prompt-cache-agent-harness

Agent Building

Plan and inspect prompt-cache behavior for long-running Claude agent loops. Use when a user wants to split stable tool, system, and history context into cacheable layers, compare captured cache metadata, estimate cost impact from supplied pricing inputs, or keep durable memory outside the cached prefix.

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How to use this skill

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Source SKILL.md: https://github.com/Prompthon-IO/agent-systems-handbook/blob/HEAD/skills/prompt-cache-agent-harness/SKILL.md

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Prompt Cache Agent Harness

For the student-facing explanation of why this package exists, read README.md first. This file is the invocation contract for Codex.

Overview

Use this skill when the user is designing or debugging a long-running Claude agent workflow where prompt caching is supposed to help. The goal is not to call the Anthropic API directly from the skill. The goal is to keep the harness local and inspectable:

plan a stable prompt spine
keep tools, system instructions, and long-lived history in explicit layers
keep durable memory and user-specific recall outside the cached prefix
read captured run artifacts after the agent loop executes
produce a small report on cache reads, cache writes, latency, and optional token-cost estimates

When To Use

Use this skill for requests such as:

design a prompt-cache layout for a persistent Claude agent
separate cacheable static context from dynamic memory or retrieval facts
compare cache reads and writes across cold and warm Claude runs
estimate input-cost changes from supplied Anthropic pricing values
explain why a cached prefix is being invalidated

Do not use this skill to promise provider-side savings without run artifacts. If usage metadata is missing, say that and limit the answer to prompt-layout review.

Prompt Layout Rules

Use a layered prompt spine:

tools: stable tool manifest, schemas, and capability boundaries
system: stable role, policies, and operating instructions
reference: stable project or domain context that changes rarely
session-summary: compressed prior state only when it is intended to stay stable for many turns
dynamic-memory: retrieved memories, current task facts, and user-specific details that should not invalidate the cached prefix
turn-input: the current user request and latest volatile state

Prefer cache breakpoints after stable layers, not after dynamic memory. When the provider supports explicit cache controls, apply them only to stable content that can be reused safely.

Expected Run Artifact

The helper reads one or more JSON or JSONL artifacts with fields like:

{
  "label": "warm-run",
  "latency_ms": 2800,
  "input_tokens": 18000,
  "cache_creation_input_tokens": 1200,
  "cache_read_input_tokens": 14500,
  "output_tokens": 900,
  "stable_layer_hash": "tools-system-reference-v1",
  "dynamic_memory_hash": "retrieval-42",
  "notes": ["user-specific recall was appended after the cache boundary"]
}

Useful aliases are accepted for common captured metadata:

prompt_tokens for input_tokens
cached_tokens for cache_read_input_tokens
cache_write_tokens for cache_creation_input_tokens
prefix_hash for stable_layer_hash

Local State And Outputs

Keep raw run artifacts outside the repo by default:

~/.codex/state/prompt-cache-agent-harness/
  inputs/
  reports/

Do not commit transcripts, API responses, secrets, or provider logs unless the user explicitly curates a redacted example for publication.

Commands

Preview the helper:

python3 scripts/prompt_cache_report.py --help

Generate a Markdown report without pricing:

python3 scripts/prompt_cache_report.py \
  --input ~/.codex/state/prompt-cache-agent-harness/inputs/run-pair.jsonl \
  --output ~/.codex/state/prompt-cache-agent-harness/reports/cache-report.md

Generate a report with user-supplied pricing values:

python3 scripts/prompt_cache_report.py \
  --input ~/.codex/state/prompt-cache-agent-harness/inputs/run-pair.jsonl \
  --base-input-usd-per-mtok 3.00 \
  --cache-write-usd-per-mtok 3.75 \
  --cache-hit-usd-per-mtok 0.30 \
  --output ~/.codex/state/prompt-cache-agent-harness/reports/cache-report.md

Interpretation Rules

Treat changes to stable_layer_hash as the strongest cache-break signal.
Treat changes to dynamic_memory_hash as expected only when dynamic memory is placed after the cache boundary.
High cache writes with low cache reads usually means the workflow is paying setup cost without warm reuse.
Strong cache reads on the warm run indicate the stable prefix is being reused.
Keep durable memory separate from prompt caching; durable memory decides what to recall, while prompt caching rewards exact reusable prompt prefixes.

Safety Boundaries

Do not store sensitive conversation history in the cached prefix by default.
Do not ask the user to expose Anthropic API keys to generate a report.
Do not persist cost or trace reports in the repo unless the user asks.
Do not turn a prompt-cache harness into an uncontrolled autonomous agent runner.

Response Pattern

When reporting back, include:

which layers should be cacheable
cold versus warm cache-read share
cache-write tokens versus cache-read tokens
whether the stable layer hash changed
whether dynamic memory appears to be outside the cached prefix
cost estimates only when pricing values were supplied