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Warm Cache

Community
hegemonart
warm-cache

Pre-warms Anthropic's 5-min prompt cache across all agents that import reference/shared-preamble.md. Issues one no-op Haiku ping per agent so the identical preamble-first prefix lives in Anthropic's cache before a design sprint starts. Layer A of the D-08 two-layer cache. Run this once at the start of a {{command_prefix}}* sprint for ~90% input-cost savings on the first repeated spawn.

Overview

Publisherhegemonart
Repositoryget-design-done
Skill namewarm-cache
Stars
3
Forks
1
Bundled files
Instructions only
LicenseMIT
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 hegemonart on GitHub. Read the source before you install it.

Installation

Install the Warm Cache 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.
    https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/warm-cache
  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/hegemonart/get-design-done.git /tmp/get-design-done
mkdir -p .claude/skills
cp -r /tmp/get-design-done/scripts/skill-templates/warm-cache .claude/skills/warm-cache
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Warm Cache 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 Warm Cache 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 Warm Cache 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.

warm-cache

Role

You are the Layer A cache primer. You enumerate the agent roster by scanning agents/*.md for files that import reference/shared-preamble.md at the top of their body, then issue a single no-op Haiku ping per agent whose sole purpose is to plant the agent's preamble-first prefix into Anthropic's 5-minute prompt cache. You do not do real work. You do not write files. You do not touch .design/cache-manifest.json (that's Layer B, owned by gdd-cache-manager).

Invocation Contract

  • Command form: {{command_prefix}}warm-cache - warms all agents that import the shared preamble.
  • With filter: {{command_prefix}}warm-cache --agents design-verifier,design-planner,design-integration-checker - warms only the named agents (comma-separated, no spaces, matches agent file basename without .md).
  • Output: a single markdown summary to stdout -
    ## Warm-cache complete
    - Agents warmed: 14
    - Skipped (no shared preamble import): 3
    - Duration: 4.2s
    - Next 5 min: repeated spawns of these agents pay cached_input_per_1m rate
  • Exit code: 0 on success, 1 if the shared preamble file is missing (warn and continue in that case - do not fail the sprint).

Step-by-step Flow

Step 1: locate shared preamble

Check reference/shared-preamble.md exists. If missing, print warm-cache: reference/shared-preamble.md not found — Layer A cache priming requires Plan 10.1-04 to land first. Skipping. and exit 0.

Step 2: enumerate candidate agents

  • Glob agents/*.md excluding agents/README.md.
  • For each agent file, grep for the literal string @reference/shared-preamble.md in the first 40 lines of the body (where the import directive lives per D-17).
  • Keep only files that match.
  • If --agents flag supplied, intersect the enumerated list with the flag-supplied comma-list. Report filtered-out agents in the output summary.

Step 3: issue one no-op Haiku ping per agent

For each kept agent, spawn the agent at tier haiku with an input payload designed to be the smallest possible valid invocation:

No-op warm: acknowledge and return "ok". Do not read files. Do not write files. Do not emit anything beyond the two characters "ok".

Spawns run serially - parallelism buys nothing here because Anthropic's prompt cache keys on prompt prefix, not on concurrent calls. Swallow spawn errors per agent (log and continue) - a single broken agent must not abort the sprint.

Step 4: emit summary

Print the markdown summary described in the Invocation Contract.

Concrete Command Example

Full + filtered command-output examples live in ./../cache-manager/cache-policy.md#concrete-warm-cache-command-examples-layer-a so this skill stays close to its step-by-step flow rather than its sample output.

Integration Points

  • Pre-sprint: {{command_prefix}}warm-cache is the recommended first line of a {{command_prefix}}discover, {{command_prefix}}plan, or {{command_prefix}}verify sprint. Users type it before the real command, or an orchestrator-level wrapper runs it automatically if agent-metrics.json (Plan 10.1-05) indicates the last sprint was > 5 min ago.
  • reference/shared-preamble.md (authored in Plan 10.1-04) is the essential file for this command - agents import it first per D-17, which makes the first N tokens of every agent's rendered system prompt identical, which is what Anthropic's prompt cache keys on.
  • No interaction with hooks/budget-enforcer.ts - the hook is a PreToolUse gate; warm-cache runs as an ordinary Agent tool call itself and is subject to the hook (each no-op Haiku ping is budgeted and logged like any other spawn). This is intentional: warm-cache's own telemetry rows in .design/telemetry/costs.jsonl are the evidence that cache priming happened.

Cost Model

Full per-ping math, total-cost arithmetic for a 14-agent warm, and payback calculation versus a downstream Opus spawn live in ./../cache-manager/cache-policy.md#cost-model-layer-a. Top-line: a full warm is ~$0.001 and pays back ~500× on the first repeated planner spawn.

Failure Modes

  • Shared preamble missing → print warning, exit 0. Sprint continues without Layer A priming.
  • Individual agent spawn fails → log, continue to next agent.
  • Budget cap hit during warm-cache (hypothetical - the total cost is trivial) → hook blocks per D-02, warm-cache surfaces the error in the summary and exits 0. User can raise cap or proceed without priming.

Non-Goals

  • Does not touch Layer B (.design/cache-manifest.json). That is the cache-manager skill's territory.
  • Does not attempt to bypass or accelerate Anthropic's cache beyond issuing real API calls at minimal cost. There is no API to "pre-populate" the cache other than by issuing identical prompts.
  • Does not persist between Anthropic's 5-min TTL. After 5 minutes of inactivity, a re-warm is needed.

Frequently asked questions

What does the Warm Cache AI skill do?

Pre-warms Anthropic's 5-min prompt cache across all agents that import reference/shared-preamble.md. Issues one no-op Haiku ping per agent so the identical preamble-first prefix lives in Anthropic's cache before a design sprint starts. Layer A of the D-08 two-layer cache. Run this once at the start of a {{command_prefix}}* sprint for ~90% input-cost savings on the first repeated spawn.

Why use Warm Cache on TypingMind?

Because you install it once and use it with any model. Warm Cache 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 Warm Cache in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hegemonart/get-design-done/tree/main/scripts/skill-templates/warm-cache. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Warm Cache?

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 Warm Cache?

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

Is the Warm Cache AI skill free?

Yes. It is published on GitHub by hegemonart under the MIT 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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