aider-1h-ttl
Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.
Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero.
$ npx -y skills add OnlyTerp/prompt-cache-skills --skill cline-openai-cache-key --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/cline-openai-cache-keyContext preview
The summary Claude sees to decide when to auto-load this skill.
Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero.
name: cline-openai-cache-key description: Cline OpenAI native provider sends no prompt_cache_key. Add a stable per-task key so cached_tokens stops being zero. target_harness: Cline target_repo: cline/cline target_files: - src/core/api/providers/openai-native.ts target_commit: 65e9727c (verify current at apply time) estimated_savings: 50-90% input discount on OpenAI calls (currently 0%)
`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
Cline's OpenAI native provider only READS cache stats from responses, it never enables caching:
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.
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.
--- 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.
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:
5. Inspect responses:
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](../../docs/gotchas.md) #9b for the full explanation, and [docs/concepts/openai.md](../../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](../../audits/codex-cli.md).
Full audit: [audits/cline.md](../../audits/cline.md).
Drop-in prompt-caching fixes for the LLM agent harness you use. Point your AI coding agent at this repo and it ships the patches.
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