Аудит кэширования промптов

---
name: audit-prompt-caching
description: "Use whenever the user mentions cached_tokens=0,total_cached_tokens,cache_read_input_tokens,cache_creation_input_tokens,cache_write_tokens,prompt_cache_key,prompt_cache_options,prompt_cache_breakpoint,previous_interaction_id,cache_control/cachePoint,TTFT,KV reuse; prefix_cache_retention_interval,prefix_caching_hash_algo,Mamba/SWA/hybrid,cross-process block hash; LLM cost or speed regressed,repeated long prompts,speeding up agents; LLM request shape changes: tools,schemas,response_format,model/router,agent loops,compaction; Not for generic prompt writing,RAG,token counts,non-LLM perf"
---

# Prompt Cache Audit

Diagnose LLM prompt/prefix cache misses as request-path engineering problems:
stable reusable prefixes, provider telemetry, cache-aware routing, and entries
that live long enough to be reused. Caching is worth changing only when the
prefix is stable, long enough, repeated, measurable, and safe. Do not add cache
controls, keys, salts, provider pins, routing hints, or broader cache sharing
until the applicability, telemetry, and trust-boundary checks justify them.

## When to use

Use this skill for LLM calls where repeated prompt prefixes may affect cost,
TTFT, prefill latency, or self-hosted KV reuse. Typical triggers:

- `cached_tokens=0`, `cache_read_input_tokens=0`, `cache_write_tokens`, writes without reads, or unclear usage fields; GPT-5.6/GPT-6 Astra `prompt_cache_options`/`prompt_cache_breakpoint`; or migration from `prompt_cache_retention`.
- Effort or reasoning changes inside a cached conversation: per-step `reasoning.effort`, GPT-6 Astra `configuration_update`, Claude per-message `output_config.effort`, thinking toggles.
- Cache hit rate, TTFT, prefill latency, or input-token cost changed; LLM cost or speed regressed around repeated long prompts, shared context, long agents, or tool loops, or a reported hit rate is not trusted.
- LLM request shape changed where repeated long prompts, TTFT, cached-token telemetry, or LLM cost matter: inspect prompt text, message order, request builders, tools, schemas, `response_format`, provider API surface, model/router settings, agent loops, or context compaction.
- Long system prompts, tool catalogs, schemas, static documents, few-shot/RAG context, provider cache APIs, or vLLM/SGLang multi-replica KV deployments with KV pressure, tokenizer/chat-template drift, cache salts, or APC benchmarks such as `vllm bench serve`, `prefix_repetition`, and `benchmark_prefix_caching.py`.

## When not to use

Do not use this skill for:
- generic prompt writing, prompt-quality editing, or ordinary short prompt edits without repeated-prefix, TTFT, cache telemetry, or LLM cost concern
- generic RAG design unless repeated context placement/cacheability is part of the task
- token counting or context-window sizing only
- response caching only, unless comparing it with prompt prefix caching
- non-LLM frontend/backend performance or non-inference Kubernetes routing
- speculative savings claims without usage data or explicitly stated assumptions