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Commerce Prompt Caching

OrganizationPopular
anthropics
commerce-prompt-caching

The reference agents' cache-stable request assembly, covering the static system and per-request context split, the fixed tool list, the rolling conversation breakpoint, which config fields are prompt bytes, and verification. Load when writing or reviewing a commerce agent's prompt assembly, when cache reads are zero, or when a turn's latency or cost is the question.

Overview

Publisheranthropics
Repositorycommerce-agents
Skill namecommerce-prompt-caching
Stars
3K
Forks
574
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by anthropics on GitHub. Read the source before you install it.

Installation

Install the Commerce Prompt Caching AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/anthropics/commerce-agents.git /tmp/commerce-agents
mkdir -p .claude/skills
cp -r /tmp/commerce-agents/plugins/commerce-builder/skills/commerce-prompt-caching .claude/skills/commerce-prompt-caching
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Commerce Prompt Caching in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Commerce Prompt Caching on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Commerce Prompt Caching is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cache-stable request assembly

commerce_common/ is commerce-common/commerce_common/, shopping_agent/ is shopping-agent/core/shopping_agent/, and merchant_agent/ is merchant-agent/core/merchant_agent/.

The three breakpoints

A request's cacheable prefix runs tools, then system, then the messages; a breakpoint ends a span the next call reads back. The reference agents place three, all from commerce_common/prompt_assembly.py:

  1. The last tool: with_tool_cache_control(tools).
  2. The static system text: build_system_blocks(static, context) marks it and appends the context block unmarked.
  3. The newest persisted message: build_request_messages(messages, rolling_breakpoint=...) marks the last persisted block on the outgoing request only; the next call then reads the earlier rounds, search payloads included, from cache.

Everything per request goes in the context block, which the role's build_dynamic_context(...) renders once per turn. In the static block or the tool list, those bytes would break the system or tool span on every request; in the context block they cost one re-read of the conversation on a turn whose cart, page, or facts moved, and the rolling marker holds from that turn's next round. The clock renders to the hour (context_clock), so a new minute moves nothing.

The split as implemented

  • build_static_system(config, skills) in each role's prompt.py renders identity, the most-turns rules, the fence notice, and the skill index from the config and the installed skills: the same bytes for the life of a deployment.
  • build_dynamic_context(...) in the same module renders the per-request material inside the role's data fence: shopping takes preferences, memory facts, the cart, the page, the account block, and the time; merchant takes the store context, memory facts, and the time. Backend context blocks have their own size cap.
  • ShoppingAgent.__init__ and MerchantAgent.__init__ (each role's runtime-messages-api orchestrator.py) build _static_system and _tools once per process; stream_turn renders the context block per turn and calls build_request_messages per model call.
  • build_tools in each role's tools/registry.py emits the built-ins in a fixed order (the merchant builder adds run_analysis after them when enabled), the load_skill enum sorted, then extensions in the order given, then web_search when enabled; every registered tool ships on every request, and the executor decides on arrival whether a call can be served.
  • The Agent SDK runtimes pass the same static text (plus SKILL_TOOL_ADAPTER) and the same contracts, and the SDK caches them; the hosted manifests carry the text as system.md, which scripts/check.py compares with the builder.

When the rolling marker is skipped

Two rounds carry no marker. A bare first call (one message): a one-shot session would pay the write without a read, and the second call's marker covers the first message anyway. A round whose tool_choice is other than auto (the grounding-forced first iteration and the forced-text last one): tool_choice keys the messages span, so an entry written under a forced round is unreadable by the auto rounds after it; the system and tool spans still hit. Both live in build_request_messages and the loops' rolling_breakpoint= argument; rolling_conversation_cache turns the marker off for debugging.

When the conversation is compacted

When a turn's last call was given compact_history_above_tokens or more (its usage says; the default is the platform's own tool-result-clearing default, a tenth of the window), the turn ends with compact_history in commerce_common/turn.py, which replaces the oldest tool results in the stored conversation with a one-line marker until the conversation is half its previous size. The next turn's first round rewrites the messages span once and later rounds read the shorter one; turn_complete.results_cleared tells a host that appends its transcript to rewrite it. The system and tool spans, the messages, and the write gates are unaffected; provenance lives on the session state.

