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Performance Profiling Agent (#63)
* auto-claude: subtask-1-1 - Create performance profiler analysis module * auto-claude: subtask-2-1 - Create performance profiler prompt * auto-claude: subtask-2-2 - Add performance_profiler to AGENT_CONFIGS * auto-claude: subtask-2-3 - Create performance profiler agent module - Added run_performance_profiler function in agents/performance_profiler.py - Added get_performance_profiler_prompt function in prompts_pkg/prompts.py - Exported new function in prompts_pkg/__init__.py - Follows patterns from planner.py and coder.py - Verification passes successfully Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com> * auto-claude: subtask-3-1 - Export performance profiler from agents module * fix: address SonarCloud unused variable warnings - Replace unused `response` with `_` in agents/performance_profiler.py - Replace unused `line` with `_line` in analysis/performance_profiler.py - Third SonarCloud issue (collapsible if) already resolved by develop merge Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> * fix: add explicit import for run_performance_profiler (CodeQL) CodeQL flagged py/undefined-export because run_performance_profiler was in __all__ but only defined via __getattr__ lazy import. Add explicit re-export to match the pattern used by other agents. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 4.5 <noreply@anthropic.com> Co-authored-by: Test User <test@example.com>
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‎apps/backend/agents/__init__.py‎

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from .memory_manager import get_graphiti_context as get_graphiti_context
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from .memory_manager import save_session_memory as save_session_memory
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from .memory_manager import save_session_to_graphiti as save_session_to_graphiti
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from .performance_profiler import run_performance_profiler as run_performance_profiler
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from .planner import run_followup_planner as run_followup_planner
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from .session import post_session_processing as post_session_processing
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from .session import run_agent_session as run_agent_session
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# Main API
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"run_autonomous_agent",
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"run_followup_planner",
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"run_performance_profiler",
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"run_code_review_session",
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"run_documentation_generator_session",
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# Memory

