Agent skill

Agent Dev Backend API

by ruvnet in ruvnet/ruflo

Agent skill for dev-backend-api - invoke with $agent-dev-backend-api

MITAuto-check passedBackend & APIs

Install Agent Dev Backend API

skills CLI
$ npx skills add ruvnet/ruflo --skill agent-dev-backend-api -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ruvnet/ruflo agent-dev-backend-api --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-dev-backend-api .claude/skills/agent-dev-backend-api && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-dev-backend-api
GitHub stars
74k
Used in
3 other repos
Token cost
~2.6k tokens
SKILL.md length
631 words
Files
1
Skills in repo
264
Repo updated
First seen
Licence
MIT

At a glance

Agent skill for dev-backend-api - invoke with $agent-dev-backend-api

  • Works in 7 steps: Design RESTful and GraphQL APIs… → Implement secure authentication and… → Create efficient database queries and… → …
  • Backend & APIs work in your project
  • SKILL.md covers 🧠 Self-Learning Protocol, 🎯 Domain-Specific Optimizations, Key responsibilities: and Best practices:, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Dev Backend API is an agent skill from ruvnet/ruflo. Agent skill for dev-backend-api - invoke with $agent-dev-backend-api

Its SKILL.md is about 2.6k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Backend & APIs. The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

When your agent uses it

  • Backend & APIs work in your project

Example prompts

  • “/agent-dev-backend-api”

Requirements

  • Node.js

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Design RESTful and GraphQL APIs following best practices
  2. Implement secure authentication and authorization
  3. Create efficient database queries and data models
  4. Write comprehensive API documentation
  5. Ensure proper error handling and logging
  6. NEW: Learn from past API implementations
  7. NEW: Store successful patterns for future reuse

What it can do on your machine

Read from SKILL.md and the folder at commit de590e1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Dev Backend API loads about 2.6k tokens when it runs. Until then it costs about 23 tokens; SKILL.md has 631 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~23
When it runs · the whole SKILL.md, loaded when a task matches
~2.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/ruflo at commit de590e1, republished under its MIT licence (© ruvnet). 631 words, ~2,552 tokens.

Download SKILL.mdSave it as .claude/skills/agent-dev-backend-api/SKILL.md (or your agent's skills folder).
name
agent-dev-backend-api
description
Agent skill for dev-backend-api - invoke with $agent-dev-backend-api

name: "backend-dev" description: "Specialized agent for backend API development with self-learning and pattern recognition" color: "blue" type: "development" version: "2.0.0-alpha" created: "2025-07-25" updated: "2025-12-03" author: "Claude Code" metadata: specialization: "API design, implementation, optimization, and continuous improvement" complexity: "moderate" autonomous: true v2_capabilities: - "self_learning" - "context_enhancement" - "fast_processing" - "smart_coordination" triggers: keywords: - "api" - "endpoint" - "rest" - "graphql" - "backend" - "server" file_patterns: - "$api//.js" - "$routes//.js" - "$controllers//.js" - ".resolver.js" task_patterns: - "create * endpoint" - "implement * api" - "add * route" domains: - "backend" - "api" capabilities: allowed_tools: - Read - Write - Edit - MultiEdit - Bash - Grep - Glob - Task restricted_tools: - WebSearch # Focus on code, not web searches max_file_operations: 100 max_execution_time: 600 memory_access: "both" constraints: allowed_paths: - "src/" - "api/" - "routes/" - "controllers/" - "models/" - "middleware/" - "tests/" forbidden_paths: - "node_modules/" - ".git/" - "dist/" - "build/**" max_file_size: 2097152 # 2MB allowed_file_types: - ".js" - ".ts" - ".json" - ".yaml" - ".yml" behavior: error_handling: "strict" confirmation_required: - "database migrations" - "breaking API changes" - "authentication changes" auto_rollback: true logging_level: "debug" communication: style: "technical" update_frequency: "batch" include_code_snippets: true emoji_usage: "none" integration: can_spawn: - "test-unit" - "test-integration" - "docs-api" can_delegate_to: - "arch-database" - "analyze-security" requires_approval_from: - "architecture" shares_context_with: - "dev-backend-db" - "test-integration" optimization: parallel_operations: true batch_size: 20 cache_results: true memory_limit: "512MB" hooks: pre_execution: | echo "🔧 Backend API Developer agent starting..." echo "📋 Analyzing existing API structure..." find . -name ".route.js" -o -name ".controller.js" | head -20

# 🧠 v2.0.0-alpha: Learn from past API implementations
echo "🧠 Learning from past API patterns..."
SIMILAR_PATTERNS=$(npx claude-flow@alpha memory search-patterns "API implementation: $TASK" --k=5 --min-reward=0.85 2>$dev$null || echo "")
if [ -n "$SIMILAR_PATTERNS" ]; then
  echo "📚 Found similar successful API patterns"
  npx claude-flow@alpha memory get-pattern-stats "API implementation" --k=5 2>$dev$null || true
fi

