
Sparc Implement
- 642 installs
- 67k repo stars
- Updated August 4, 2026
- ruvnet/ruflo
sparc-implement is a ruflo agent skill that runs SPARC phases 2 and 3 to write pseudocode, define module boundaries and API contracts, and implement code for developers following structured agent-driven delivery.
About
sparc-implement is a ruvnet/ruflo skill covering SPARC Pseudocode and Architecture phases 2 and 3 after specification is complete. It guides agents to draft algorithm pseudocode, establish module boundaries and API contracts, then implement executable code using claude-flow memory, task, hooks intelligence, neural predict, and workflow tools plus Bash Read Write Edit. Developers use sparc-implement when a feature spec exists and they want methodical translation into architecture and code rather than ad-hoc generation. The skill fits multi-step agent workflows where trajectory steps and task completion tracking matter.
- Executes the full SPARC implementation protocol: Structure, Plan, Act, Review, Conclude
- Breaks complex tasks into atomic, verifiable implementation steps
- Maintains strict separation between planning and execution phases
- Produces auditable artifacts at each stage of the implementation cycle
- Works with any language or framework through agentic orchestration
Sparc Implement by the numbers
- 642 all-time installs (skills.sh)
- +6 installs in the week ending Aug 5, 2026 (Skillselion tracking)
- Ranked #1,524 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
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| Installs | 642 |
|---|---|
| repo stars | ★ 67k |
| Last updated | August 4, 2026 |
| Repository | ruvnet/ruflo ↗ |
How do you implement code from a SPARC architecture plan?
Turn structured SPARC plans into clean, executable code using a methodical four-phase agent workflow.
Who is it for?
Developers using the SPARC methodology who have a stored spec and need agent-orchestrated pseudocode, architecture, and implementation phases.
Skip if: Quick one-off edits without a prior SPARC specification or teams not using claude-flow task and memory infrastructure.
When should I use this skill?
A SPARC spec exists in memory and the next step is pseudocode, architecture design, or implementation for phases 2 and 3.
What you get
Pseudocode documents, module boundary definitions, API contracts, and implemented source code files.
- pseudocode document
- API contracts
- implemented source code
By the numbers
- Covers 2 SPARC phases: pseudocode (phase 2) and architecture plus implementation (phase 3)
Files
SPARC Architecture + Implementation
Run Phases 2 and 3 of the SPARC methodology: design algorithms with pseudocode, then establish architecture with module boundaries and API contracts.
When to use
After the Specification phase is complete and its gate has been passed. This skill covers both the Pseudocode and Architecture phases as they are tightly coupled — algorithm design informs module boundaries and vice versa.
Steps
1. Retrieve specification — call mcp__claude-flow__memory_search with namespace sparc-phases and query for the feature's spec. Extract requirements, acceptance criteria, constraints, and edge cases.
2. Retrieve phase state — call mcp__claude-flow__memory_search with namespace sparc-state and query for the feature to confirm we are in Phase 2 or 3.
3. Search for architectural patterns — call mcp__claude-flow__neural_predict with the feature description to find relevant architectural decisions from past projects
4. Phase 2 — Pseudocode Design: a. For each acceptance criterion, write language-agnostic pseudocode that satisfies it b. Define core data structures with type annotations c. Map control flow including:
- Happy path
- Error/exception paths for each edge case
- Concurrent access handling if applicable
d. Annotate algorithmic complexity (time and space) for critical paths e. Store pseudocode artifact:
- Call
mcp__claude-flow__memory_storewith namespacesparc-phases, keypseudo-{feature-slug} - Value:
{ status: "complete", algorithms: [...], dataStructures: [...], controlFlow: [...], complexity: {...} }
5. Phase 3 — Architecture Design: a. Define bounded contexts and aggregate roots following DDD patterns:
- Identify entity boundaries and value objects
- Define aggregate invariants
- Map domain events
b. Design API contracts:
- Request/response schemas with TypeScript interfaces
- Error response codes and formats
- Versioning strategy if applicable
c. Plan module boundaries:
- Directory structure
- Dependency direction rules (no circular dependencies)
- Public vs internal interfaces
d. Specify infrastructure concerns:
- Persistence strategy (database, cache, file)
- Messaging patterns (sync, async, event-driven)
- Configuration and environment requirements
e. Store architecture artifact:
- Call
mcp__claude-flow__memory_storewith namespacesparc-phases, keyarch-{feature-slug} - Value:
{ status: "complete", boundedContexts: [...], apiContracts: [...], moduleBoundaries: {...}, infrastructure: {...} }
6. Update phase state — call mcp__claude-flow__memory_store with namespace sparc-state, updating current phase to 3 (Architecture) with both artifacts recorded
7. Record trajectory step — call mcp__claude-flow__hooks_intelligence_trajectory-step with architecture summary
8. Begin implementation — if the user confirms, proceed to write production code: a. Create files following the defined module boundaries b. Implement interfaces and types first c. Implement core logic following the pseudocode d. Write unit tests alongside implementation (TDD when possible) e. Run tests to verify acceptance criteria
9. Present architecture — display the architecture decision record and suggest running /sparc advance to pass the Phase 3 gate
Output format
# Pseudocode: {Feature Name}
## Core Algorithms
### Algorithm 1: {name}FUNCTION processRequest(input): VALIDATE input against schema IF invalid THEN THROW ValidationError result <- TRANSFORM input STORE result RETURN result
Complexity: O(n) time, O(1) space
## Data Structures
- {StructName}: { field1: type, field2: type }
---
# Architecture: {Feature Name}
## Bounded Contexts
- {ContextName}: {description}
- Aggregates: {list}
- Events: {list}
## API Contracts
### POST /api/{resource}
- Request: { field1: string, field2: number }
- Response: { id: string, ...fields }
- Errors: 400 (validation), 409 (conflict), 500 (internal)
## Module Structuresrc/{feature}/ {feature}.types.ts # Interfaces and types {feature}.service.ts # Business logic {feature}.controller.ts # HTTP handling {feature}.repository.ts # Data access {feature}.test.ts # Tests
## Infrastructure
- Persistence: {strategy}
- Caching: {strategy}
- Events: {strategy}
---
Phases 2-3 complete. Run `/sparc advance` to pass the gate check.Related skills
How it compares
Use sparc-implement after sparc-spec when you need governed multi-step design-to-code execution instead of single-shot code generation.
FAQ
Which SPARC phases does sparc-implement cover?
sparc-implement runs SPARC phases 2 and 3: pseudocode design plus architecture with module boundaries, API contracts, and subsequent implementation. Phase 1 specification is handled by the separate sparc-spec skill.
What tools does sparc-implement use?
sparc-implement relies on claude-flow memory, task, hooks intelligence trajectory-step, neural_predict, and workflow_create tools alongside Bash Read Write Edit to store design artifacts and land implemented code.