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Aider 1h Ttl

Community
OnlyTerp
aider-1h-ttl

Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.

Overview

PublisherOnlyTerp
Repositoryprompt-cache-skills
Skill nameaider-1h-ttl
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 Aider 1h Ttl 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/aider-1h-ttl
  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/aider-1h-ttl .claude/skills/aider-1h-ttl
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Aider 1h Ttl 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 Aider 1h Ttl 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 Aider 1h Ttl 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.

Aider: enable Anthropic 1-hour cache TTL

Target

aider/coders/chat_chunks.py, aider/sendchat.py, aider/args.py in Aider-AI/aider.

Symptom

Aider uses the 5-minute default cache TTL. For chat-style sessions where the user reads code, thinks for 10 minutes, then types — common flow — the cache expires between turns. The current workaround is --cache-keepalive-pings N which sends a trivial request every 5 minutes to refresh the cache, but you pay for those pings.

Anthropic's extended-cache-ttl-2025-04-11 beta extends the TTL to 1 hour for a 2x write premium (vs 1.25x for 5min). Break-even is ~4 reads, which any active chat session clears.

Fix (three parts)

Part A — accept the flag

diff
--- a/aider/args.py
+++ b/aider/args.py
@@
+    group.add_argument(
+        "--extended-cache-ttl",
+        action=argparse.BooleanOptionalAction,
+        default=False,
+        help="Use Anthropic's 1-hour cache TTL (beta). "
+             "Costs 2x to write vs 1.25x for default 5min, "
+             "but preserves cache across long thinking pauses. "
+             "Pays for itself after ~4 cache reads.",
+    )

Part B — thread it through the message builder

diff
--- a/aider/coders/chat_chunks.py
+++ b/aider/coders/chat_chunks.py
@@
-    def add_cache_control(self, msg):
-        msg["cache_control"] = {"type": "ephemeral"}
+    def add_cache_control(self, msg, extended_ttl: bool = False):
+        if extended_ttl:
+            msg["cache_control"] = {"type": "ephemeral", "ttl": "1h"}
+        else:
+            msg["cache_control"] = {"type": "ephemeral"}
@@
-    def add_cache_control_headers(self):
+    def add_cache_control_headers(self, extended_ttl: bool = False):
         for chunk in self.cacheable_chunks():
             last = chunk[-1]
-            self.add_cache_control(last)
+            self.add_cache_control(last, extended_ttl=extended_ttl)

Part C — set the beta header when active

diff
--- a/aider/sendchat.py
+++ b/aider/sendchat.py
@@
-def send_completion(model_name, messages, ...):
+def send_completion(model_name, messages, *, extended_cache_ttl=False, ...):
@@
     extra_headers = {}
+    if extended_cache_ttl:
+        extra_headers["anthropic-beta"] = "extended-cache-ttl-2025-04-11"
     kwargs = {
         "model": model_name,
         "messages": messages,
+        "extra_headers": extra_headers,
         ...
     }
     return litellm.completion(**kwargs)

Wire args.extended_cache_ttl from the Coder constructor through format_messages() and send_completion().

Verify

  1. Run with --cache-prompts --extended-cache-ttl --no-stream.
  2. Capture wire.
  3. Request headers should include anthropic-beta: extended-cache-ttl-2025-04-11.
  4. Request body cache_control blocks should be {"type": "ephemeral", "ttl": "1h"}.
  5. Wait 6 minutes. Send another prompt.
  6. Response usage.cache_read_input_tokens should still cover the prefix — proof the cache survived the >5min gap.

Background

The 1h TTL beta exists for exactly this use case: human-in-the-loop sessions with long thinking gaps. Trade-off: 2x write cost vs 1.25x. Worth it whenever the session has ≥4 turns within the hour.

Aider users who already use --cache-keepalive-pings 5 are paying for both: keepalive pings AND repeated cache writes. The 1h TTL beats this on both fronts.

See docs/concepts/anthropic.md § "TTL" and "Pricing".

Full audit: audits/aider.md.

Frequently asked questions

What does the Aider 1h Ttl AI skill do?

Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.

Why use Aider 1h Ttl on TypingMind?

Because you install it once and use it with any model. Aider 1h Ttl 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 Aider 1h Ttl in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OnlyTerp/prompt-cache-skills/tree/main/skills/aider-1h-ttl. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Aider 1h Ttl?

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 Aider 1h Ttl?

As many as you like. As long as a model supports skills, you can use Aider 1h Ttl with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Aider 1h Ttl 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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