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Cline Openai Cache Key

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OnlyTerp
cline-openai-cache-key

Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero.

Overview

PublisherOnlyTerp
Repositoryprompt-cache-skills
Skill namecline-openai-cache-key
Stars
116
Forks
10
Bundled files
Instructions only
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 OnlyTerp on GitHub. Read the source before you install it.

Installation

Install the Cline Openai Cache Key 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/OnlyTerp/prompt-cache-skills/tree/main/skills/cline-openai-cache-key
  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/OnlyTerp/prompt-cache-skills.git /tmp/prompt-cache-skills
mkdir -p .claude/skills
cp -r /tmp/prompt-cache-skills/skills/cline-openai-cache-key .claude/skills/cline-openai-cache-key
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cline Openai Cache Key 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 Cline Openai Cache Key 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 Cline Openai Cache Key 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.

Cline: add prompt_cache_key to OpenAI native provider

Target

src/core/api/providers/openai-native.ts in cline/cline.

Permalink: https://github.com/cline/cline/blob/65e9727c/src/core/api/providers/openai-native.ts

Symptom

Cline's OpenAI native provider only READS cache stats from responses, it never enables caching:

typescript
const cacheReadTokens = usage?.prompt_tokens_details?.cached_tokens || 0
const cacheWriteTokens = 0  // ← always 0

No prompt_cache_key set on the Responses API call, no work done to verify prefix stability. OpenAI's automatic prefix caching may still fire if the prefix happens to be byte-stable, but routing is random across pods so hit rates are low and unpredictable.

Open issue #554 ("OpenAI Prompt Caching appears not enabled?") tracks user-side confusion. PR #1156 attempted a partial fix but was closed without merging.

Fix

Set a stable prompt_cache_key derived from the task ID (or a hash of the system instructions if no task ID is available). NEVER use a per-request UUID — that's worse than no key.

diff
--- a/src/core/api/providers/openai-native.ts
+++ b/src/core/api/providers/openai-native.ts
@@
-  const response = await client.responses.create({
+  // Stable cache key: same task = same pod = warm cache.
+  // NEVER use uuid() — random keys force random pod routing and
+  // kill cache hits.
+  const cacheKey = this.options.taskId
+    || this.options.ulid
+    || crypto.createHash("sha256")
+         .update(systemPrompt)
+         .digest("hex")
+         .slice(0, 16)
+
+  const response = await client.responses.create({
     model: modelId,
     input: messages,
+    prompt_cache_key: `cline:${modelId}:${cacheKey}`,
     ...
   })

If the harness uses the legacy Chat Completions endpoint (client.chat.completions.create), prompt_cache_key isn't a valid field there. In that case the only knob is prefix stability — make sure the system prompt has no per-request timestamps, session IDs, or randomized content.

Verify

  1. Start mitmproxy capture as before.
  2. Configure Cline with an OpenAI model (gpt-5.4, gpt-5-codex, etc.).
  3. Send two identical prompts in the same task.
  4. Inspect captured requests:
    • prompt_cache_key field present and identical across both
    • System prompt bytes identical
  5. Inspect responses:
    • Turn 2 usage.prompt_tokens_details.cached_tokens > 0
    • Ratio cached_tokens / prompt_tokens ≥ 0.5 on a steady-state turn

Background

OpenAI's Responses API uses prompt_cache_key as a pod-routing hint. Stable key = same pod = warm cache. Per-request UUIDs are the #1 silent killer of OpenAI cache hits.

See docs/gotchas.md #9b for the full explanation, and docs/concepts/openai.md "The prompt_cache_key trick".

Reference implementation: Codex CLI uses session thread_id as the cache key and preserves it across compaction and sub-agents — see audits/codex-cli.md.

Full audit: audits/cline.md.

Frequently asked questions

What does the Cline Openai Cache Key AI skill do?

Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero.

Why use Cline Openai Cache Key on TypingMind?

Because you install it once and use it with any model. Cline Openai Cache Key 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 Cline Openai Cache Key in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OnlyTerp/prompt-cache-skills/tree/main/skills/cline-openai-cache-key. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Cline Openai Cache Key?

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 Cline Openai Cache Key?

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

Is the Cline Openai Cache Key AI skill free?

It is published on GitHub by OnlyTerp. Check the repository for licensing terms. 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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