Atlas / Skills / ruvnet / Sparc Refine

Sparc RefineCAUTION

skills/ruvnet/sparc-refine

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Verdict
CAUTION
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
73,288
01

Overview

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Read from source at commit ef7d4f0535e5OBSERVED · 2026-09-26
02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: sparc-refine
description: Run the SPARC Refinement and Completion phases — review code, improve test coverage, validate against specification, and generate documentation
argument-hint: ""
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-step mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end mcp__plugin_ruflo-core_ruflo__neural_train mcp__plugin_ruflo-core_ruflo__neural_predict Bash Read Write Edit
---

# 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

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

10. **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

11. **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

12. **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)

13. **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" }`

14. **End trajectory** — call `mcp__plugin_ruflo-core_ruflo__hooks_intelligence_trajectory-end` with the full SPARC cycle summary

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

16. **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

17. **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
03

Trust audit

CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.

LayerWhat it checksResult
L0Provenance & inventoryWARN
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (4)

MEDIUMInventory / provenance · inv.symlink · CWE-1104
crates
crates
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/agents
plugin/agents
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/commands
plugin/commands
Why it matters. link not followed
MEDIUMInventory / provenance · inv.symlink · CWE-1104
plugin/skills
plugin/skills
Why it matters. link not followed

Gates applied: no_behavioural_pass.

Audited 2026-09-26 · audit v0.4.1 · source sha ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__sparc-refine.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-09-26ef7d4f0535e5CAUTIONB89first audit
05

Questions

What does the Sparc Refine skill do?

🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory, self-learning intelligence, federation, vector RAG integration, and native Claude Code / Codex / Hermes and many more Integrated

Is Sparc Refine safe to install?

With care. The audit graded it B (89/100) and found 4 things worth knowing before you trust this skill, listed below with the exact line each was found on.

What can Sparc Refine access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (ef7d4f0535e5), read on 2026-09-26. The repository is watched, and a new audit runs when it changes — this is the first audit.

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