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'', input, re.IGNORECASE)\ntemplate_match = re.search(r'', 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}'