Sparc SpecCAUTION
🌊 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
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
ef7d4f0535e5OBSERVED · 2026-09-26What 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-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_search` with namespace `sparc-state` and query for the feature to see if a SPARC workflow already exists. If it does, retrieve existing artifacts. If not, initialize state with phase 1.
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: updated state with artifacts list including the spec key
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 `/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 `/sparc advance` to pass the gate check.
```Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (4)
crates
plugin/agents
plugin/commands
plugin/skills
Gates applied: no_behavioural_pass.
ef7d4f0535e5full audit observations/trust-audit/skill/ruvnet__sparc-spec.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-09-26 | ef7d4f0535e5 | CAUTION | B | 89 | first audit |
Questions
What does the Sparc Spec 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 Spec 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 Spec 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.