React Nextjs Development
Orchestrates intelligent skill selection and execution for react nextjs development workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘
User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘
When to Use
Use this skill when:
- Orchestrating multi-step workflows that require skill delegation
- Implementing adaptive skill routing based on confidence scores
- Building fallback mechanisms for failed skill executions
- Creating intelligent task decomposition and parallel execution
- Designing skill dependency graphs with automatic resolution
- Implementing skill selection with historical performance weighting
- Building agent systems that need to self-organize around tasks
When NOT to Use
Avoid this skill for:
- Direct task execution without orchestration needs - use individual skills instead
- High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
- Simple linear workflows without branching or fallback requirements
- Cases where skill metadata is unavailable or unreliable
Core Workflow
Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.
Score Available Skills - Calculate match scores using multi-factor algorithm:
- Text similarity between request and skill triggers
- Historical success rate for similar tasks
- Skill availability and health status
- Required dependencies and their availability
Checkpoint: Skip to fallback if no skill scores above threshold.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
Return or Fallback - Either return successful result or apply fallback chain:
- Retry with adjusted parameters
- Try alternative skill from
related-skills - Defer to human operator for critical tasks
Checkpoint: Record outcome with timing and confidence metadata.
Implementation Patterns
Pattern 1: Skill Selection Logic
import { readFileSync, existsSync } from 'fs';
import { join } from 'path';
interface NextProjectState {
framework: 'app' | 'pages';
hasTailwind: boolean;
hasEslint: boolean;
hasTypeScript: boolean;
nodeVersion: string;
}
interface TaskSelection {
command: string;
args: string[];
confidence: number;
fallbackCommand?: string;
}
/**
* Analyzes Next.js project structure and selects optimal development task.
* Implements multi-factor scoring: framework type, dependency presence, user intent.
*/
export function selectNextTask(
projectRoot: string,
userIntent: string,
availableTasks: string[]
): TaskSelection {
// Guard clause - Early Exit (Law 1)
if (!projectRoot || !existsSync(projectRoot)) {
throw new Error(`Project root not found: ${projectRoot}`);
}
// Parse project state - Make Illegal States Unrepresentable (Law 2)
const pkg = JSON.parse(readFileSync(join(projectRoot, 'package.json'), 'utf-8'));
const hasAppRouter = existsSync(join(projectRoot, 'app'));
const hasPagesRouter = existsSync(join(projectRoot, 'pages'));
const framework = hasAppRouter ? 'app' : hasPagesRouter ? 'pages' : 'unknown';
const score = (task: string): number => {
let match = 0;
if (task === 'dev' && userIntent.includes('run') && framework !== 'unknown') match += 0.8;
if (task === 'build' && userIntent.includes('prod') && framework !== 'unknown') match += 0.9;
if (task === 'lint' && userIntent.includes('check') && pkg.devDependencies?.eslint) match += 0.7;
if (task === 'typecheck' && pkg.devDependencies?.typescript) match += 0.6;
return match;
};
let bestTask = 'dev';
let bestScore = 0;
for (const task of availableTasks) {
const s = score(task);
if (s > bestScore) {
bestScore = s;
bestTask = task;
}
}
// Atomic Predictability (Law 3) - Return new structure
return {
command: 'next',
args: [bestTask],
confidence: bestScore,
fallbackCommand: bestTask === 'build' ? 'dev' : undefined
};
}
Pattern 2: Execution with Fallback
import { execSync } from 'child_process';
import { join } from 'path';
interface ExecutionResult {
success: boolean;
output: string;
attempts: number;
latencyMs: number;
fallbackUsed?: boolean;
}
/**
* Executes Next.js task with resilient fallback chain.
* Implements Fail Fast, Fail Loud (Law 4) for build/runtime errors.
