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Cloudflare Kv

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secondsky
cloudflare-kv

Cloudflare Workers KV global key-value storage. Use for namespaces, caching, TTL, or encountering KV_ERROR, 429 rate limits, consistency issues.

Overview

Publishersecondsky
Repositoryclaude-skills
Skill namecloudflare-kv
Stars
219
Forks
31
Bundled files
30
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 30 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by secondsky on GitHub. Read the source before you install it.

Installation

Install the Cloudflare Kv AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/secondsky/claude-skills.git /tmp/claude-skills
mkdir -p .claude/skills
cp -r /tmp/claude-skills/plugins/cloudflare-kv/skills/cloudflare-kv .claude/skills/cloudflare-kv
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloudflare Kv in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cloudflare Kv on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cloudflare Kv is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cloudflare Workers KV

Status: Production Ready ✅ | Last Verified: 2025-12-27


What Is Workers KV?

Global key-value storage on Cloudflare edge:

  • Eventually consistent
  • Low latency worldwide
  • 1GB+ values supported
  • TTL expiration
  • Metadata support

Quick Start (5 Minutes)

1. Create KV Namespace

bash
bunx wrangler kv namespace create MY_NAMESPACE
bunx wrangler kv namespace create MY_NAMESPACE --preview

2. Configure Binding

jsonc
{
  "name": "my-worker",
  "main": "src/index.ts",
  "compatibility_date": "2025-10-11",
  "kv_namespaces": [
    {
      "binding": "MY_NAMESPACE",
      "id": "<PRODUCTION_ID>",
      "preview_id": "<PREVIEW_ID>"
    }
  ]
}

3. Basic Operations

typescript
export default {
  async fetch(request, env, ctx) {
    // Write
    await env.MY_NAMESPACE.put('key', 'value');

    // Read
    const value = await env.MY_NAMESPACE.get('key');

    // Delete
    await env.MY_NAMESPACE.delete('key');

    return new Response(value);
  }
};

Load references/setup-guide.md for complete setup.


KV API Methods

put() - Write

typescript
// Basic
await env.MY_NAMESPACE.put('key', 'value');

// With TTL (1 hour)
await env.MY_NAMESPACE.put('key', 'value', {
  expirationTtl: 3600
});

// With expiration timestamp
await env.MY_NAMESPACE.put('key', 'value', {
  expiration: Math.floor(Date.now() / 1000) + 3600
});

// With metadata
await env.MY_NAMESPACE.put('key', 'value', {
  metadata: { role: 'admin', created: Date.now() }
});

get() - Read

typescript
// Simple get
const value = await env.MY_NAMESPACE.get('key');

// With type
const text = await env.MY_NAMESPACE.get('key', 'text');
const json = await env.MY_NAMESPACE.get('key', 'json');
const buffer = await env.MY_NAMESPACE.get('key', 'arrayBuffer');
const stream = await env.MY_NAMESPACE.get('key', 'stream');

// With metadata
const { value, metadata } = await env.MY_NAMESPACE.getWithMetadata('key');

delete() - Remove

typescript
await env.MY_NAMESPACE.delete('key');

list() - List Keys

typescript
// Basic list
const { keys } = await env.MY_NAMESPACE.list();

// With prefix
const { keys } = await env.MY_NAMESPACE.list({
  prefix: 'user:',
  limit: 100
});

// Pagination
const { keys, cursor } = await env.MY_NAMESPACE.list({
  cursor: previousCursor
});

Critical Rules

Always Do ✅

  1. Use TTL for temporary data
  2. Handle null (key might not exist)
  3. Use metadata for small data
  4. Paginate lists (max 1000 keys)
  5. Use prefixes for organization
  6. Cache in Worker (avoid multiple KV calls)
  7. Use waitUntil() for async writes
  8. Handle eventual consistency
  9. Monitor rate limits
  10. Use JSON.stringify for objects

Never Do ❌

  1. Never assume instant consistency
  2. Never exceed 25MB per value
  3. Never list all keys without pagination
  4. Never skip error handling
  5. Never use for real-time data
  6. Never exceed rate limits (1000 writes/second)
  7. Never store secrets unencrypted
  8. Never use as database (no transactions)
  9. Never ignore metadata limits (1024 bytes)
  10. Never skip TTL for temporary data

Common Use Cases

Use Case 1: API Response Caching

typescript
const cacheKey = `api:${url}`;
let cached = await env.MY_NAMESPACE.get(cacheKey, 'json');

if (!cached) {
  cached = await fetch(url).then(r => r.json());
  await env.MY_NAMESPACE.put(cacheKey, JSON.stringify(cached), {
    expirationTtl: 300  // 5 minutes
  });
}

return Response.json(cached);

Use Case 2: User Preferences

typescript
const userId = '123';
const preferences = {
  theme: 'dark',
  language: 'en'
};

await env.MY_NAMESPACE.put(
  `user:${userId}:preferences`,
  JSON.stringify(preferences),
  {
    metadata: { updated: Date.now() }
  }
);

Use Case 3: Rate Limiting

typescript
const key = `ratelimit:${ip}`;
const count = parseInt(await env.MY_NAMESPACE.get(key) || '0');

if (count >= 100) {
  return new Response('Rate limit exceeded', { status: 429 });
}

await env.MY_NAMESPACE.put(key, String(count + 1), {
  expirationTtl: 60  // 1 minute window
});

