aider-1h-ttl
Aider uses 5min TTL by default and works around long pauses with keepalive pings. Wire up the 1h TTL beta instead.
Continue's Gemini provider doesn't use the cachedContents API at all. Add explicit caching for sessions over the minimum token threshold.
$ npx -y skills add OnlyTerp/prompt-cache-skills --skill continue-gemini-explicit --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/continue-gemini-explicitContext preview
The summary Claude sees to decide when to auto-load this skill.
Continue's Gemini provider doesn't use the cachedContents API at all. Add explicit caching for sessions over the minimum token threshold.
name: continue-gemini-explicit description: Continue's Gemini provider doesn't use the cachedContents API at all. Add explicit caching for sessions over the minimum token threshold. target_harness: Continue target_repo: continuedev/continue target_files: - packages/openai-adapters/src/apis/Gemini.ts target_commit: main (last push 2026-05-26) estimated_savings: 75% input discount on Gemini Pro / 50% on Flash for sessions above the min-token threshold
`packages/openai-adapters/src/apis/Gemini.ts` in `continuedev/continue`.
The Gemini provider class does not use the `cachedContents` API at all. Gemini 2.5+ has implicit caching that fires automatically for byte-stable prefixes (free, best-effort), but explicit `cachedContents` gives guaranteed cost reduction (0.25x input price on Pro) and controllable TTL. Continue leaves this entire mechanism unused.
Result: long-running Gemini sessions with the same system prompt + tools (the exact use case caching is for) get only implicit-cache luck, not the guaranteed 75% discount.
Add a `_maybeCreateCache` helper and reuse the cached content across calls within a session:
--- a/packages/openai-adapters/src/apis/Gemini.ts
+++ b/packages/openai-adapters/src/apis/Gemini.ts
@@
export class GeminiApi implements BaseLlmApi {
apiBase: string = "https://generativelanguage.googleapis.com/v1beta/";
private genAI: GoogleGenAI;
+ private cacheName: string | null = null;
+ private cacheSystemHash: string | null = null;
@@
+ private estimateTokens(text: string): number {
+ // Conservative ~4 chars/token; fine for threshold gating
+ return Math.ceil(text.length / 4);
+ }
+
+ private async _maybeCreateCache(
+ systemInstruction: string,
+ tools?: any[],
+ ): Promise<string | null> {
+ if (!systemInstruction) return null;
+
+ // Min tokens: 4096 for Pro variants, 1024 for Flash
+ const isPro = this.config.model.toLowerCase().includes("pro");
+ const minTokens = isPro ? 4096 : 1024;
+ if (this.estimateTokens(systemInstruction) < minTokens) return null;
+
+ // Dedupe: only create a cache if system has changed
+ const hash = crypto.createHash("sha256")
+ .update(systemInstruction + JSON.stringify(tools ?? []))
+ .digest("hex").slice(0, 16);
+ if (this.cacheName && this.cacheSystemHash === hash) {
+ return this.cacheName;
+ }
+
+ try {
+ const cache = await this.genAI.caches.create({
+ model: `models/${this.config.model}`,
+ config: {
+ systemInstruction,
+ tools,
+ ttl: "3600s", // 1 hour
+ },
+ });
+ this.cacheName = cache.name ?? null;
+ this.cacheSystemHash = hash;
+ return this.cacheName;
+ } catch (e) {
+ // Cache creation can fail (e.g. content under min). Fall back
+ // to implicit caching by returning null.
+ return null;
+ }
+ }Then in the `generateContent` call path:
+ const cachedContent = await this._maybeCreateCache(
+ systemInstruction,
+ tools,
+ );
+
const response = await this.genAI.models.generateContent({
model: this.config.model,
contents: messages,
+ config: cachedContent
+ ? { cachedContent }
+ : undefined,
});1. Start a Continue chat with `gemini-2.5-pro` or `gemini-3-pro-preview` and a system prompt over 4096 tokens (or load a big AGENTS.md). 2. Capture wire. 3. First call: should see a `POST /v1beta/cachedContents` create call followed by a `generateContent` call referencing the cache name. 4. Second call: only `generateContent`, reusing the same `cachedContent` reference. 5. Inspect response `usageMetadata.cachedContentTokenCount` — should be > 0 on both calls.
Gemini has two caching paths: implicit (automatic, free, best-effort) and explicit (`cachedContents` API, guaranteed discount, configurable TTL, costs storage per hour). For agent loops with a stable large system prompt, explicit is strictly better — you trade pennies of storage for dollars of discount.
Minimums:
Below threshold, `caches.create()` returns 400 — the fallback to implicit (return null) handles this gracefully.
See [docs/concepts/gemini.md](../../docs/concepts/gemini.md). Full audit: [audits/continue.md](../../audits/continue.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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