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SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) development methodology with multi-agent orchestration. Use when running a structured spec-to-code workflow or decomposing a feature through the SPARC phases.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
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| name | sparc-methodology |
| description | SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) development methodology with multi-agent orchestration. Use when running a structured spec-to-code workflow or decomposing a feature through the SPARC phases. |
| version | 2.7.0 |
| category | development |
| tags | ["sparc","tdd","architecture","orchestration","methodology","multi-agent"] |
| author | Claude Flow |
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) is a systematic development methodology integrated with Claude Flow's multi-agent orchestration capabilities. It provides 17 specialized modes for comprehensive software development, from initial research through deployment and monitoring.
SPARC methodology emphasizes:
Goal: Define requirements, constraints, and success criteria
Key Modes: researcher, analyzer, memory-manager
Goal: Design system structure and component interfaces
Key Modes: architect, designer, orchestrator
Goal: Implement features with test-first approach
Key Modes: tdd, coder, tester
Goal: Ensure code quality, security, and performance
Key Modes: reviewer, optimizer, debugger
Goal: Integration, deployment, and monitoring
Key Modes: workflow-manager, documenter, memory-manager
The full detail lives in references/ and loads only when needed:
references/modes.md — available sparc modes & activation methods.references/patterns.md — orchestration patterns, tdd workflows, best practices & integration examples.// Train patterns from successful workflows
mcp__claude-flow__neural_train {
pattern_type: "coordination",
training_data: "successful_tdd_workflow.json",
epochs: 50
}
// Save session state
mcp__claude-flow__memory_persist {
sessionId: "feature-auth-v1"
}
// Restore in new session
mcp__claude-flow__context_restore {
snapshotId: "feature-auth-v1"
}
// Analyze repository
mcp__claude-flow__github_repo_analyze {
repo: "owner/repo",
analysis_type: "code_quality"
}
// Manage pull requests
mcp__claude-flow__github_pr_manage {
repo: "owner/repo",
pr_number: 123,
action: "review"
}
// Real-time swarm monitoring
mcp__claude-flow__swarm_monitor {
swarmId: "current",
interval: 5000
}
// Bottleneck analysis
mcp__claude-flow__bottleneck_analyze {
component: "api-layer",
metrics: ["latency", "throughput", "errors"]
}
// Token usage tracking
mcp__claude-flow__token_usage {
operation: "feature-development",
timeframe: "24h"
}
Proven Results:
# List modes
npx claude-flow sparc modes
# Run specific mode
npx claude-flow sparc run <mode> "task"
# TDD workflow
npx claude-flow sparc tdd "feature"
# Full pipeline
npx claude-flow sparc pipeline "task"
# Batch execution
npx claude-flow sparc batch <modes> "task"
// Initialize swarm
mcp__claude-flow__swarm_init { topology: "hierarchical" }
// Execute mode
mcp__claude-flow__sparc_mode { mode: "coder", task_description: "..." }
// Monitor progress
mcp__claude-flow__swarm_monitor { interval: 5000 }
// Store in memory
mcp__claude-flow__memory_usage { action: "store", key: "...", value: "..." }
Remember: SPARC = Systematic, Parallel, Agile, Refined, Complete