neural-query — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited neural-query (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.
Search the neural knowledge graph for functions, classes, modules, or concepts.
Returns layered results:
/neural-inspect to go deeperVia MCP tool (neural-memory configured as MCP server in Claude Code):
Tool: neural_query
{ "query": "your search term", "limit": 10 }Via Python (working directly in the project):
import asyncio
from neural_memory.server import neural_query, QueryInput
asyncio.run(neural_query(QueryInput(query="your search term")))| Parameter | Type | Default | Description |
|---|---|---|---|
query | str | required | Function name, class name, or concept keyword |
project_root | str | "." | Project root directory |
limit | int | 10 | Max results (1–50) |
language | str | null | Filter results to a specific language (e.g. 'python', 'typescript', 'rust') |
Results are ranked by importance score — the most connected, public-facing code appears first.
Use /neural-inspect on any result's node_id to see full context, callers, callees, and source code.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.