MCPInstall

sparc-refine

Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/ruvnet/ruflo --skill sparc-refine
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads sparc-refine automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

SPARC Refinement + Completion

Run Phases 4 and 5 of the SPARC methodology: iteratively improve through code review and testing, then finalize with validation, documentation, and deployment readiness.

When to use

After the Architecture phase is complete and its gate has been passed. This skill covers the final two phases that bring a feature from implemented to production-ready.

Steps

Phase 4 — Refinement

  1. Retrieve all prior artifacts — call mcp__plugin_ruflo-core_ruflo__memory_search with namespace sparc-phases and query for the feature slug. Load spec (acceptance criteria), pseudocode, and architecture.

  2. Retrieve phase state — call mcp__plugin_ruflo-core_ruflo__memory_search with namespace sparc-state to confirm we are in Phase 4.

  3. Code review — review the implementation against: a. Specification compliance: does every acceptance criterion have a corresponding code path? b. Architecture adherence: do modules follow the defined boundaries and dependency rules? c. Pseudocode fidelity: does the implementation match the designed algorithms? d. Code quality: naming conventions, single responsibility, error handling, no dead code e. Document findings as review comments

  4. Test coverage analysis: a. Run existing tests and measure coverage b. Identify uncovered acceptance criteria c. Write missing tests:

    • Unit tests for each public function
    • Integration tests for cross-module interactions
    • Edge case tests for each identified edge case from the spec d. Target coverage >= 80% on new code
  5. Performance validation — if the spec includes performance constraints: a. Profile critical paths identified in the pseudocode b. Compare measured performance against constraint thresholds c. Optimize if thresholds are not met

  6. Iterate — repeat steps 3-5 until:

    • All acceptance criteria have passing tests
    • Code review has no critical or high-severity issues
    • Coverage meets the threshold
    • Performance constraints are satisfied
  7. Store refinement artifact — call mcp__plugin_ruflo-core_ruflo__memory_store with namespace sparc-phases, key refine-{feature-slug}, value: { status: "complete", reviewFindings: [...], coveragePercent: N, performanceResults: {...}, iterations: N }

  8. Record trajectory step — call mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-step with refinement summary

Phase 5 — Completion

  1. Full regression — run the complete test suite to verify no regressions from refinement changes

  2. Traceability matrix — build a matrix mapping every acceptance criterion to:

    • The test(s) that verify it
    • The code file(s) that implement it
    • The current pass/fail status
  3. Documentation: a. Generate API documentation from code comments and type definitions b. Write usage examples for key public interfaces c. Update any existing documentation affected by the changes

  4. Deployment readiness checklist:

    • All tests passing
    • Documentation complete
    • Database migrations prepared (if applicable)
    • Configuration changes documented
    • Feature flags configured (if applicable)
    • Rollback plan defined
    • Security review complete (no secrets, inputs validated)
  5. Store completion artifact — call mcp__plugin_ruflo-core_ruflo__memory_store with namespace sparc-phases, key complete-{feature-slug}, value: { status: "complete", traceabilityMatrix: [...], documentationFiles: [...], deploymentChecklist: {...}, regressionResult: "pass" }

  6. End trajectory — call mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end with the full SPARC cycle summary

  7. Train neural patterns — call mcp__plugin_ruflo-core_ruflo__neural_train with the successful SPARC cycle data to improve future predictions

  8. Store learned pattern — call mcp__plugin_ruflo-core_ruflo__memory_store with namespace patterns, key sparc-{feature-slug}, value summarizing what worked, phase durations, and common blockers encountered

  9. Present completion report — display the traceability matrix, deployment checklist, and final status. Suggest running /sparc advance to pass the final gate, or /sparc report for the full methodology report.

Output format

# Refinement: {Feature Name}

## Code Review Summary
- Critical issues: {N} (must be 0 to pass gate)
- High issues: {N}
- Medium issues: {N}
- Resolved: {N}/{total}

## Test Coverage
- Overall: {N}%
- New code: {N}%
- Acceptance criteria covered: {N}/{total}

## Performance
| Constraint | Target | Measured | Status |
|-----------|--------|----------|--------|
| Response time | <200ms | 145ms | Pass |

---

# Completion: {Feature Name}

## Traceability Matrix
| AC | Test | Code | Status |
|----|------|------|--------|
| AC-1 | test_xxx | service.ts:42 | Pass |
| AC-2 | test_yyy | controller.ts:18 | Pass |
| AC-3 | test_zzz | repository.ts:31 | Pass |

## Deployment Checklist
- [x] All tests passing
- [x] Documentation complete
- [x] Migrations prepared
- [x] Config documented
- [x] Rollback plan defined
- [x] Security reviewed

---
SPARC workflow complete. Run `/sparc report` for the full methodology report.

FAQ

What does the sparc-refine skill do?

Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation

How do I install sparc-refine?

Run npx skills add https://github.com/ruvnet/ruflo --skill sparc-refine in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sparc-refine/ (personal) or .claude/skills/sparc-refine/ (project).

Which AI agents can use sparc-refine?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is sparc-refine safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains sparc-refine?

It is published by ruvnet in ruvnet/ruflo. It has 729 recorded installs.

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