Cache Efficiency
Analyze prompt-cache effectiveness for Claude Code usage from the Agent Monitor...
Install
npx skills add hoangsonww/Claude-Code-Agent-Monitor --skill cache-efficiency -g -a claude-code -y- Repository
- hoangsonww/Claude-Code-Agent-Monitor1.1k
- Category
- AI and agents
- Updated
- 5d ago
- License
- MIT
- Context cost
- Lightabout 832 tokens
- Runs scripts
- No, instructions only
What it does
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.
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).
