neural-update — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited neural-update (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.
Sync neural memory with recent code changes without a full re-index.
Via MCP tool (neural-memory configured as MCP server in Claude Code):
Tool: neural_update
{}Via Python (working directly in the project):
import asyncio
from neural_memory.server import neural_update, UpdateInput
asyncio.run(neural_update(UpdateInput()))UpdateInput has no required fields. Pass project_root="." to be explicit.
/neural-status reports stalenessMuch faster than a full index — only touches changed files.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.