/sparc-spec
Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory
Claude Code
$ npx -y skills add ruvnet/ruflo --skill sparc-spec --agent claude-code
Codex
$ npx skills add ruvnet/ruflo --skill sparc-spec -a codex -y
Cursor
$ npx skills add ruvnet/ruflo --skill sparc-spec -a cursor -y
Gemini CLI
$ npx skills add ruvnet/ruflo --skill sparc-spec -a gemini-cli -y
GitHub Copilot
$ npx skills add ruvnet/ruflo --skill sparc-spec -a github-copilot -y
OpenCode
$ npx skills add ruvnet/ruflo --skill sparc-spec -a opencode -y
Windsurf
$ npx skills add ruvnet/ruflo --skill sparc-spec -a windsurf -y
SKILL.md
sparc-spec.SKILL.mdname: sparc-spec
description: Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory
argument-hint: "<feature-description>"
allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__task_create mcp__plugin_ruflo-core_ruflo__task_update mcp__plugin_ruflo-core_ruflo__task_complete mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step mcp__plugin_ruflo-core_ruflo__neural_predict Bash Read Edit
SPARC Specification Phase
Run Phase 1 of the SPARC methodology: define what must be built and how success is measured.
When to use
When starting a new feature or project that needs structured requirements gathering before any code is written. This phase produces the foundational specification that all subsequent phases (Pseudocode, Architecture, Refinement, Completion) build upon.
Steps
1. **Initialize phase tracking** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start` with metadata `{ "phase": "specification", "feature": "$ARGUMENTS" }`
2. **Check for prior work** — call `mcp__plugin_ruflo-core_ruflo__memory_retrieve` with namespace `sparc-state` and exact key `current-phase-{feature-slug}` to see if this workflow exists. If it does, retrieve existing artifacts. If not, initialize state at that exact key with phase 1, `status: "active"`, and `startedAt` and `updatedAt` set to the current ISO timestamp.
3. **Search for similar patterns** — call `mcp__plugin_ruflo-core_ruflo__neural_predict` with the feature description to find relevant past specifications and learned patterns
4. **Gather requirements** — analyze the feature description and the codebase to identify:
- **Functional requirements**: what the feature must do (user-facing behaviors)
- **Non-functional requirements**: performance targets, security constraints, scalability needs
- **Integration points**: what existing systems or APIs are affected
- **Data requirements**: what data is created, read, updated, or deleted
5. **Define acceptance criteria** — write at least 3 concrete, testable acceptance criteria in Given/When/Then format:
AC-1: Given [precondition], when [action], then [expected result]
AC-2: Given [precondition], when [action], then [expected result]
AC-3: Given [precondition], when [action], then [expected result]
6. **Identify constraints** — document:
- Performance constraints (latency, throughput, resource limits)
- Security constraints (authentication, authorization, data sensitivity)
- Compatibility constraints (browser support, API versions, backward compatibility)
- Infrastructure constraints (deployment environment, dependencies)
7. **Map edge cases** — list at least 3 edge cases or failure scenarios:
- What happens with invalid input?
- What happens under concurrent access?
- What happens when external dependencies fail?
8. **Store specification** — call `mcp__plugin_ruflo-core_ruflo__memory_store` with:
- Namespace: `sparc-phases`
- Key: `spec-{feature-slug}`
- Value: JSON with `{ status: "complete", requirements, acceptanceCriteria, constraints, edgeCases, integrationPoints }`
9. **Update phase state** — call `mcp__plugin_ruflo-core_ruflo__memory_store` with:
- Namespace: `sparc-state`
- Key: `current-phase-{feature-slug}`
- Value: preserve existing state fields, update artifacts list to include the spec key, and set `updatedAt` to the current ISO timestamp
10. **Record trajectory step** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step` with the specification summary
11. **Present specification** — display the full specification document to the user with a summary table and suggest running `/ruflo-sparc:ruflo-sparc advance` to pass the gate and move to the Pseudocode phase
Output format
# Specification: {Feature Name}
## Requirements
### Functional
- FR-1: ...
- FR-2: ...
### Non-Functional
- NFR-1: ...
## Acceptance Criteria
- AC-1: Given ..., when ..., then ...
- AC-2: Given ..., when ..., then ...
- AC-3: Given ..., when ..., then ...
## Constraints
- Performance: ...
- Security: ...
- Compatibility: ...
## Edge Cases
- EC-1: ...
- EC-2: ...
- EC-3: ...
## Integration Points
- IP-1: ...
