neural-index — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited neural-index (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Build the complete neural memory knowledge graph for this codebase.
Via MCP tool (neural-memory configured as MCP server in Claude Code):
Tool: neural_index
{ "mode": "both" }Via Python (working directly in the project):
import asyncio
from neural_memory.server import neural_index, IndexInput
asyncio.run(neural_index(IndexInput(mode="both")))| Parameter | Type | Default | Description |
|---|---|---|---|
project_root | str | "." | Project root directory |
mode | str | config default | "ast_only" (fast/local), "api_only" (AI summaries), "both" (default) |
First run takes longer. Subsequent runs can use /neural-update for incremental changes.
After indexing, use /neural-query to search and /neural-inspect to deep-dive.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.