# Prompt Caching

> Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation) Use when: prompt caching, cache prompt, response cache, cag, cache augm...

- Skill: `techwavedev/prompt-caching` (Agent Skill)
- Install (CLI): `npx skillmds@latest add techwavedev/prompt-caching`
- Raw SKILL.md: https://api.skillmd.com/api/skills/techwavedev/prompt-caching/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: techwavedev (https://skillmd.com/u/techwavedev)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/techwavedev/prompt-caching

---


# Prompt Caching

You're a caching specialist who has reduced LLM costs by 90% through strategic caching.
You've implemented systems that cache at multiple levels: prompt prefixes, full responses,
and semantic similarity matches.

You understand that LLM caching is different from traditional caching—prompts have
prefixes that can be cached, responses vary with temperature, and semantic similarity
often matters more than exact match.

Your core principles:
1. Cache at the right level—prefix, response, or both
2. K

## Capabilities

- prompt-cache
- response-cache
- kv-cache
- cag-patterns
- cache-invalidation

## Patterns

### Anthropic Prompt Caching

Use Claude's native prompt caching for repeated prefixes

### Response Caching

Cache full LLM responses for identical or similar queries

### Cache Augmented Generation (CAG)

Pre-cache documents in prompt instead of RAG retrieval

## Anti-Patterns

### ❌ Caching with High Temperature

### ❌ No Cache Invalidation

### ❌ Caching Everything

## ⚠️ Sharp Edges

| Issue | Severity | Solution |
|-------|----------|----------|
| Cache miss causes latency spike with additional overhead | high | // Optimize for cache misses, not just hits |
| Cached responses become incorrect over time | high | // Implement proper cache invalidation |
| Prompt caching doesn't work due to prefix changes | medium | // Structure prompts for optimal caching |

## Related Skills

Works well with: `context-window-management`, `rag-implementation`, `conversation-memory`

## When to Use
This skill is applicable to execute the workflow or actions described in the overview.

---

<!-- AGI-INTEGRATION-START -->

## AGI Framework Integration

> **Adapted for [@techwavedev/agi-agent-kit](https://www.npmjs.com/package/@techwavedev/agi-agent-kit)**
> Original source: [antigravity-awesome-skills](https://github.com/sickn33/antigravity-awesome-skills)

### Memory-First Protocol

Retrieve prior agent configurations, team compositions, and orchestration patterns. Critical for multi-agent system consistency.

```bash
# Check for prior AI agent orchestration context before starting
python3 execution/memory_manager.py auto --query "agent patterns and orchestration strategies for Prompt Caching"
```

### Storing Results

After completing work, store AI agent orchestration decisions for future sessions:

```bash
python3 execution/memory_manager.py store \
  --content "Agent pattern: hierarchical orchestration with Control Tower dispatcher, 3 specialist sub-agents" \
  --type decision --project <project> \
  --tags prompt-caching ai-agents
```

### Multi-Agent Collaboration

This skill is inherently multi-agent. Use cross-agent context to coordinate task distribution and avoid duplicate work.

```bash
python3 execution/cross_agent_context.py store \
  --agent "<your-agent>" \
  --action "Agent architecture designed — Control Tower + specialist agents with shared Qdrant memory" \
  --project <project>
```

### Control Tower Integration

Register agents and tasks with the Control Tower (`execution/control_tower.py`) for centralized orchestration across machines and LLM providers.

### Blockchain Identity

Each agent has a cryptographic Ed25519 identity. All memory writes are signed — enabling trust verification in multi-agent systems.

<!-- AGI-INTEGRATION-END -->