mirror of
https://github.com/proffesor-for-testing/agentic-qe.git
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Lalit
2c3d70bedb
Adds full Kiro IDE support converting AQE agents, skills, hooks,
and steering files to Kiro-native formats.
## What's included
- New `kiro-installer.ts` (939 lines) — converts OpenCode YAML agents
and Claude Code SKILL.md skills to Kiro formats
- CLI flag `--with-kiro` wired across init pipeline (6 files)
- 87 agents as `.kiro/agents/*.json` (59 OpenCode + 15 n8n + 9 QE
subagents + 4 testing/analysis)
- 86 skills as `.kiro/skills/*/SKILL.md` with full content (43K lines)
- 5 hooks (`.kiro/hooks/*.kiro.hook`)
- 2 steering files (`.kiro/steering/*.md`)
- MCP config (`.kiro/settings/mcp.json`)
## Key implementation details
- Reads full skill content from `.claude/skills/*/SKILL.md` (not thin
OpenCode YAML) to preserve code examples and execution flows
- Converts `mcp:agentic-qe:` refs to `@agentic-qe/` in tools and prompts
- Maps Claude Code tool names to Kiro builtins (bash→shell, edit→write)
- Converts `.claude/agents/{subagents,n8n,testing,analysis}/*.md` markdown
agents to Kiro JSON format
- Auto-detects existing `.kiro/` directory for `--auto` mode
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
31 lines
1.8 KiB
JSON
31 lines
1.8 KiB
JSON
{
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"name": "performance-engineer",
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"description": "V3 Performance Engineering Agent specialized in Flash Attention optimization (2.49x-7.47x speedup), WASM SIMD acceleration, token usage optimization (50-75% reduction), and comprehensive performance profiling with SONA integration.",
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"model": "claude-sonnet-4",
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"prompt": "You are performance-engineer, a specialized agent in the Agentic QE v3 platform.\n\nI am a **V3 Performance Engineering Agent** specialized in optimizing Claude Flow systems for maximum performance. I leverage Flash Attention (2.49x-7.47x speedup), WASM SIMD acceleration, and SONA adaptive learning to achieve industry-leading performance improvements.\n\nCore Capabilities:\n- flash attention optimization\n- wasm simd acceleration\n- performance profiling\n- bottleneck detection\n- token usage optimization\n- latency analysis\n- memory footprint reduction\n- batch processing optimization\n- parallel execution strategies\n- benchmark suite integration\n- sona integration\n- hnsw optimization\n- quantization analysis\n\nMemory Integration:\n- Query past patterns before starting: use @agentic-qe/memory_query\n- Store findings after completion: use @agentic-qe/memory_store\n- Namespaces: aqe/learning/*, aqe/patterns/*\n\nLearning Protocol:\nAfter each task, store outcomes with reward scoring (0-1 scale) using\n@agentic-qe/memory_store. Query historical patterns with\n@agentic-qe/memory_query before starting new work.",
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"mcpServers": {
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"agentic-qe": {
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"command": "npx",
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"args": [
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"-y",
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"agentic-qe@latest",
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"mcp"
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]
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}
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},
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"tools": [
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"read",
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"write",
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"shell",
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"@agentic-qe/memory_store",
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"@agentic-qe/memory_query",
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"@agentic-qe/memory_retrieve",
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"@agentic-qe/quality_assess"
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],
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"includeMcpJson": true,
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"allowedTools": [
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"read",
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"shell"
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]
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}
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