Continue Enable Defaults
OnlyTerp/prompt-cache-skills
Continue's prompt caching is opt-in via config and off by default.
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills prompt-caching --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/prompt-caching .claude/skills/prompt-caching && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .claude/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-cachingType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills prompt-caching --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/prompt-caching .agents/skills/prompt-caching && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .agents/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills prompt-caching --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/prompt-caching .cursor/skills/prompt-caching && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .cursor/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/prompt-caching--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills prompt-caching --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/prompt-caching .gemini/skills/prompt-caching && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .gemini/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills prompt-cachingInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/prompt-caching .github/skills/prompt-caching && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .github/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills prompt-caching --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/prompt-caching .opencode/skills/prompt-caching && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "prompt-caching" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/prompt-caching into .opencode/skills/prompt-caching/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "prompt-caching", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
prompt-cachingCaching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
Prompt Caching is an agent skill from sickn33/agentic-awesome-skills. Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering LLM cost and token optimization and Caching. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.
Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Prompt Caching loads about 3.4k tokens when it runs. Until then it costs about 35 tokens; SKILL.md has 1,186 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its MIT licence (© sickn33). 1,186 words, ~3,424 tokens.
.claude/skills/prompt-caching/SKILL.md (or your agent's skills folder).Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation)
upstash-redis)Use Claude's native prompt caching for repeated prefixes
When to use: Using Claude API with stable system prompts or context
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic();
// Cache the stable parts of your prompt async function queryWithCaching(userQuery: string) { const response = await client.messages.create({ model: "claude-sonnet-4-20250514", max_tokens: 1024, system: [ { type: "text", text: LONG_SYSTEM_PROMPT, // Your detailed instructions cache_control: { type: "ephemeral" } // Cache this! }, { type: "text", text: KNOWLEDGE_BASE, // Large static context cache_control: { type: "ephemeral" } } ], messages: [ { role: "user", content: userQuery } // Dynamic part ] });
// Check cache usage
console.log(`Cache read: ${response.usage.cache_read_input_tokens}`);
console.log(`Cache write: ${response.usage.cache_creation_input_tokens}`);
return response;
}
// Cost savings: 90% reduction on cached tokens // Latency savings: Up to 2x faster
Cache full LLM responses for identical or similar queries
When to use: Same queries asked repeatedly
import { createHash } from 'crypto'; import Redis from 'ioredis';
const redis = new Redis(process.env.REDIS_URL); // Serverless/edge alternative without a persistent connection: // import { Redis } from '@upstash/redis'; const redis = Redis.fromEnv(); // then use redis.set(key, value, { ex: ttl })
class ResponseCache { private ttl = 3600; // 1 hour default
// Exact match caching
async getCached(prompt: string): Promise<string | null> {
const key = this.hashPrompt(prompt);
return await redis.get(`response:${key}`);
}
async setCached(prompt: string, response: string): Promise<void> {
const key = this.hashPrompt(prompt);
await redis.set(`response:${key}`, response, 'EX', this.ttl);
}
private hashPrompt(prompt: string): string {
return createHash('sha256').update(prompt).digest('hex');
}
// Semantic similarity caching
async getSemanticallySimilar(
prompt: string,
threshold: number = 0.95
): Promise<string | null> {
const embedding = await embed(prompt);
const similar = await this.vectorCache.search(embedding, 1);
if (similar.length && similar[0].similarity > threshold) {
return await redis.get(`response:${similar[0].id}`);
}
return null;
}
// Temperature-aware caching
async getCachedWithParams(
prompt: string,
params: { temperature: number; model: string }
): Promise<string | null> {
// Only cache low-temperature responses
if (params.temperature > 0.5) return null;
const key = this.hashPrompt(
`${prompt}|${params.model}|${params.temperature}`
);
return await redis.get(`response:${key}`);
}
}
Pre-cache documents in prompt instead of RAG retrieval
When to use: Document corpus is stable and fits in context
// CAG: Pre-compute document context, cache in prompt // Better than RAG when: // - Documents are stable // - Total fits in context window // - Latency is critical
class CAGSystem { private cachedContext: string | null = null; private lastUpdate: number = 0;
async buildCachedContext(documents: Document[]): Promise<void> {
// Pre-process and format documents
const formatted = documents.map(d =>
`## ${d.title}\n${d.content}`
).join('\n\n');
// Store with timestamp
this.cachedContext = formatted;
this.lastUpdate = Date.now();
}
async query(userQuery: string): Promise<string> {
// Use cached context directly in prompt
const response = await client.messages.create({
model: "claude-sonnet-4-20250514",
max_tokens: 1024,
system: [
{
type: "text",
text: "You are a helpful assistant with access to the following documentation.",
cache_control: { type: "ephemeral" }
},
{
type: "text",
text: this.cachedContext!, // Pre-cached docs
cache_control: { type: "ephemeral" }
}
],
messages: [{ role: "user", content: userQuery }]
});
return response.content[0].text;
}
// Periodic refresh
async refreshIfNeeded(documents: Document[]): Promise<void> {
const stale = Date.now() - this.lastUpdate > 3600000; // 1 hour
if (stale) {
await this.buildCachedContext(documents);
}
}
}
// CAG vs RAG decision matrix: // | Factor | CAG Better | RAG Better | // |------------------|------------|------------| // | Corpus size | < 100K tokens | > 100K tokens | // | Update frequency | Low | High | // | Latency needs | Critical | Flexible | // | Query specificity| General | Specific |
Severity: HIGH
Situation: Slow response when cache miss, slower than no caching
Symptoms:
Why this breaks: Cache check adds latency. Cache write adds more latency. Miss + overhead > no caching.
