient systems with proper error handling, retry patterns, circuit breakers, graceful degradation, and timeout management. Use when implementing API calls, database connections, external service integrations, background jobs, or any code that can fail. Also use when the user mentions retry logic, circuit breaker, graceful degradation, error boundary, timeout handling, dead letter queue, or asks how to make code more resilient.

Enforces disciplined git practices on every commit. Small atomic commits, conventional messages, branch hygiene, and PR readiness checks. Changes how the agent HANDLES git — not just what commands it knows. Auto-triggers on any git operation. Use when committing, branching, merging, or preparing PRs. Also use when git history is messy, commits are too large, or PRs get rejected for poor git hygiene.

Use when about to create a skill, start a feature, design a system, or plan any non-trivial work. Also use when user says grill me, question my plan, challenge this, stress-test, or validate my thinking. Prevents building the wrong thing by resolving decisions before implementation.

Use when completing any task, fixing bugs, implementing features, or whenever about to claim work is done. Also use when the agent says "fixed", "done", "implemented", "all set", or "should work now" without showing evidence. Auto-triggers before any completion claim.

After every code change, automatically review your own work before presenting it. Catches bugs, style issues, and missed requirements BEFORE the user sees them. Use when implementing features, fixing bugs, refactoring, or any code modification. Auto-triggers on code changes. Reduces back-and-forth by catching issues first-pass.

Prevents long-session corruption AND context compaction amnesia. Monitors session health, detects instruction drift, enforces critical-rule recitation, and splits proactively before degradation hits. Use when working on complex multi-step tasks, when a session is getting long, when the agent starts ignoring rules it followed earlier, when CLAUDE.md conventions drift, when output quality seems to degrade, or after any context compaction event.

Diagnose and fix web performance through a disciplined measure-first loop. Forces evidence-based optimization: measure → identify bottleneck → fix ONE thing → re-measure → repeat. Prevents guessing. Use when pages load slowly, user mentions performance, LCP, INP, CLS, lighthouse, bundle size, or "make it faster".