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 agentmods add skills/secondsky/claude-skills/cloudflare-vectorizenpx 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.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.03201 |
| Opus 5 | $0.00018 | $0.01600 |
| Sonnet 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 yesterday.
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.
- yesterday First seen · 414 lines · 37 tokens per session scan A 3242fef7cb64
cloudflare-vectorize is a skill published in the GitHub repository secondsky/claude-skills (214 stars, last pushed yesterday), licensed MIT. It adds 37 tokens to every session and 3,201 once invoked, about $0.0002 per session on Opus 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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bootstrap
Set up chamnan in this repository for the first time — build the architecture index, measure how well the code describes itself, fill in missing file comments, and record a baseline. Run once per repo.
milestone
Record a change that reshaped the repository — what moved, why it was worth doing, and which areas it touched. Use after a migration, a rewrite, or a decision that changed how part of the system works.
resume
Write down where this stretch of work stopped, so the next session continues instead of restarting. Use at the end of a working session, or when handing the repository to someone else.