Recommended fix:
// Optimize for cache misses, not just hits
class OptimizedCache {
async queryWithCache(prompt: string): Promise<string> {
const cacheKey = this.hash(prompt);
// Non-blocking cache check
const cachedPromise = this.cache.get(cacheKey);
const llmPromise = this.queryLLM(prompt);
// Race: use cache if available before LLM returns
const cached = await Promise.race([
cachedPromise,
sleep(50).then(() => null) // 50ms cache timeout
]);
if (cached) {
// Cancel LLM request if possible
return cached;
}
// Cache miss: continue with LLM
const response = await llmPromise;
// Async cache write (don't block response)
this.cache.set(cacheKey, response).catch(console.error);
return response;
}
}
// Alternative: Probabilistic caching // Only cache if query matches known high-frequency patterns class SelectiveCache { private patterns: Map<string, number> = new Map();
shouldCache(prompt: string): boolean {
const pattern = this.extractPattern(prompt);
const frequency = this.patterns.get(pattern) || 0;
// Only cache high-frequency patterns
return frequency > 10;
}
recordQuery(prompt: string): void {
const pattern = this.extractPattern(prompt);
this.patterns.set(pattern, (this.patterns.get(pattern) || 0) + 1);
}
}
Severity: HIGH
Situation: Users get outdated or wrong information from cache
Symptoms:
Why this breaks: Source data changed. No cache invalidation. Long TTLs for dynamic data.
Recommended fix:
// Implement proper cache invalidation
class InvalidatingCache { // Version-based invalidation private cacheVersion = 1;
getCacheKey(prompt: string): string {
return `v${this.cacheVersion}:${this.hash(prompt)}`;
}
invalidateAll(): void {
this.cacheVersion++;
// Old keys automatically become orphaned
}
// Content-hash invalidation
async setWithContentHash(
key: string,
response: string,
sourceContent: string
): Promise<void> {
const contentHash = this.hash(sourceContent);
await this.cache.set(key, {
response,
contentHash,
timestamp: Date.now()
});
}
async getIfValid(
key: string,
currentSourceContent: string
): Promise<string | null> {
const cached = await this.cache.get(key);
if (!cached) return null;
// Check if source content changed
const currentHash = this.hash(currentSourceContent);
if (cached.contentHash !== currentHash) {
await this.cache.delete(key);
return null;
}
return cached.response;
}
// Event-based invalidation
onSourceUpdate(sourceId: string): void {
// Invalidate all caches that used this source
this.invalidateByTag(`source:${sourceId}`);
}
}
Severity: MEDIUM
Situation: Cache misses despite similar prompts
Symptoms:
Why this breaks: Anthropic caching requires exact prefix match. Timestamps or dynamic content in prefix. Different message order.
Recommended fix:
// Structure prompts for optimal caching
class CacheOptimizedPrompts {
// WRONG: Dynamic content in cached prefix
buildPromptBad(query: string): SystemMessage[] {
return [
{
type: "text",
text: You are helpful. Current time: ${new Date()}, // BREAKS CACHE!
cache_control: { type: "ephemeral" }
}
];
}
// RIGHT: Static prefix, dynamic at end
buildPromptGood(query: string): SystemMessage[] {
return [
{
type: "text",
text: STATIC_SYSTEM_PROMPT, // Never changes
cache_control: { type: "ephemeral" }
},
{
type: "text",
text: STATIC_KNOWLEDGE_BASE, // Rarely changes
cache_control: { type: "ephemeral" }
}
// Dynamic content goes in messages, NOT system
];
}
// Prefix ordering matters
buildWithConsistentOrder(components: string[]): SystemMessage[] {
// Sort components for consistent ordering
const sorted = [...components].sort();
return sorted.map((c, i) => ({
type: "text",
text: c,
cache_control: i === sorted.length - 1
? { type: "ephemeral" }
: undefined // Only cache the full prefix
}));
}
}
Severity: WARNING
Message: Caching with high temperature. Responses are non-deterministic.
