76/100 (Low)

OpenClaw： benign
VirusTotal： suspicious
StaticScan： unknown

OpenClaw： benign

The skill is internally coherent for a CLI that wraps Voyage AI + MongoDB Atlas Vector Search: it requires the 'vai' binary and a VOYAGE_API_KEY and installs via the public npm package; nothing in the...

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VirusTotal： suspicious VT 报告

静态扫描： unknown

README 未提供

{
    "latestVersion": {
        "_creationTime": 1770124519386,
        "_id": "k9745ze7bze7f747bpz3298k5s80fk34",
        "changelog": "- Added comprehensive SKILL.md documentation detailing all features, setup instructions, environment variables, and command references.\n- Included usage examples for embeddings, reranking, vector search, model management, batch ingestion, and more.\n- Documented common workflows for typical vector search and retrieval use cases.\n- Listed global flags and linked reference guides for models and vector search patterns.",
        "changelogSource": "user",
        "createdAt": 1770124519386,
        "parsed": {
            "clawdis": {
                "emoji": "🧭",
                "requires": {
                    "bins": [
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                    "env": [
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                    ]
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        "version": "1.4.0"
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    "owner": {
        "_creationTime": 0,
        "_id": "publishers:missing",
        "displayName": "Michael Lynn",
        "handle": "mrlynn",
        "image": "https:\/\/avatars.githubusercontent.com\/u\/192552?v=4",
        "kind": "user",
        "linkedUserId": "kn772psekq7cpczrpfrc3q9zrd809q8s"
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    "ownerHandle": "mrlynn",
    "skill": {
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        "badges": [],
        "createdAt": 1770124519386,
        "displayName": "Voyage AI CLI",
        "latestVersionId": "k9745ze7bze7f747bpz3298k5s80fk34",
        "ownerUserId": "kn772psekq7cpczrpfrc3q9zrd809q8s",
        "slug": "voyageai-skill",
        "stats": {
            "comments": 0,
            "downloads": 1647,
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            "installsCurrent": 1,
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        },
        "summary": "Voyage AI embedding and reranking CLI integrated with MongoDB Atlas Vector Search. Use for: generating text embeddings, reranking search results, storing embeddings in Atlas, performing vector similarity search, creating vector search indexes, listing available models, comparing text similarity, bulk ingestion, interactive demos, and learning about AI concepts. Triggers: embed text, generate embeddings, vector search, rerank documents, voyage ai, semantic search, similarity search, store embeddings, atlas vector search, embedding models, cosine similarity, bulk ingest, explain embeddings.",
        "tags": {
            "cli": "k9745ze7bze7f747bpz3298k5s80fk34",
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            "mongodb": "k9745ze7bze7f747bpz3298k5s80fk34",
            "reranking": "k9745ze7bze7f747bpz3298k5s80fk34",
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