# Send Voyage AI CLI to your agent Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually. ## Fast path - Download the package from Yavira. - Extract it into a folder your agent can access. - Paste one of the prompts below and point your agent at the extracted folder. ## Suggested prompts ### New install ```text I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete. ``` ### Upgrade existing ```text I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run. ``` ## Machine-readable fields ```json { "schemaVersion": "1.0", "item": { "slug": "voyageai-skill", "name": "Voyage AI CLI", "source": "tencent", "type": "skill", "category": "开发工具", "sourceUrl": "https://clawhub.ai/mrlynn/voyageai-skill", "canonicalUrl": "https://clawhub.ai/mrlynn/voyageai-skill", "targetPlatform": "OpenClaw" }, "install": { "downloadUrl": "/downloads/voyageai-skill", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=voyageai-skill", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "packageFormat": "ZIP package", "primaryDoc": "SKILL.md", "includedAssets": [ "SKILL.md", "references/vector-search.md", "references/models.md" ], "downloadMode": "redirect", "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-05-07T17:22:31.273Z", "expiresAt": "2026-05-14T17:22:31.273Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report", "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/voyageai-skill" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] } }, "links": { "detailUrl": "https://openagent3.xyz/skills/voyageai-skill", "downloadUrl": "https://openagent3.xyz/downloads/voyageai-skill", "agentUrl": "https://openagent3.xyz/skills/voyageai-skill/agent", "manifestUrl": "https://openagent3.xyz/skills/voyageai-skill/agent.json", "briefUrl": "https://openagent3.xyz/skills/voyageai-skill/agent.md" } } ``` ## Documentation ### 🧭 Voyage AI Skill Uses the vai CLI (voyageai-cli) for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Pure Node.js — no Python required. ### Setup npm install -g voyageai-cli ### Environment Variables VariableRequired ForDescriptionVOYAGE_API_KEYembed, rerank, store, search, similarity, ingest, pingModel API key from MongoDB AtlasMONGODB_URIstore, search, index, ingest, ping (optional)Atlas connection string Get your API key: MongoDB Atlas → AI Models → Create model API key ### embed — Generate embeddings vai embed "What is MongoDB?" vai embed "search query" --model voyage-4-large --input-type query --dimensions 512 vai embed --file document.txt --input-type document cat texts.txt | vai embed vai embed "hello" --output-format array ### rerank — Rerank documents vai rerank --query "database performance" --documents "MongoDB is fast" "SQL is relational" vai rerank --query "best database" --documents-file candidates.json --top-k 3 ### store — Embed and store in Atlas vai store --db mydb --collection docs --field embedding \\ --text "MongoDB Atlas is a cloud database" \\ --metadata '{"source": "docs"}' # Batch from JSONL vai store --db mydb --collection docs --field embedding --file documents.jsonl ### search — Vector search vai search --query "cloud database" --db mydb --collection docs \\ --index vector_index --field embedding # With pre-filter vai search --query "performance" --db mydb --collection docs \\ --index vector_index --field embedding --filter '{"category": "guides"}' --limit 5 ### index — Manage vector search indexes vai index create --db mydb --collection docs --field embedding \\ --dimensions 1024 --similarity cosine --index-name my_index vai index list --db mydb --collection docs vai index delete --db mydb --collection docs --index-name my_index ### models — List available models vai models vai models --type embedding vai models --type reranking vai models --json ### ping — Test connectivity vai ping vai ping --json ### config — Manage persistent configuration vai config set api-key "pa-your-key" echo "pa-your-key" | vai config set api-key --stdin vai config get vai config delete api-key vai config path vai config reset ### demo — Interactive guided walkthrough vai demo vai demo --no-pause vai demo --skip-pipeline vai demo --keep ### explain — Learn about AI concepts vai explain # List all topics vai explain embeddings vai explain reranking vai explain vector-search vai explain rag vai explain cosine-similarity vai explain two-stage-retrieval vai explain input-type vai explain models vai explain api-keys vai explain api-access vai explain batch-processing ### similarity — Compare text similarity vai similarity "MongoDB is a document database" "MongoDB Atlas is a cloud database" vai similarity "database performance" --against "MongoDB is fast" "PostgreSQL is relational" vai similarity --file1 doc1.txt --file2 doc2.txt vai similarity "text A" "text B" --json ### ingest — Bulk import with progress vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding vai ingest --file data.csv --db myapp --collection docs --field embedding --text-column content vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding \\ --model voyage-4 --batch-size 100 --input-type document vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding --dry-run ### completions — Shell completion scripts vai completions bash # Output bash completion script vai completions zsh # Output zsh completion script # Install bash completions vai completions bash >> ~/.bashrc && source ~/.bashrc # Install zsh completions vai completions zsh > ~/.zsh/completions/_vai ### help — Display help vai help vai help embed vai embed --help ### Embed → Store → Search Pipeline # 1. Store documents vai store --db myapp --collection articles --field embedding \\ --text "MongoDB Atlas provides a fully managed cloud database" \\ --metadata '{"title": "Atlas Overview"}' # 2. Create index vai index create --db myapp --collection articles --field embedding \\ --dimensions 1024 --similarity cosine --index-name article_search # 3. Search vai search --query "how does cloud database work" \\ --db myapp --collection articles --index article_search --field embedding ### Two-Stage Retrieval (Embed + Rerank) # 1. Get candidates via vector search vai search --query "database scaling" --db myapp --collection articles \\ --index article_search --field embedding --limit 20 --json > candidates.json # 2. Rerank for precision vai rerank --query "database scaling" --documents-file candidates.json --top-k 5 ### Bulk Ingest Pipeline # 1. Validate data (dry run) vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding --dry-run # 2. Ingest with progress vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding # 3. Create index vai index create --db myapp --collection docs --field embedding \\ --dimensions 1024 --similarity cosine ### Global Flags FlagDescription--jsonMachine-readable JSON output--quietSuppress non-essential output ### References Model Catalog — All models with pricing and specs Vector Search Patterns — Atlas Vector Search integration guide ## Trust - Source: tencent - Verification: Indexed source record - Publisher: mrlynn - Version: 1.4.0 ## Source health - Status: healthy - Source download looks usable. - Yavira can redirect you to the upstream package for this source. - Health scope: source - Reason: direct_download_ok - Checked at: 2026-05-07T17:22:31.273Z - Expires at: 2026-05-14T17:22:31.273Z - Recommended action: Download for OpenClaw ## Links - [Detail page](https://openagent3.xyz/skills/voyageai-skill) - [Send to Agent page](https://openagent3.xyz/skills/voyageai-skill/agent) - [JSON manifest](https://openagent3.xyz/skills/voyageai-skill/agent.json) - [Markdown brief](https://openagent3.xyz/skills/voyageai-skill/agent.md) - [Download page](https://openagent3.xyz/downloads/voyageai-skill)