---
Phase 1 complete. Run `/ruflo-sparc:ruflo-sparc advance` to pass the gate check.Read more
name: sparc-spec description: Run the SPARC Specification phase — gather requirements, define acceptance criteria, identify constraints, and store the spec in memory argument-hint: "<feature-description>" allowed-tools: mcp__plugin_ruflo-core_ruflo__memory_store mcp__plugin_ruflo-core_ruflo__memory_search mcp__plugin_ruflo-core_ruflo__memory_retrieve mcp__plugin_ruflo-core_ruflo__task_create mcp__plugin_ruflo-core_ruflo__task_update mcp__plugin_ruflo-core_ruflo__task_complete mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step mcp__plugin_ruflo-core_ruflo__neural_predict Bash Read Edit
SPARC Specification Phase
Run Phase 1 of the SPARC methodology: define what must be built and how success is measured.
When to use
When starting a new feature or project that needs structured requirements gathering before any code is written. This phase produces the foundational specification that all subsequent phases (Pseudocode, Architecture, Refinement, Completion) build upon.
Steps
1. **Initialize phase tracking** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-start` with metadata `{ "phase": "specification", "feature": "$ARGUMENTS" }`
2. **Check for prior work** — call `mcp__plugin_ruflo-core_ruflo__memory_retrieve` with namespace `sparc-state` and exact key `current-phase-{feature-slug}` to see if this workflow exists. If it does, retrieve existing artifacts. If not, initialize state at that exact key with phase 1, `status: "active"`, and `startedAt` and `updatedAt` set to the current ISO timestamp.
3. **Search for similar patterns** — call `mcp__plugin_ruflo-core_ruflo__neural_predict` with the feature description to find relevant past specifications and learned patterns
4. **Gather requirements** — analyze the feature description and the codebase to identify:
- **Functional requirements**: what the feature must do (user-facing behaviors)
- **Non-functional requirements**: performance targets, security constraints, scalability needs
- **Integration points**: what existing systems or APIs are affected
- **Data requirements**: what data is created, read, updated, or deleted
5. **Define acceptance criteria** — write at least 3 concrete, testable acceptance criteria in Given/When/Then format:
AC-1: Given [precondition], when [action], then [expected result] AC-2: Given [precondition], when [action], then [expected result] AC-3: Given [precondition], when [action], then [expected result]
6. **Identify constraints** — document:
- Performance constraints (latency, throughput, resource limits)
- Security constraints (authentication, authorization, data sensitivity)
- Compatibility constraints (browser support, API versions, backward compatibility)
- Infrastructure constraints (deployment environment, dependencies)
7. **Map edge cases** — list at least 3 edge cases or failure scenarios:
- What happens with invalid input?
- What happens under concurrent access?
- What happens when external dependencies fail?
8. **Store specification** — call `mcp__plugin_ruflo-core_ruflo__memory_store` with:
- Namespace: `sparc-phases`
- Key: `spec-{feature-slug}`
- Value: JSON with `{ status: "complete", requirements, acceptanceCriteria, constraints, edgeCases, integrationPoints }`
9. **Update phase state** — call `mcp__plugin_ruflo-core_ruflo__memory_store` with:
- Namespace: `sparc-state`
- Key: `current-phase-{feature-slug}`
- Value: preserve existing state fields, update artifacts list to include the spec key, and set `updatedAt` to the current ISO timestamp
10. **Record trajectory step** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step` with the specification summary
11. **Present specification** — display the full specification document to the user with a summary table and suggest running `/ruflo-sparc:ruflo-sparc advance` to pass the gate and move to the Pseudocode phase
Output format
# Specification: {Feature Name}
## Requirements
### Functional
- FR-1: ...
- FR-2: ...
### Non-Functional
- NFR-1: ...
## Acceptance Criteria
- AC-1: Given ..., when ..., then ...
- AC-2: Given ..., when ..., then ...
- AC-3: Given ..., when ..., then ...
## Constraints
- Performance: ...
- Security: ...
- Compatibility: ...
## Edge Cases
- EC-1: ...
- EC-2: ...
- EC-3: ...
## Integration Points
- IP-1: ...
---
Phase 1 complete. Run `/ruflo-sparc:ruflo-sparc advance` to pass the gate check.📖 RuFlo Explained — Build an AI Team That Plans, Remembers, Tests, and Improves A 14-chapter guide: from the basic idea to a first useful task, then memory, agent teams, plugins, cost and verification. Agent = Model + Harness.
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Questions about sparc-spec.
- Which coding agents can use sparc-spec?
- sparc-spec installs in Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode and Windsurf with the skills CLI, using each agent's flag shown on this page. Flowy's auto-invocation, which fires a skill when your prompt matches, runs in Claude Code only.

