Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add secondsky/claude-skills --skill cloudflare-vectorizegit clone --depth 1 https://github.com/secondsky/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/secondsky/claude-skills/cloudflare-vectorize)<a href="https://agentmods.dev/skills/secondsky/claude-skills/cloudflare-vectorize"><img src="https://agentmods.dev/badge/skills/secondsky/claude-skills/cloudflare-vectorize/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/secondsky/claude-skills/cloudflare-vectorize"><img src="https://agentmods.dev/badge/skills/secondsky/claude-skills/cloudflare-vectorize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.03201 |
| Opus 5.5 | $0.00015 | $0.01280 |
| Sonnet 5.5 | $0.00007 | $0.00640 |
| Haiku 4.5 | $0.00004 | $0.00320 |
Grade A, and why
cloudflare-vectorize scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
async fetch(request: Request, env: Env): Promise<Response> { How it starts
The opening of the file, as written. The whole thing — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cloudflare Vectorize
Complete implementation guide for Cloudflare Vectorize - a globally distributed vector database for building semantic search, RAG (Retrieval Augmented Generation), and AI-powered applications with Cloudflare Workers.
Status: Production Ready ✅ Last Updated: 2025-11-21 Dependencies: cloudflare-worker-base (for Worker setup), cloudflare-workers-ai (for embeddings) Latest Versions: [email protected], @cloudflare/[email protected] Token Savings: ~65% Errors Prevented: 8 Dev Time Saved: ~3 hours
What This Skill Provides
Core Capabilities
- ✅ Index Management: Create, configure, and manage vector indexes
- ✅ Vector Operations: Insert, upsert, query, delete, and list vectors
- ✅ Metadata Filtering: Advanced filtering with 10 metadata indexes per index
- ✅ Semantic Search: Find similar vectors using cosine, euclidean, or dot-product metrics
- ✅ RAG Patterns: Complete retrieval-augmented generation workflows
- ✅ Workers AI Integration: Native embedding generation with @cf/baai/bge-base-en-v1.5
- ✅ OpenAI Integration: Support for text-embedding-3-small/large models
- ✅ Document Processing: Text chunking and batch ingestion pipelines
Templates Included
- basic-search.ts - Simple vector search with Workers AI
- rag-chat.ts - Full RAG chatbot with context retrieval
- document-ingestion.ts - Document chunking and embedding pipeline
- metadata-filtering.ts - Advanced filtering examples
Critical Setup Rules
⚠️ MUST DO BEFORE INSERTING VECTORS
# 1. Create the index with FIXED dimensions and metric
bunx wrangler vectorize create my-index \
--dimensions=768 \
--metric=cosine
# 2. Create metadata indexes IMMEDIATELY (before inserting vectors!)
bunx wrangler vectorize create-metadata-index my-index \
--property-name=category \
--type=string
bunx wrangler vectorize create-metadata-index my-index \
--property-name=timestamp \
--type=number
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/embedding-models.md 10 KB
- references/index-operations.md 8.0 KB
- references/integration-openai-embeddings.md 12 KB
- references/integration-workers-ai-bge-base.md 9.7 KB
- references/metadata-guide.md 9.1 KB
- references/vector-operations.md 8.1 KB
- references/wrangler-commands.md 8.9 KB
- templates/basic-search.ts 6.0 KB runs code
- templates/document-ingestion.ts 13 KB runs code
- templates/metadata-filtering.ts 12 KB runs code
- templates/rag-chat.ts 8.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 414 lines · 37 tokens per session scan A 3242fef7cb64
cloudflare-vectorize is a skill published in the GitHub repository secondsky/claude-skills (231 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 3,201 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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