Fix action: Only cache responses with temperature <= 0.5
Severity: WARNING
Message: Cache without TTL. May serve stale data indefinitely.
Fix action: Set appropriate TTL based on data freshness requirements
Severity: WARNING
Message: Dynamic content in cached prefix. Will cause cache misses.
Fix action: Move dynamic content outside of cache_control blocks
Severity: INFO
Message: Cache without hit/miss tracking. Can't measure effectiveness.
Fix action: Add cache hit/miss metrics and logging
Skills: prompt-caching, context-window-management, rag-implementation
Workflow:
1. Analyze query patterns
2. Implement prompt caching for stable prefixes
3. Add response caching for frequent queries
4. Consider CAG for stable document sets
5. Monitor and optimize hit ratesWorks well with: context-window-management, rag-implementation, conversation-memory
© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in skills/prompt-caching of sickn33/agentic-awesome-skills.
Open the folder on GitHubat commit 680176d
We found 11 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 9, 2026.
Prompt Caching next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Prompt Caching this skillsickn33/agentic-awesome-skills | 47k | 2 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Continue Enable DefaultsOnlyTerp/prompt-cache-skills | 114 | — | ~977 | Automated safety check: Pass | Custom licence | |
| Dt Obs GenaiDynatrace/dynatrace-for-ai | 162 | — | ~4.5k | Automated safety check: Pass | Apache-2.0 | |
| Anth Performance Tuningjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.9k | Automated safety check: Pass | MIT | |
| LLM Cost OptimizationBagelHole/DevOps-Security-Agent-Skills | 1.1k | — | ~2.2k | Automated safety check: Pass | MIT | |
| Context Engineering Reviewmohitagw15856/pm-claude-skills | 1.4k | — | ~1.4k | Automated safety check: Pass | MIT |
OnlyTerp/prompt-cache-skills
Continue's prompt caching is opt-in via config and off by default.
Dynatrace/dynatrace-for-ai
Analyze & debug GenAI/LLM apps: token cost & caching by prompt, model & provider; latency/errors; agent & tool loops/failures; conversations; guardrails; evaluations; OpenTelemetry/dt-evals setup.
jeremylongshore/tons-of-skills-marketplace
Optimize Claude API performance with prompt caching, model selection, streaming, and latency reduction techniques.
BagelHole/DevOps-Security-Agent-Skills
Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies.
mohitagw15856/pm-claude-skills
Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself.
mohitagw15856/pm-claude-skills
Model the cost and latency of an LLM feature before it ships and surprises the bill.
sickn33/agentic-awesome-skills
Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.
sickn33/agentic-awesome-skills
Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.
sickn33/agentic-awesome-skills
Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.
sickn33/agentic-awesome-skills
Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.
sickn33/agentic-awesome-skills
Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.
sickn33/agentic-awesome-skills
Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.
Categories
Caching strategies for LLM prompts including Anthropic prompt caching, response caching, and CAG (Cache Augmented Generation). Prompt Caching is an agent skill from sickn33/agentic-awesome-skills.
Prompt Caching fits situations like: tasks that involve LLM cost and token optimization; tasks that involve Caching.
Run `npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a claude-code`. Or copy the skill folder (skills/prompt-caching in sickn33/agentic-awesome-skills) into .claude/skills/prompt-caching in your project. Claude Code loads it when a task matches its description.
Run `npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a codex`. Or copy the skill folder (skills/prompt-caching in sickn33/agentic-awesome-skills) into .agents/skills/prompt-caching in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill prompt-caching -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/prompt-caching, .gemini/skills/prompt-caching, .github/skills/prompt-caching and .opencode/skills/prompt-caching in your project.
SKILL.md names no scripts, command-line tools or credentials: Prompt Caching is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Prompt Caching is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.4k tokens (SKILL.md is roughly 14k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Prompt Caching: Continue Enable Defaults (OnlyTerp/prompt-cache-skills, 114 stars), Dt Obs Genai (Dynatrace/dynatrace-for-ai, 162 stars), Anth Performance Tuning (jeremylongshore/tons-of-skills-marketplace, 2.8k stars) and LLM Cost Optimization (BagelHole/DevOps-Security-Agent-Skills, 1.1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.