本文へ移動
cccskills
無料GitHub で公開

V3 Security Overhaul

Complete security architecture overhaul for claude-flow v3. Addresses critical CVEs (CVE-1, CVE-2, CVE-3) and implements secure-by-default patterns. Use for security-first v3 implementation.

インストール方法を見る

含まれるファイル(1)

  • SKILL.md2.4 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

V3 Security Overhaul

What This Skill Does

Orchestrates comprehensive security overhaul for claude-flow v3, addressing critical vulnerabilities and establishing security-first development practices using specialized v3 security agents.

Quick Start

# Initialize V3 security domain (parallel)
Task("Security architecture", "Design v3 threat model and security boundaries", "v3-security-architect")
Task("CVE remediation", "Fix CVE-1, CVE-2, CVE-3 critical vulnerabilities", "security-auditor")
Task("Security testing", "Implement TDD London School security framework", "test-architect")

Critical Security Fixes

CVE-1: Vulnerable Dependencies

npm update @anthropic-ai/claude-code@^2.0.31
npm audit --audit-level high

CVE-2: Weak Password Hashing

// ❌ Old: SHA-256 with hardcoded salt
const hash = crypto.createHash('sha256').update(password + salt).digest('hex');

// ✅ New: bcrypt with 12 rounds
import bcrypt from 'bcrypt';
const hash = await bcrypt.hash(password, 12);

CVE-3: Hardcoded Credentials

// ✅ Generate secure random credentials
const apiKey = crypto.randomBytes(32).toString('hex');

Security Patterns

Input Validation (Zod)

import { z } from 'zod';

const TaskSchema = z.object({
  taskId: z.string().uuid(),
  content: z.string().max(10000),
  agentType: z.enum(['security', 'core', 'integration'])
});

Path Sanitization

function securePath(userPath: string, allowedPrefix: string): string {
  const resolved = path.resolve(allowedPrefix, userPath);
  if (!resolved.startsWith(path.resolve(allowedPrefix))) {
    throw new SecurityError('Path traversal detected');
  }
  return resolved;
}

Safe Command Execution

import { execFile } from 'child_process';

// ✅ Safe: No shell interpretation
const { stdout } = await execFile('git', [userInput], { shell: false });

Success Metrics

  • Security Score: 90/100 (npm audit + custom scans)
  • CVE Resolution: 100% of critical vulnerabilities fixed
  • Test Coverage: >95% security-critical code
  • Implementation: All secure patterns documented and tested

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

Implement semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.

日本語の概要は準備中です。原文の説明を表示しています。

ruvnet/RuVector4,5582026年10月12日 更新

ruvnet のスキルをすべて見る

このスキルの問題を報告する