Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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。
Migrate Agentic QE projects from v2 to v3 with zero data loss
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。
実行スコア 未検証
インストール方法を見る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。
<default_to_action> When migrating from v2 to v3:
Never delete v2 data without explicit user confirmation. </default_to_action>
# When v3 becomes main release, just update the package
npm install agentic-qe@latest
# Run migration
aqe migrate
# Or use this skill
/aqe-v2-v3-migration
| Component | v2 Location | v3 Location | Auto-Migrate |
|---|---|---|---|
| Memory DB | .agentic-qe/memory.db | .aqe/agentdb/ | Yes |
| Config | .agentic-qe/config.json | .aqe/config.json | Yes |
| Patterns | .agentic-qe/patterns/ | .aqe/reasoning-bank/ | Yes |
| Cache | .agentic-qe/cache/ | .aqe/cache/ | Optional |
| Logs | .agentic-qe/logs/ | .aqe/logs/ | No (fresh start) |
.agentic-qe/ directory)aqe --version (should be 2.x.x)npm run backup (in v2 project)npm install agentic-qe@latestaqe migrateaqe testaqe coverageaqe patterns listv2 Structure: v3 Structure:
├── src/mcp/tools/ ├── src/domains/
│ ├── test-*.ts (40+ tools) │ ├── test-generation/
│ └── ... │ ├── test-execution/
├── src/core/agents/ │ ├── coverage-analysis/
│ ├── mixed agents │ ├── quality-assessment/
│ └── ... │ ├── defect-intelligence/
└── src/core/memory/ │ ├── requirements-validation/
└── scattered impls │ ├── code-intelligence/
│ ├── security-compliance/
│ ├── contract-testing/
│ ├── visual-accessibility/
│ ├── chaos-resilience/
│ └── learning-optimization/
├── src/kernel/
│ ├── event-bus.ts
│ └── coordinator.ts
└── src/mcp/
└── domain-handlers.ts
| v2 API | v3 API | Notes |
|---|---|---|
aqe init | aqe init | Different binary |
aqe.generateTests() | testGeneration.generate() | Domain-based |
aqe.analyzeGaps() | coverageAnalysis.findGaps() | O(log n) now |
memory.store() | agentDB.store() | HNSW-indexed |
patterns.learn() | reasoningBank.record() | With verdicts |
{
"version": "2.8.2",
"memory": {
"path": ".agentic-qe/memory.db",
"type": "sqlite"
},
"agents": {
"enabled": ["test-generator", "coverage-analyzer"]
}
}
{
"version": "3.0.0",
"kernel": {
"eventBus": "in-memory",
"coordinator": "queen"
},
"domains": {
"test-generation": { "enabled": true },
"test-execution": { "enabled": true },
"coverage-analysis": {
"enabled": true,
"algorithm": "hnsw",
"dimensions": 128
}
},
"memory": {
"backend": "agentdb",
"path": ".aqe/agentdb/",
"hnsw": {
"M": 16,
"efConstruction": 200
}
},
"learning": {
"reasoningBank": true,
"sona": true
}
}
// v2: Direct SQLite access
import Database from 'better-sqlite3';
const db = new Database('.agentic-qe/memory.db');
const patterns = db.prepare('SELECT * FROM patterns').all();
// v3: AgentDB with HNSW
import { AgentDB } from 'agentic-qe';
const db = new AgentDB('.aqe/agentdb/');
await db.initialize({ dimensions: 128, M: 16 });
// Migration script transfers and indexes
for (const pattern of v2Patterns) {
await db.store({
key: pattern.id,
value: pattern.data,
embedding: await generateEmbedding(pattern.data),
metadata: { migratedFrom: 'v2', originalId: pattern.id }
});
}
Import Paths
// v2
import { AgenticQE } from 'agentic-qe';
// v3 (when v3 becomes main release, package name is still 'agentic-qe')
import { TestGenerationDomain } from 'agentic-qe/domains';
CLI Commands
# v2
aqe test --parallel
# v3
aqe test --workers=4 --topology=mesh
MCP Server
# v2
claude mcp add aqe -- npx aqe-mcp
# v3 (same CLI name, enhanced capabilities)
claude mcp add aqe -- npx aqe mcp
aqe.runTests() → Use domain-specific methodsIf migration fails or you need to revert:
# 1. v3 does NOT modify v2 data
# Your .agentic-qe/ folder is untouched
# 2. Downgrade to v2
npm install agentic-qe@2.x
rm -rf .aqe/
# 3. Continue using v2
aqe --version # Should show 2.x.x
// Use Task tool to spawn migration agents in parallel
Task({
prompt: "Analyze v2 memory.db and extract all patterns",
subagent_type: "researcher",
description: "Analyze v2 patterns"
});
Task({
prompt: "Convert v2 config to v3 format",
subagent_type: "coder",
description: "Convert config"
});
Task({
prompt: "Validate migration results",
subagent_type: "tester",
description: "Validate migration"
});
| Issue | Cause | Solution |
|---|---|---|
| "Cannot find .agentic-qe/" | No v2 installation | Run aqe init first |
| "Memory migration failed" | Corrupted SQLite | Use backup: npm run backup:restore |
| "HNSW index error" | Dimension mismatch | Set dimensions: 128 in config |
| "Pattern not found" | Not migrated | Re-run: aqe migrate --patterns |
# Run migration with debug output
DEBUG=aqe:migrate aqe migrate
# Check migration logs
cat .aqe/logs/migration.log
[v2-v3-migration] tag| v2 Version | v3 Version | Migration Support |
|---|---|---|
| 2.8.x | 3.0.x | Full |
| 2.7.x | 3.0.x | Full |
| 2.6.x | 3.0.x | Partial (config only) |
| 2.5.x and below | 3.0.x | Manual migration |
Skill Version: 1.0.0 | Last Updated: 2026-01-11
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Use when running comprehensive WCAG accessibility audits with axe-core + pa11y + Lighthouse, generating context-aware remediation, or testing video accessibility. Supports 3-tier browser cascade with graceful degradation.
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。
WCAG 2.2 compliance testing, screen reader validation, and inclusive design verification. Use when ensuring legal compliance (ADA, Section 508), testing for disabilities, or building accessible applications for 1 billion disabled users globally.
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。
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。
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。
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。
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。