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io.github.dingdawg/dingdawg-shield

NPM · DINGDAWG-SHIELD · SCANNED AUG 3

AI security scanning and trust scoring. Stack-specific threat models. Free local scan.

Available components

+22 this week 68 Trust /100
Trust breakdown (6 categories)

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score →

Supply Chain Security86
  • No malware found by supply-chain analysis.Pass
  • Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.Partial
  • No install/post-install scripts declared.Pass
  • Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree. View diagnostics → Partial
Provenance & Transparency45
Schema Quality & AI Usability71
  • AI-judged instruction clarity (good).Pass
  • Tool/resource definitions use about 340 tokens (~113/item across 3 items; 3 tools + 0 resources), lean.Pass
  • Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management27
  • Stability observed for 8 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
  • 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
  • 100% of tool parameters carry a description.Pass
Capabilities100
  • Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
Install

Add this component to your MCP client. Where a client-specific snippet is available, pick your client below and copy it straight into your config; otherwise use the connection detail shown.

npm · dingdawg-shield

# add to Claude Code
claude mcp add dingdawg-dingdawg-shield -- npx -y dingdawg-shield
# add to Codex CLI
codex mcp add dingdawg-dingdawg-shield -- npx -y dingdawg-shield
// opencode.json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dingdawg-dingdawg-shield": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "dingdawg-shield"
      ],
      "enabled": true
    }
  }
}
# add to OpenClaw
openclaw mcp add dingdawg-dingdawg-shield --command npx --arg -y --arg dingdawg-shield
# ~/.hermes/config.yaml
mcp_servers:
  dingdawg-dingdawg-shield:
    command: "npx"
    args: ["-y", "dingdawg-shield"]
// mcp.json
{
  "mcpServers": {
    "dingdawg-dingdawg-shield": {
      "command": "npx",
      "args": [
        "-y",
        "dingdawg-shield"
      ]
    }
  }
}
Changelog

Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.

  • 3 Aug 26 +4
    • Stability: unverified → 0.27 functional
  • 2 Aug 26 +38
    • Provenance: unverified → fail security
    • Install scripts: unverified → pass security
    • Known CVEs: unverified → partial security
    • Malware scan: unverified → pass security
    • Stability: Stability not yet verified: we do not have a sandbox capture of the MCP schema this version of the package serves yet. security
    • Capabilities: pass → unverified functional
    • Schema quality: unverified → good functional
    • Maintenance: unverified → pass functional
    • Dependency health: unverified → partial functional
    • License: unverified → pass functional
    • Licence: MIT functional
  • 1 Aug 26 +21
    • Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window). security
    • MCP protocol: unverified → pass functional
    • Tool coverage: unverified → 100 functional
  • 31 Jul 26 −23
    • We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
  • 30 Jul 26 −18
    • Malware scan: pass → unverified security
  • 27 Jul 26 46

    First indexed and scored.

Diagnostics

Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.

Captured 3 Aug 2026 · Analysed npm/[email protected]

Provenance none

Ecosystem: npm · Outcome: none

Dependencies 94 packages

94 packages in the resolved dependency tree · 94 deprecated · 29 stale.

The dependency tree was only partially resolved, so these counts may be incomplete.

MCP tools — 3 exposed · ~340 tokens

The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability.

Tool Tokens
governance_check ~92

Check if an AI system meets governance requirements. Evaluates transparency, accountability, fairness, safety, and privacy controls. Free — runs locally.

NameTypeReqDescription
deployment_contextstringWhere this AI system is deployed
has_human_oversightbooleanWhether human oversight exists for AI decisions
system_descriptionstringyesDescribe your AI system: purpose, data sources, decision scope, affected users

No output schema declared.

No examples provided.

security_scan ~119

Scan a codebase for AI security vulnerabilities. Free tier: 1 local scan per day. Checks SQL injection, XSS, SSRF, path traversal, prompt injection, hardcoded secrets, insecure deserialization, missing auth, data leakage, and more.

NameTypeReqDescription
code_snippetstringyesCode or system description to scan for AI security vulnerabilities
languagestringProgramming language (python, typescript, javascript, etc.)
scan_depthstringScan depth — quick (free), standard (API), deep (API)

No output schema declared.

No examples provided.

trust_score ~129

Get a quantified trust score for an AI agent or system. $0.05 per check via API, or free local assessment. Returns a 0-100 score with breakdown across reliability, safety, transparency, fairness, and accountability.

NameTypeReqDescription
certificationsarrayExisting certifications (SOC2, ISO 27001, etc.)
incident_historystringAny known incidents, failures, or complaints
system_descriptionstringyesWhat the system does, how it makes decisions, what data it uses
system_namestringyesName of the AI system or agent to score

No output schema declared.

No examples provided.