| name | sparc-methodology |
| description | SPARC — a systematic 5-phase development lifecycle (Specification, Pseudocode, Architecture, Refinement, Completion) run through Claude Flow multi-agent orchestration. Use when driving a feature from spec through review and deployment with coordinated agents, or when you want a structured spec→design→TDD→review→completion pipeline rather than ad-hoc edits. |
| version | 2.7.0 |
| category | development |
| tags | ["sparc","tdd","architecture","orchestration","methodology","multi-agent"] |
| author | Claude Flow |
SPARC Methodology
SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) is a
systematic development lifecycle wired into Claude Flow's multi-agent
orchestration. It offers 17 specialized modes covering research, architecture,
TDD, review, and deployment. This guide is the quick-path; the deep detail lives
in references/.
When to use
Reach for SPARC when a task benefits from a structured, multi-phase lifecycle with
coordinated agents — e.g. building a feature from requirements through tested,
reviewed, deployed code, or running a spec→design→TDD→review pipeline.
When not to use
- Full quality engineering with 111+ agents, coverage gates, and defect prediction — use build-with-quality.
- GitHub-specific code-review swarms on PRs — use github-code-review.
- Queen-led hive-mind / Byzantine consensus without a full dev lifecycle — use hive-mind-advanced.
- PRD-to-documentation generation without the SPARC lifecycle — use prd2build.
- Raw swarm-coordination primitives (quick mesh/star/ring, load balancing) without methodology overhead — use swarm-advanced.
- Simple single-file changes that need no multi-phase orchestration — edit directly.
Core philosophy
Specification before code · design before implementation · tests before features ·
review everything · document continuously. Emphasises TDD, parallel agent
execution, persistent Memory sharing across agents/sessions, and modular design.
The five phases
| Phase | Goal | Key modes |
|---|
| 1. Specification | Requirements, constraints, success criteria, pseudocode planning | researcher, analyzer, memory-manager |
| 2. Architecture | System structure, interfaces, schema, API contracts, infra | architect, designer, orchestrator |
| 3. Refinement (TDD) | Failing tests → minimum code → pass → refactor → iterate | tdd, coder, tester |
| 4. Review | Quality, security, performance, best-practice validation | reviewer, optimizer, debugger |
| 5. Completion | Integration, deployment, monitoring, docs, knowledge capture | workflow-manager, documenter, memory-manager |
The 17 modes span core orchestration (orchestrator, swarm-coordinator,
workflow-manager, batch-executor), development (coder, architect, tdd,
reviewer), analysis/research (researcher, analyzer, optimizer), and
creative/support (designer, innovator, documenter, debugger, tester,
memory-manager). Full per-mode capabilities, quality standards, and usage
snippets: references/modes.md.
Quick path
Run a single mode (preferred inside Claude Code):
mcp__claude-flow__sparc_mode { mode: "coder", task_description: "implement JWT auth" }
Or from the terminal:
npx claude-flow sparc modes
npx claude-flow sparc run <mode> "task"
npx claude-flow sparc tdd "feature"
npx claude-flow sparc pipeline "task"
npx claude-flow sparc batch <mode1,mode2> "task"
For complex work, initialize a swarm first, then spawn agents and monitor:
mcp__claude-flow__swarm_init { topology: "hierarchical", strategy: "auto", maxAgents: 8 }
mcp__claude-flow__sparc_mode { mode: "orchestrator", task_description: "coordinate feature dev" }
mcp__claude-flow__swarm_monitor { swarmId: "current", interval: 5000 }
References
- references/modes.md — the five phases in depth and all 17 modes (capabilities, quality standards, usage snippets).
- references/orchestration.md — activation methods (MCP / NPX / local), the five swarm topologies, and the complete TDD + red-green-refactor workflows.
- references/cookbook.md — best practices (Memory, batching, hooks, coverage, file layout), worked integration examples (full-stack, innovation, legacy refactor), common CLI workflows, advanced features (neural training, cross-session memory, GitHub, monitoring), and performance context.
Working principles
- Memory for coordination: store architectural decisions and share across agents/sessions (
memory_store / memory_retrieve).
- Batch related operations in a single message rather than one call per message.
- Wire hooks (
pre-task / post-edit / post-task) for lifecycle coordination.
- Target ~90% coverage and document as you build.
See
references/cookbook.md for the full detail on each.