Config fields that are prompt bytes

The fields marked (prompt) in commerce_common/config.py and the role configs, plus enable_analysis and max_items_per_change, which the merchant tool builder reads, render into the static text or the tool list; changing one is a redeploy and a miss on the next request:

ConfigFields
BaseAgentConfig (commerce_common/config.py)brand_name, assistant_name, brand_voice, enable_web_search
ShoppingAgentConfig (shopping_agent/config.py)domain_search_notes, enable_disclosures, and the system switches enable_cart, enable_orders, enable_policies, enable_fulfillment
MerchantAgentConfig (merchant_agent/config.py)enable_analysis, require_host_approval, approval_surface, stage_shows_preview, and the system switches enable_listing_edits, enable_inventory, enable_pricing, enable_campaigns; max_items_per_change and max_search_results set maxItems and maximum on tool schemas

Skills, presentation extensions, and delegates are prompt bytes too. Gate lexicons, guardrail limits, memory settings, the latency knobs (eager_tool_dispatch, rolling_conversation_cache, eager_partial_frames, close_on_presentation), cart caps, and compact_history_above_tokens are not; tests/test_role_registries.py asserts it for each.

What breaks a hit

  • Anything per request in the static block or the tool list: a name, a cart count, a page, a clock, a request id. The context block takes the first four; a request id belongs nowhere.
  • A set or dict iterated into prompt text or a schema without sorting.
  • tools[] membership decided per request (a flag read per request instead of once at construction).
  • Rebuilding the static text or the tool list inside the turn instead of in the constructor.
  • Persisting the rolling marker into the stored conversation, which then gains one marker per turn.
  • Toggling a (prompt) field, a skill, or an extension on a running deployment; each is a redeploy.
  • A prompt variant chosen per request instead of per deployment.
  • A prefix under the model's minimum cacheable length, which writes nothing.
  • A forced tool_choice (grounding first, none last) misses the messages span only; the loops skip the marker there.

How to verify

  • turn_complete carries usage (usage_totals in commerce_common/turn.py, the turn's calls summed) and elapsed_ms; every call also logs one line with the same counters and its own time (log_model_call, on shopping_agent_runtime.orchestrator and merchant_agent_runtime.orchestrator). Zero cache reads on the second turn of a conversation means the prefix changed. The counters worth charting per config version are cache reads as a share of input, elapsed_ms, rounds per turn, and blocked tool_result events by gate.
  • tests/test_role_registries.py builds each role's prompt and tools twice and compares bytes, checks the cache marks, and checks that non-prompt settings change nothing; commerce-common/tests/test_prompt_assembly.py covers the block builders, the context clock, the rolling marker, and the skip cases; tests/test_turn_loop.py pins both loops to the same blocks, marker, and clean history. A deployment's tests carry the same three checks.
  • In a deployed environment, run one three-turn conversation and read the second and third turns' usage; a proxy or retry layer that rewrites requests shows up here and nowhere else.

Frequently asked questions

What does the Commerce Prompt Caching AI skill do?

The reference agents' cache-stable request assembly, covering the static system and per-request context split, the fixed tool list, the rolling conversation breakpoint, which config fields are prompt bytes, and verification. Load when writing or reviewing a commerce agent's prompt assembly, when cache reads are zero, or when a turn's latency or cost is the question.

Why use Commerce Prompt Caching on TypingMind?

Because you install it once and use it with any model. Commerce Prompt Caching is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Commerce Prompt Caching in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/commerce-agents/tree/main/plugins/commerce-builder/skills/commerce-prompt-caching. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Commerce Prompt Caching?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Commerce Prompt Caching?

As many as you like. As long as a model supports skills, you can use Commerce Prompt Caching with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Commerce Prompt Caching AI skill free?

Yes. It is published on GitHub by anthropics under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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