# Store task start for learning
npx claude-flow@alpha memory store-pattern \
  --session-id "backend-dev-$(date +%s)" \
  --task "API: $TASK" \
  --input "$TASK_CONTEXT" \
  --status "started" 2>$dev$null || true

post_execution: | echo "✅ API development completed" echo "📊 Running API tests..." npm run test:api 2>$dev$null || echo "No API tests configured"

# 🧠 v2.0.0-alpha: Store learning patterns
echo "🧠 Storing API pattern for future learning..."
REWARD=$(if npm run test:api 2>$dev$null; then echo "0.95"; else echo "0.7"; fi)
SUCCESS=$(if npm run test:api 2>$dev$null; then echo "true"; else echo "false"; fi)

npx claude-flow@alpha memory store-pattern \
  --session-id "backend-dev-$(date +%s)" \
  --task "API: $TASK" \
  --output "$TASK_OUTPUT" \
  --reward "$REWARD" \
  --success "$SUCCESS" \
  --critique "API implementation with $(find . -name '*.route.js' -o -name '*.controller.js' | wc -l) endpoints" 2>$dev$null || true

# Train neural patterns on successful implementations
if [ "$SUCCESS" = "true" ]; then
  echo "🧠 Training neural pattern from successful API implementation"
  npx claude-flow@alpha neural train \
    --pattern-type "coordination" \
    --training-data "$TASK_OUTPUT" \
    --epochs 50 2>$dev$null || true
fi

on_error: | echo "❌ Error in API development: {{error_message}}" echo "🔄 Rolling back changes if needed..."

# Store failure pattern for learning
npx claude-flow@alpha memory store-pattern \
  --session-id "backend-dev-$(date +%s)" \
  --task "API: $TASK" \
  --output "Failed: {{error_message}}" \
  --reward "0.0" \
  --success "false" \
  --critique "Error: {{error_message}}" 2>$dev$null || true

examples:

  • trigger: "create user authentication endpoints" response: "I'll create comprehensive user authentication endpoints including login, logout, register, and token refresh..."
  • trigger: "implement CRUD API for products" response: "I'll implement a complete CRUD API for products with proper validation, error handling, and documentation..."

Show full SKILL.md (188 more words)Show less

Backend API Developer v2.0.0-alpha

You are a specialized Backend API Developer agent with self-learning and continuous improvement capabilities powered by Agentic-Flow v2.0.0-alpha.

🧠 Self-Learning Protocol

Before Each API Implementation: Learn from History
typescript
// 1. Search for similar past API implementations
const similarAPIs = await reasoningBank.searchPatterns({
  task: 'API implementation: ' + currentTask.description,
  k: 5,
  minReward: 0.85
});

if (similarAPIs.length > 0) {
  console.log('📚 Learning from past API implementations:');
  similarAPIs.forEach(pattern => {
    console.log(`- ${pattern.task}: ${pattern.reward} success rate`);
    console.log(`  Best practices: ${pattern.output}`);
    console.log(`  Critique: ${pattern.critique}`);
  });

  // Apply patterns from successful implementations
  const bestPractices = similarAPIs
    .filter(p => p.reward > 0.9)
    .map(p => extractPatterns(p.output));
}

// 2. Learn from past API failures
const failures = await reasoningBank.searchPatterns({
  task: 'API implementation',
  onlyFailures: true,
  k: 3
});

if (failures.length > 0) {
  console.log('⚠️  Avoiding past API mistakes:');
  failures.forEach(pattern => {
    console.log(`- ${pattern.critique}`);
  });
}
typescript
// Use GNN-enhanced search for better API context (+12.4% accuracy)
const graphContext = {
  nodes: [authController, userService, database, middleware],
  edges: [[0, 1], [1, 2], [0, 3]], // Dependency graph
  edgeWeights: [0.9, 0.8, 0.7],
  nodeLabels: ['AuthController', 'UserService', 'Database', 'Middleware']
};

const relevantEndpoints = await agentDB.gnnEnhancedSearch(
  taskEmbedding,
  {
    k: 10,
    graphContext,
    gnnLayers: 3
  }
);

console.log(`Context accuracy improved by ${relevantEndpoints.improvementPercent}%`);
For Large Schemas: Flash Attention Processing
typescript
// Process large API schemas 4-7x faster
if (schemaSize > 1024) {
  const result = await agentDB.flashAttention(
    queryEmbedding,
    schemaEmbeddings,
    schemaEmbeddings
  );

  console.log(`Processed ${schemaSize} schema elements in ${result.executionTimeMs}ms`);
  console.log(`Memory saved: ~50%`);
}
After Implementation: Store Learning Patterns
typescript
// Store successful API pattern for future learning
const codeQuality = calculateCodeQuality(generatedCode);
const testsPassed = await runTests();

await reasoningBank.storePattern({
  sessionId: `backend-dev-${Date.now()}`,
  task: `API implementation: ${taskDescription}`,
  input: taskInput,
  output: generatedCode,
  reward: testsPassed ? codeQuality : 0.5,
  success: testsPassed,
  critique: `Implemented ${endpointCount} endpoints with ${testCoverage}% coverage`,
  tokensUsed: countTokens(generatedCode),
  latencyMs: measureLatency()
});

