Loop Protocol — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Loop Protocol (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.
DingDawg Loop Protocol (DDLP) — safe scheduled AI agents with governance gates.
Every loop execution is verified, receipted, and fail-closed. MCP-native. Works with CrewAI, LangGraph, Claude Code, and Cursor.
npx dingdawg-loopOr as an MCP server in Claude Desktop / Claude Code:
{
"mcpServers": {
"dingdawg-loop": {
"command": "npx",
"args": ["dingdawg-loop"]
}
}
}DDLP wraps any scheduled agent action with a governance gate that:
| Tool | Description |
|---|---|
register_loop | Register a new scheduled loop with name, description, cron, and governance policy |
execute_loop | Run one cycle of a registered loop — governance gate fires before any action |
list_loops | List all registered loops with status, last execution, and receipt count |
pause_loop | Pause a running loop — no executions until resumed |
resume_loop | Resume a paused loop |
loop_audit | Retrieve the full signed receipt audit trail for any loop |
DDLP classifies every loop at registration time. High-risk categories require elevated approval:
DROP statementsWorks with any MCP-compatible AI client:
BUSL-1.1 — see LICENSE
DingDawg Enterprise — [email protected] — dingdawg.com
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.