orchestrated-discussions/smarttools/discussion-performance/config.yaml

name: discussion-performance
description: Performance optimization specialist participant for discussions
category: Discussion
meta:
  display_name: AI-Performance
  alias: performance
  type: voting
  expertise:
  - Performance profiling
  - Algorithm optimization
  - Caching strategies
  - Database optimization
  - Memory management
  - Load testing
  - Scalability patterns
  concerns:
  - What's the time complexity?
  - Where are the bottlenecks?
  - How does this scale under load?
  - Are we using resources efficiently?
  voice: en-US-Neural2-J
  provider: opencode-reasoner
  color:
  - 255
  - 220
  - 100
arguments:
- flag: --callout
  variable: callout
  default: ''
  description: Specific question or @mention context
- flag: --templates-dir
  variable: templates_dir
  default: templates
  description: Path to templates directory
- flag: --diagrams-dir
  variable: diagrams_dir
  default: diagrams
  description: Path to save diagrams
- flag: --log-file
  variable: log_file
  default: ''
  description: Path to log file for progress updates
steps:
- type: code
  code: "import re\nimport os\n\nphase_match = re.search(r'<!--\\s*Phase:\\s*(\\w+)\\\
    s*-->', input, re.IGNORECASE)\ntemplate_match = re.search(r'<!--\\s*Template:\\\
    s*(\\w+)\\s*-->', input, re.IGNORECASE)\n\ncurrent_phase = phase_match.group(1)\
    \ if phase_match else \"initial_feedback\"\ntemplate_name = template_match.group(1)\
    \ if template_match else \"feature\"\n\ntemplate_path = os.path.join(templates_dir,\
    \ template_name + \".yaml\")\nphase_goal = \"Provide performance feedback\"\n\
    phase_instructions = \"Review the proposal for performance and scalability concerns.\"\
    \n\nif os.path.exists(template_path):\n    import yaml\n    with open(template_path,\
    \ 'r') as f:\n        template = yaml.safe_load(f)\n    phases = template.get(\"\
    phases\", {})\n    phase_info = phases.get(current_phase, {})\n    phase_goal\
    \ = phase_info.get(\"goal\", phase_goal)\n    phase_instructions = phase_info.get(\"\
    instructions\", phase_instructions)\n\nphase_context = \"Current Phase: \" + current_phase\
    \ + \"\\n\"\nphase_context += \"Phase Goal: \" + phase_goal + \"\\n\"\nphase_context\
    \ += \"Phase Instructions:\\n\" + phase_instructions\n"
  output_var: phase_context, current_phase
- type: code
  code: "import sys\nimport datetime as dt\ntimestamp = dt.datetime.now().strftime(\"\
    %H:%M:%S\")\nfor msg in [f\"Phase: {current_phase}\", \"Calling AI provider...\"\
    ]:\n    line = f\"[{timestamp}] [performance] {msg}\"\n    print(line, file=sys.stderr)\n\
    \    sys.stderr.flush()\n    if log_file:\n        with open(log_file, 'a') as\
    \ f:\n            f.write(line + \"\\n\")\n            f.flush()\n"
  output_var: _progress1
- type: prompt
  prompt: "You are AI-Performance (also known as Perry), a performance optimization\
    \ specialist\nwho ensures systems are fast, efficient, and scalable.\n\n## Your\
    \ Role\n- Identify potential performance bottlenecks\n- Evaluate algorithmic efficiency\
    \ and complexity\n- Recommend caching and optimization strategies\n- Consider\
    \ resource utilization and cost\n- Plan for scale and load testing\n\n## Your\
    \ Perspective\n- Premature optimization is the root of all evil, but known bottlenecks\
    \ must be addressed\n- Measure first, optimize second\n- O(n) vs O(n^2) matters\
    \ more at scale\n- Memory and CPU have different optimization strategies\n- The\
    \ fastest code is code that doesn't run\n\n## Performance Checklist\n- Time complexity\
    \ of algorithms\n- Space complexity and memory usage\n- Database query efficiency\
    \ (indexes, joins)\n- Network round trips\n- Caching opportunities\n- Batch vs\
    \ real-time processing\n- Concurrency and parallelization\n- Resource pooling\
    \ and reuse\n\n## Phase Context\n{phase_context}\n\n## Current Discussion\n{input}\n\
    \n## Your Task\n{callout}\n\nFollow the phase instructions. Analyze from a performance\
    \ and scalability perspective.\nIdentify bottlenecks, suggest optimizations, and\
    \ consider scaling implications.\n\n## Response Format\nRespond with valid JSON\
    \ only. Use \\n for newlines in strings:\n{{\n  \"comment\": \"Your performance\
    \ analysis...\\n\\nOptimization opportunities:\\n1. ...\\n2. ...\",\n  \"vote\"\
    : \"READY\" or \"CHANGES\" or \"REJECT\" or null,\n  \"diagram\": null\n}}\n\n\
    Vote meanings:\n- READY: Performance is acceptable\n- CHANGES: Performance improvements\
    \ needed\n- REJECT: Significant performance issues\n- null: Comment only, no vote\
    \ change\n\nIf you have nothing meaningful to add, respond: {{\"sentinel\": \"\
    NO_RESPONSE\"}}\n"
  provider: opencode-reasoner
  output_var: response
- type: code
  code: "import sys\nimport datetime as dt\ntimestamp = dt.datetime.now().strftime(\"\
    %H:%M:%S\")\nline = f\"[{timestamp}] [performance] AI response received\"\nprint(line,\
    \ file=sys.stderr)\nsys.stderr.flush()\nif log_file:\n    with open(log_file,\
    \ 'a') as f:\n        f.write(line + \"\\n\")\n        f.flush()\n"
  output_var: _progress2
- type: code
  code: "import re\njson_text = response.strip()\nif json_text.startswith('```'):\n\
    \    code_block = re.search(r'```(?:json)?\\s*(\\{.*\\})\\s*```', json_text, re.DOTALL)\n\
    \    if code_block:\n        json_text = code_block.group(1).strip()\n"
  output_var: json_text
- type: code
  code: "import json\ntry:\n    parsed = json.loads(json_text)\nexcept json.JSONDecodeError\
    \ as e:\n    fixed = json_text.replace('\\n', '\\\\n')\n    try:\n        parsed\
    \ = json.loads(fixed)\n    except json.JSONDecodeError:\n        import re\n \
    \       comment_match = re.search(r'\"comment\"\\s*:\\s*\"(.*?)\"(?=\\s*[,}])',\
    \ json_text, re.DOTALL)\n        vote_match = re.search(r'\"vote\"\\s*:\\s*(\"\
    ?\\w+\"?|null)', json_text)\n        parsed = {\n            \"comment\": comment_match.group(1).replace('\\\
    n', ' ') if comment_match else \"Parse error\",\n            \"vote\": vote_match.group(1).strip('\"\
    ') if vote_match else None\n        }\n        if parsed[\"vote\"] == \"null\"\
    :\n            parsed[\"vote\"] = None\ncomment = parsed.get(\"comment\", \"\"\
    )\nvote = parsed.get(\"vote\")\n"
  output_var: comment, vote
- type: code
  code: 'import json

    result = {"comment": comment, "vote": vote}

    final_response = json.dumps(result)

    '
  output_var: final_response
output: '{final_response}'