‎apps/backend/agents/performance_profiler.py‎

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"""
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Performance Profiler Agent Module
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==================================
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Specialized agent for analyzing and optimizing application performance.
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Identifies bottlenecks, profiles runtime and memory usage, suggests optimizations,
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and can implement performance improvements autonomously.
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"""
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import logging
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from pathlib import Path
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from core.client import create_client
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from phase_config import get_phase_model, get_phase_thinking_budget
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from phase_event import ExecutionPhase, emit_phase
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from task_logger import (
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LogPhase,
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get_task_logger,
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)
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from ui import (
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BuildState,
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Icons,
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StatusManager,
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bold,
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box,
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highlight,
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icon,
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muted,
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print_status,
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)
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from .session import run_agent_session, save_token_stats
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logger = logging.getLogger(__name__)
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async def run_performance_profiler(
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project_dir: Path,
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spec_dir: Path,
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model: str,
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verbose: bool = False,
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) -> bool:
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"""
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Run the performance profiler agent to analyze and optimize code performance.
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The profiler agent will:
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- Profile runtime performance and memory usage
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- Identify bottlenecks and optimization opportunities
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- Suggest specific optimizations
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- Implement optimizations with user approval
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- Provide before/after performance comparisons
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- Track performance trends over time
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Args:
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project_dir: Root directory for the project
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spec_dir: Directory containing the spec
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model: Claude model to use
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verbose: Whether to show detailed output
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Returns:
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bool: True if profiling completed successfully
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"""
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from prompts_pkg import get_performance_profiler_prompt
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# Initialize status manager for ccstatusline
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status_manager = StatusManager(project_dir)
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status_manager.set_active(spec_dir.name, BuildState.BUILDING)
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emit_phase(ExecutionPhase.PERFORMANCE_PROFILING, "Profiling performance")
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# Initialize task logger for persistent logging
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task_logger = get_task_logger(spec_dir)
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# Show header
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content = [
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bold(f"{icon(Icons.GEAR)} PERFORMANCE PROFILER SESSION"),
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"",
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f"Spec: {highlight(spec_dir.name)}",
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muted("Analyzing and optimizing application performance."),
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"",
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muted("The agent will profile your code and suggest optimizations."),
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]
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print()
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print(box(content, width=70, style="heavy"))
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print()
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# Start performance profiling phase in task logger
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if task_logger:
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task_logger.start_phase(
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LogPhase.CODING, "Starting performance profiling session..."
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)
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task_logger.set_session(1)
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# Create client with phase-specific model and thinking budget
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# Respects task_metadata.json configuration when no CLI override
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profiler_model = get_phase_model(spec_dir, "performance_profiling", model)
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profiler_thinking_budget = get_phase_thinking_budget(
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spec_dir, "performance_profiling"
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)
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client = create_client(
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project_dir,
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spec_dir,
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profiler_model,
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agent_type="performance_profiler",
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max_thinking_tokens=profiler_thinking_budget,
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)
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# Generate performance profiler prompt
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prompt = get_performance_profiler_prompt(spec_dir)
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print_status("Running performance profiler...", "progress")
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print()
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try:
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# Run single profiling session
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async with client:
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status, _, usage_metadata = await run_agent_session(
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client, prompt, spec_dir, verbose, phase=LogPhase.CODING
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)
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# Save token statistics for performance profiling phase
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if usage_metadata:
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try:
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input_tokens = usage_metadata.get("input_tokens", 0)
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output_tokens = usage_metadata.get("output_tokens", 0)
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if (
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"input_tokens" not in usage_metadata
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or "output_tokens" not in usage_metadata
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):
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logger.debug(
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"Usage metadata missing expected token keys; defaulting to 0s: %s",
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usage_metadata,
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)
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saved = save_token_stats(
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spec_dir,
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"performance_profiling",
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input_tokens,
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output_tokens,
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)
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if saved:
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logger.debug(
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"Performance profiling token stats saved: %d in, %d out",
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input_tokens,
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output_tokens,
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)
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except Exception as e:
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logger.warning(
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"Failed to save performance profiling token stats: %s", e
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)
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# End profiling phase in task logger
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if task_logger:
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task_logger.end_phase(
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LogPhase.CODING,
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success=(status != "error"),
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message="Performance profiling session completed",
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)
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if status == "error":
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print()
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print_status("Performance profiling failed", "error")
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status_manager.update(state=BuildState.ERROR)
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return False
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# Success
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print()
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content = [
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bold(f"{icon(Icons.SUCCESS)} PERFORMANCE PROFILING COMPLETE"),
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"",
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muted("Performance analysis and optimizations completed."),
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muted("Review the profiling results and optimization suggestions."),
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]
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print(box(content, width=70, style="heavy"))
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print()
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status_manager.update(state=BuildState.COMPLETE)
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return True
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except Exception as e:
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print()
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print_status(f"Performance profiling error: {e}", "error")
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if task_logger:
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task_logger.log_error(f"Performance profiling error: {e}", LogPhase.CODING)
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status_manager.update(state=BuildState.ERROR)
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return False

‎apps/backend/agents/tools_pkg/models.py‎

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"thinking_default": "high",
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},
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# ═══════════════════════════════════════════════════════════════════════
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# PERFORMANCE PROFILING
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# ═══════════════════════════════════════════════════════════════════════
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"performance_profiler": {
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"tools": BASE_READ_TOOLS + BASE_WRITE_TOOLS + WEB_TOOLS,
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"mcp_servers": ["context7", "graphiti", "auto-claude"],
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"mcp_servers_optional": ["linear"],
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"auto_claude_tools": [
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TOOL_UPDATE_SUBTASK_STATUS,
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TOOL_GET_BUILD_PROGRESS,
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TOOL_RECORD_DISCOVERY,
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TOOL_RECORD_GOTCHA,
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TOOL_GET_SESSION_CONTEXT,
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],
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"thinking_default": "high",
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},
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# ═══════════════════════════════════════════════════════════════════════
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# DOCUMENTATION GENERATION
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# ═══════════════════════════════════════════════════════════════════════
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"documentation_generator": {

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