*/
export function executeNextTask(
taskConfig: TaskSelection,
projectRoot: string,
maxRetries: number = 2
): ExecutionResult {
const startTime = Date.now();
let lastError: Error | null = null;
for (let attempt = 0; attempt <= maxRetries; attempt++) {
try {
// Parse context - Ensure trusted state (Law 2)
const env = { ...process.env, NODE_ENV: taskConfig.command === 'build' ? 'production' : 'development' };
// Execute domain-specific command
const output = execSync(`cd "${projectRoot}" && npx next ${taskConfig.args.join(' ')}`, {
env,
stdio: 'pipe',
timeout: 300000
}).toString();
return {
success: true,
output,
attempts: attempt + 1,
latencyMs: Date.now() - startTime
};
} catch (err: any) {
lastError = err;
// Fail Fast - Don't patch bad build state (Law 4)
if (err.message?.includes('ENOENT') || err.message?.includes('EACCES')) {
throw new Error(`Critical system error: ${err.message}`);
}
// Transient error - apply fallback chain
if (attempt === maxRetries && taskConfig.fallbackCommand) {
console.warn(`Fallback triggered: switching to ${taskConfig.fallbackCommand}`);
return executeNextTask({
...taskConfig,
command: taskConfig.fallbackCommand,
args: [],
confidence: taskConfig.confidence * 0.8
}, projectRoot, 0);
}
}
}
// All retries exhausted - Fail Loud (Law 4)
throw new Error(`Next.js task failed after ${maxRetries + 1} attempts: ${lastError?.message}`);
}
MUST DO
- Always validate skill metadata before selection (Early Exit)
- Implement fallback chain with at least 2 levels (Fallback Skill + Human)
- Log all skill selections with full context for auditability
- Return new data structures instead of mutating inputs (Atomic Predictability)
- Fail immediately with descriptive errors on invalid states
- Update confidence scores after each execution for adaptive routing
- Reference
code-philosophy(5 Laws of Elegant Defense) in all logic
MUST NOT DO
- Select skills based on a single factor (e.g., only confidence score)
- Disable fallback mechanisms "temporarily" - this creates fragile systems
- Skip validation of skill dependencies before execution
- Return partial results - either complete success or clear failure
- Use magic numbers for confidence thresholds - make them configurable
- Cache skill selections without considering context changes
TL;DR Checklist
- Parse all inputs at boundary before processing (Law 2)
- Handle edge cases with early returns at function top (Law 1)
- Fail immediately with descriptive errors on invalid states (Law 4)
- Return new data structures, never mutate inputs (Law 3)
- Implement minimum 2-level fallback chain for all skill executions
- Log all skill selections with context for full audit trail
- Validate skill metadata and dependencies before selection
- Update confidence scores after each execution for learning
TL;DR for Code Generation
- Use guard clauses - return early on invalid input before doing work
- Return simple types (dict, str, int, bool, list) - avoid complex nested objects
- Cyclomatic complexity < 10 per function - split anything larger
- Handle null/empty cases explicitly at function top (Early Exit)
- Never mutate input parameters - return new dicts/objects
- Fail fast with descriptive errors - don't try to "patch" bad data
- Reference code-philosophy laws in comments for complex logic
- Include timing and confidence metadata in all return values
Output Template
When applying this skill, produce:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- Timing Estimates - Expected latency including fallback scenarios
Related Skills
| Skill | Purpose |
|---|---|
typescript-typing-system |
Provides TypeScript patterns for React components and Next.js API routes |
frontend-architecture-patterns |
Covers frontend architectural patterns that complement Next.js project structure |
Constraints
MUST DO
- Define clear input/output contracts for every step in the orchestration flow with explicit validation
- Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
- Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
- Validate all preconditions before starting — do not proceed if required resources or permissions are missing
MUST NOT DO
- Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
- Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
- Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
- Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies
Live References
Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.
- Next.js Official Documentation — Complete official docs covering App Router, Server Components, data fetching, and routing
- React 19 Documentation — Official React reference covering hooks, components, and concurrent features
- TypeScript Handbook — Official TypeScript documentation for type-safe React and Next.js development
- Vercel Deployment Guide — Official deployment guide for Next.js applications on Vercel
- React Server Components Architecture — React team's documentation on RSC design and implementation