Use Case 4: List with Prefix

typescript
const { keys } = await env.MY_NAMESPACE.list({
  prefix: 'user:',
  limit: 100
});

const users = await Promise.all(
  keys.map(({ name }) => env.MY_NAMESPACE.get(name, 'json'))
);

Use Case 5: waitUntil() Pattern

typescript
export default {
  async fetch(request, env, ctx) {
    // Don't wait for KV write
    ctx.waitUntil(
      env.MY_NAMESPACE.put('analytics', JSON.stringify(data))
    );

    return new Response('OK');
  }
};

Limits (Summary)

Key Limits:

  • Key size: 512 bytes max
  • Value size: 25 MB max
  • Metadata: 1024 bytes max

Rate Limits:

  • Writes: 1000/sec per key
  • List: 100/sec per namespace
  • Reads: Unlimited

For detailed limits, pricing, and optimization strategies, load references/limits-quotas.md


Eventual Consistency

KV is eventually consistent:

  • Writes propagate globally (~60 seconds)
  • Not suitable for real-time data
  • Use D1 for strong consistency

Pattern:

typescript
// Write
await env.MY_NAMESPACE.put('key', 'value');

// May not be visible immediately in other regions
const value = await env.MY_NAMESPACE.get('key');  // Might be null

When to Load References

Load specific reference files based on task context:

For Setup & Configuration:

  • Load references/setup-guide.md when creating namespaces or configuring bindings

For Performance Optimization:

  • Load references/best-practices.md when implementing caching or optimizing performance
  • Load references/performance-tuning.md for advanced optimization scenarios, cacheTtl strategies, or benchmarking

For API Usage:

  • Load references/workers-api.md when implementing KV operations or need method signatures

For Troubleshooting:

  • Load references/troubleshooting.md when debugging errors or consistency issues

For Limits & Quotas:

  • Load references/limits-quotas.md when planning capacity or encountering quota errors

For Migration:

  • Load references/migration-guide.md when migrating from localStorage, Redis, D1, R2, or other storage solutions

Resources

References (references/):

  • best-practices.md - Production patterns, caching strategies, rate limit handling, error recovery
  • setup-guide.md - Complete setup with Wrangler CLI commands, namespace creation, bindings configuration
  • workers-api.md - Complete API reference, consistency model (eventual consistency), limits & quotas, performance optimization
  • troubleshooting.md - Comprehensive error catalog with solutions
  • limits-quotas.md - Detailed limits, quotas, pricing, and optimization tips
  • migration-guide.md - Complete migration guides from localStorage, Redis, D1, R2, and other storage solutions
  • performance-tuning.md - Advanced cacheTtl strategies, bulk operations, key design, benchmarking techniques

Templates (templates/):

  • kv-basic-operations.ts - Basic KV operations (get, put, delete, list)
  • kv-caching-pattern.ts - HTTP caching with KV
  • kv-list-pagination.ts - List with cursor pagination
  • kv-metadata-pattern.ts - Metadata usage patterns
  • wrangler-kv-config.jsonc - KV namespace bindings

Scripts (scripts/):

  • check-versions.sh - Validate KV API endpoints and package versions
  • test-kv-connection.sh - Test KV namespace connection and operations
  • setup-kv-namespace.sh - Interactive namespace setup wizard
  • validate-kv-config.sh - Validate wrangler.jsonc configuration
  • analyze-kv-usage.sh - Analyze code for KV usage patterns and optimizations

Commands:

  • /cloudflare-kv:setup - Interactive KV namespace setup wizard
  • /cloudflare-kv:test - Test KV operations and connection
  • /cloudflare-kv:optimize - Analyze and optimize KV usage

Agents:

  • kv-optimizer - Analyzes KV usage and suggests performance optimizations
  • kv-debugger - Helps debug KV errors and consistency issues

Examples (examples/):

  • rate-limiting/ - Complete rate limiting implementation (fixed window, sliding window, token bucket, multi-tier)
  • session-management/ - Production session store with TTL expiration, metadata tracking, and admin controls
  • api-caching/ - HTTP response caching patterns (cache-aside, stale-while-revalidate, conditional caching, ETag)
  • config-management/ - Feature flags, A/B testing, environment configs, version tracking, hot-reload

Official Documentation


Questions? Issues?

  1. Check references/setup-guide.md for complete setup
  2. Verify namespace binding configured
  3. Handle eventual consistency
  4. Check rate limits

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cloudflare Kv AI skill do?

Cloudflare Workers KV global key-value storage. Use for namespaces, caching, TTL, or encountering KV_ERROR, 429 rate limits, consistency issues.

Why use Cloudflare Kv on TypingMind?

Because you install it once and use it with any model. Cloudflare Kv is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cloudflare Kv in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/secondsky/claude-skills/tree/main/plugins/cloudflare-kv/skills/cloudflare-kv. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cloudflare Kv?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cloudflare Kv?

As many as you like. As long as a model supports skills, you can use Cloudflare Kv with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cloudflare Kv AI skill free?

Yes. It is published on GitHub by secondsky under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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