メニュー
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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Compile a goal into a durable, file-backed agent loop — a .loop/<name>/ charter any coding agent (Claude Code, Codex, Gemini, Grok) can execute unattended. Use when the user wants to "build a loop", "design a loop", "run X continuously/24-7", "make an agent keep working on Y", automate recurring work, or convert a big backlog into autonomous iteration. Produces LOOP.md + state.json + backlog.md, ready for loop-runner.
Operate a fleet of durable agent loops under .loop/ — schedule which loop runs next, assign agents to roles (maker vs checker), enforce promotion gates from propose-only to write access, allocate budgets, detect stalled or dead loops, and roll up fleet status into one report. Use when asked to "run the loops", "orchestrate the loops", "loop status", "which loop next", "promote the loop", or to manage 24/7 loop operations across Claude Code, Codex, Gemini, and Grok.
Execute exactly one disciplined iteration of a designed loop from .loop/<name>/ (LOOP.md charter + state.json + journal.md). Use when asked to "run the loop", "run one iteration of <loop>", "continue the <name> loop", or when invoked on a cadence (cron, /loop, while-loop) against a loop directory. Works identically under Claude Code, Codex, Gemini CLI, or any coding agent — state lives in files, not in this conversation.
Generate high-quality AI Architect newsletters with latest AI news, research integration, and visual design. Use when creating newsletters, researching AI developments, synthesizing AI news for architects, or distributing intelligence reports. Covers AI model releases, research papers, tool launches, architecture patterns, production systems, and enterprise AI trends. Integrates with FrankX research hub and supports both daily briefs and weekly deep-dives.
Design and generate premium book covers using NB2 (Nano Banana 2 / Gemini 3.1 Flash Image) with deep thinking about book tension, emotion, and genre. Pulls from top book cover design principles (Chip Kidd, Peter Mendelsund, Coralie Bickford-Smith). Auto-activates when user requests book cover, cover art, cover design, or mentions generating a cover for a book project.
Build autonomous AI agents with the Claude Agent SDK — computer use, tool calling, MCP integration, and production best practices for Anthropic models. Use when writing agent code against the Claude/Anthropic SDK, wiring tools or MCP servers, or hardening an agent for production.
| 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