# Cache Efficiency

Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor...

- Repository: https://github.com/hoangsonww/Claude-Code-Agent-Monitor (1136 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cache-efficiency -g -a claude-code -y`
Codex: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cache-efficiency -g -a codex -y`
Cursor: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cache-efficiency -g -a cursor -y`
Gemini CLI: `npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cache-efficiency -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add hoangsonww/Claude-Code-Agent-Monitor`

## Description

Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor dashboard, cache hit rate (total_cache_read / (total_cache_read + total_input)), cache_write vs cache_read reuse, cache-read vs cache-write spend, and the sessions with the poorest reuse. Pulls token totals from /api/analytics, per-session detail from /api/sessions, and dollar splits from /api/pricing/cost. Use when diagnosing cache spend or deciding whether prompt caching is paying off.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Cache Efficiency

Diagnose whether prompt caching is actually saving money, and where it is not.

## Input

The user provides: **$ARGUMENTS**

This may be: empty (analyze the whole fleet), "today" / "this week" / a date range, a session ID to scope the analysis, or a target like "hit rate > 80%". When empty, analyze all data from `/api/analytics`.

## Data Sources

| Endpoint | Returns |
|----------|---------|
| `GET /api/analytics` | `tokens.total_input`, `tokens.total_output`, `tokens.total_cache_read`, `tokens.total_cache_write` (baselines pre-summed), plus `daily_sessions` |
| `GET /api/sessions?limit=200` | Session list, each has model, cwd, started_at, ended_at, inline `cost`, metadata (JSON: usage_extras with cache token detail) |
| `GET /api/sessions/{id}` | Full session detail with nested agents and events, for drill-down on a flagged session |
| `GET /api/pricing/cost` | `{ total_cost, breakdown: [{ model, input_tokens, output_tokens, cache_read_tokens, cache_write_tokens, cost, matched_rule }] }`, used to price cache read vs write spend |

### How cache economics work

```
cache_hit_rate   = total_cache_read / (total_cache_read + total_input)
cache_reuse      = total_cache_read / total_cache_write
cache_read_cost  = (cache_read_tokens  / 1M) × cache_read_per_mtok
cache_write_cost = (cache_write_tokens / 1M) × cache_write_per_mtok
```

Cache writes cost more per token than cache reads (e.g. Sonnet $3.75 write vs $0.30 read per Mtok), and writes are billed even if the cached block is never reused. The payoff only arrives on subsequent reads, so a healthy fleet shows **cache_read_tokens far exceeding cache_write_tokens**. When `cache_reuse < 1`, you are paying to cache context you barely re-read.

Token counts are **effective totals** = `current + baseline` (baselines preserve pre-compaction tokens).

## Report Sections

The rest: https://github.com/hoangsonww/Claude-Code-Agent-Monitor/blob/HEAD/plugins/ccam-analytics/skills/cache-efficiency/SKILL.md