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sparc-refine

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

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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_retrieve with namespace sparc-state and exact key current-phase-{feature-slug} to confirm we are in Phase 4 (or Phase 5 when resuming completion). If status === "complete", report the existing completion and do not write again; stop on missing/malformed state.

  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 }

    Before moving to Phase 5, evaluate the Phase 4 gate (passing acceptance tests, approved review, coverage >= 80%) and store its evidence in sparc-gates. On failure preserve phase, increment gateAttempts, refresh updatedAt with the current ISO timestamp at the exact current-phase key, report blockers and stop. On pass preserve existing state fields, set phase 5, phaseName "Completion", status "active", and updatedAt to the current ISO timestamp at the same key in sparc-state. When resuming Phase 5, do not repeat the Phase 4 transition.

  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. Evaluate the final Phase 5 gate — completion is allowed only when final gate evidence for every criterion passes: full regression actually ran and passed, documentation is complete, deployment checklist is verified, and traceability links every acceptance criterion to passing tests. Store the actual pass/fail evidence at gate-5-{feature-slug}-{timestamp} in sparc-gates. If evidence is missing, failed, interrupted, or unrun, preserve state and phase, increment gateAttempts, refresh updatedAt to the current ISO timestamp at the exact current-phase key, report blockers and stop. Do not store a complete artifact or complete the state, set completedAt, or train a successful cycle on failure.

    After the final gate passes, 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" }

    Close the workflow — retrieve current-phase-{feature-slug} exactly from sparc-state again and decode it as state. If status === "complete", do not write again or change its timestamps. Otherwise require phase 5 and the successful final gate evidence above; stop on missing/malformed or changed state. Preserve all existing state fields and set now = new Date().toISOString(). Call mcp__plugin_ruflo-core_ruflo__memory_store with namespace sparc-state and exact key current-phase-{feature-slug}, value { ...state, phase: 5, phaseName: "Completion", status: "complete", completedAt: now, updatedAt: now } once. Keep phase 5, never increment to 6. Report completion only after this write succeeds. A completion artifact alone does not close state; this read/write sequence is not an atomic concurrency guarantee.

  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. After the final gate and state write succeed, suggest /ruflo-sparc:ruflo-sparc report for the full methodology report. Otherwise show active status and blockers, never a completion claim.

Output format

Success example only: use actual evidence and show completion only after the final gate and state write succeed.

# 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 `/ruflo-sparc:ruflo-sparc report` for the full methodology report.

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