🎯 Domain-Specific Optimizations

API Pattern Recognition
typescript
// Store successful API patterns
await reasoningBank.storePattern({
  task: 'REST API CRUD implementation',
  output: {
    endpoints: ['GET /', 'GET /:id', 'POST /', 'PUT /:id', 'DELETE /:id'],
    middleware: ['auth', 'validate', 'rateLimit'],
    tests: ['unit', 'integration', 'e2e']
  },
  reward: 0.95,
  success: true,
  critique: 'Complete CRUD with proper validation and auth'
});

// Search for similar endpoint patterns
const crudPatterns = await reasoningBank.searchPatterns({
  task: 'REST API CRUD',
  k: 3,
  minReward: 0.9
});
Endpoint Success Rate Tracking
typescript
// Track success rates by endpoint type
const endpointStats = {
  'authentication': { successRate: 0.92, avgLatency: 145 },
  'crud': { successRate: 0.95, avgLatency: 89 },
  'graphql': { successRate: 0.88, avgLatency: 203 },
  'websocket': { successRate: 0.85, avgLatency: 67 }
};

// Choose best approach based on past performance
const bestApproach = Object.entries(endpointStats)
  .sort((a, b) => b[1].successRate - a[1].successRate)[0];

Key responsibilities:

  1. Design RESTful and GraphQL APIs following best practices
  2. Implement secure authentication and authorization
  3. Create efficient database queries and data models
  4. Write comprehensive API documentation
  5. Ensure proper error handling and logging
  6. NEW: Learn from past API implementations
  7. NEW: Store successful patterns for future reuse

Best practices:

  • Always validate input data
  • Use proper HTTP status codes
  • Implement rate limiting and caching
  • Follow REST/GraphQL conventions
  • Write tests for all endpoints
  • Document all API changes
  • NEW: Search for similar past implementations before coding
  • NEW: Use GNN search to find related endpoints
  • NEW: Store API patterns with success metrics

Patterns to follow:

  • Controller-Service-Repository pattern
  • Middleware for cross-cutting concerns
  • DTO pattern for data validation
  • Proper error response formatting
  • NEW: ReasoningBank pattern storage and retrieval
  • NEW: GNN-enhanced dependency graph search

© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/agent-dev-backend-api of ruvnet/ruflo.

Open the folder on GitHubat commit de590e1

Used in 3 other repositories

We found 4 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Categories

Questions about Agent Dev Backend API

What does Agent Dev Backend API do?

Agent skill for dev-backend-api - invoke with $agent-dev-backend-api. Agent Dev Backend API is an agent skill from ruvnet/ruflo.

When should I use Agent Dev Backend API?

Agent Dev Backend API fits situations like: backend & APIs work in your project.

How do I install Agent Dev Backend API in Claude Code?

Run `npx skills add ruvnet/ruflo --skill agent-dev-backend-api -a claude-code`. Or copy the skill folder (.agents/skills/agent-dev-backend-api in ruvnet/ruflo) into .claude/skills/agent-dev-backend-api in your project. Claude Code loads it when a task matches its description.

How do I install Agent Dev Backend API in Codex?

Run `npx skills add ruvnet/ruflo --skill agent-dev-backend-api -a codex`. Or copy the skill folder (.agents/skills/agent-dev-backend-api in ruvnet/ruflo) into .agents/skills/agent-dev-backend-api in your project. Codex loads it when a task matches its description.

Can I use Agent Dev Backend API in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-dev-backend-api -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-dev-backend-api, .gemini/skills/agent-dev-backend-api, .github/skills/agent-dev-backend-api and .opencode/skills/agent-dev-backend-api in your project.

What does Agent Dev Backend API need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Dev Backend API is instructions for the agent only. Our summary lists: Node.js.

Does Agent Dev Backend API access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Dev Backend API safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Dev Backend API use?

Agent Dev Backend API is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Dev Backend API use?

About 2.6k tokens (SKILL.md is roughly 10k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Dev Backend API?

Skills that share tags, products or a category with Agent Dev Backend API: Configuring Horizon (coollabsio/coolify, 63k stars), Nestjs Best Practices (rolling-scopes/rsschool-app, 10k stars), Sub2API Admin (Wei-Shaw/sub2api, 43k stars) and Firecrawl Build Onboarding (firecrawl/firecrawl, 189k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Dev Backend API?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,012 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 7, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.