clawd
d81e403f01
COMPLETED TASKS: ✅ 06-01: Workout Swap System - Added swapped_from_id to workout_logs - Created workout_swaps table for history - POST /api/workouts/:id/swap endpoint - GET /api/workouts/available endpoint - Reversible swaps with audit trail ✅ 06-02: Muscle Group Recovery Tracking - Created muscle_group_recovery table - Implemented calculateRecoveryScore() function - GET /api/recovery/muscle-groups endpoint - GET /api/recovery/most-recovered endpoint - Auto-tracking on workout log completion ✅ 06-03: Smart Workout Recommendations - GET /api/recommendations/smart-workout endpoint - 7-day workout analysis algorithm - Recovery-based filtering (>30% threshold) - Top 3 recommendations with context - Context-aware reasoning messages DATABASE CHANGES: - Added 4 new tables: muscle_group_recovery, workout_swaps, custom_workouts, custom_workout_exercises - Extended workout_logs with: swapped_from_id, source_type, custom_workout_id, custom_workout_exercise_id - Created 7 new indexes for performance IMPLEMENTATION: - Recovery service with 4 core functions - 2 new route handlers (recovery, smartRecommendations) - Updated workouts router with swap endpoints - Integrated recovery tracking into POST /api/logs - Full error handling and logging TESTING: - Test file created: /backend/test/phase-06-tests.js - Ready for E2E and staging validation STATUS: Ready for frontend integration and production review Branch: feature/06-phase-06
2.5 KiB
2.5 KiB
auto agent
Automatically spawn and manage agents based on task requirements.
Usage
npx claude-flow auto agent [options]
Options
--task, -t <description>- Task description for agent analysis--max-agents, -m <number>- Maximum agents to spawn (default: auto)--min-agents <number>- Minimum agents required (default: 1)--strategy, -s <type>- Selection strategy: optimal, minimal, balanced--no-spawn- Analyze only, don't spawn agents
Examples
Basic auto-spawning
npx claude-flow auto agent --task "Build a REST API with authentication"
Constrained spawning
npx claude-flow auto agent -t "Debug performance issue" --max-agents 3
Analysis only
npx claude-flow auto agent -t "Refactor codebase" --no-spawn
Minimal strategy
npx claude-flow auto agent -t "Fix bug in login" -s minimal
How It Works
-
Task Analysis
- Parses task description
- Identifies required skills
- Estimates complexity
- Determines parallelization opportunities
-
Agent Selection
- Matches skills to agent types
- Considers task dependencies
- Optimizes for efficiency
- Respects constraints
-
Topology Selection
- Chooses optimal swarm structure
- Configures communication patterns
- Sets up coordination rules
- Enables monitoring
-
Automatic Spawning
- Creates selected agents
- Assigns specific roles
- Distributes subtasks
- Initiates coordination
Agent Types Selected
- Architect: System design, architecture decisions
- Coder: Implementation, code generation
- Tester: Test creation, quality assurance
- Analyst: Performance, optimization
- Researcher: Documentation, best practices
- Coordinator: Task management, progress tracking
Strategies
Optimal
- Maximum efficiency
- May spawn more agents
- Best for complex tasks
- Highest resource usage
Minimal
- Minimum viable agents
- Conservative approach
- Good for simple tasks
- Lowest resource usage
Balanced
- Middle ground
- Adaptive to complexity
- Default strategy
- Good performance/resource ratio
Integration with Claude Code
// In Claude Code after auto-spawning
mcp__claude-flow__auto_agent {
task: "Build authentication system",
strategy: "balanced",
maxAgents: 6
}
See Also
agent spawn- Manual agent creationswarm init- Initialize swarm manuallysmart spawn- Intelligent agent spawningworkflow select- Choose predefined workflows