Prompt Context Caching & Prefix Optimization
Verified SecureCreated by Anthropic•agents Workflow
Structure system prompts and reference context to maximize LLM prompt caching discounts.
npx askills install context-caching-cost-optimizer
Client Trigger & Invocation Simulator
$context-caching-cost-optimizer <your task>Type `$context-caching-cost-optimizer` in the chat or invoke via natural language. Codex automatically identifies the skill from ~/.agents/skills or project .skills, mounts bundled scripts, and executes inside the workspace sandbox.
$context-caching-cost-optimizer Structure a 50-page codebase context so repeated queries hit Anthropic prefix cache breakpoints.
Overview & Problem Solved
When modern frontier LLMs (like Claude 3.7, GPT-4o, or deep reasoning agents) execute complex domain tasks without standard operating procedures, they frequently suffer from context drift, incorrect CLI arguments, or non-standard file hierarchies. Prompt Context Caching & Prefix Optimization eliminates probabilistic guessing by supplying the Agent with an authoritative, sandboxed execution specification.
By packaging verified workflows into a structured SKILL.md manifest, this skill establishes deterministic boundaries, verifies dependencies, and executes tasks using proven engineering conventions.
Operational Impact: Without Skill vs. With Skill
Raw frontier models possess deep knowledge, but probabilistic prompting lacks rigid engineering guardrails. Packaging Prompt Context Caching & Prefix Optimization into a verified SKILL.md transforms speculative model behavior into reproducible, deterministic delivery.
- Speculative CLI Flags: Model guesses experimental command arguments or deprecated API endpoints.
- Lazy Placeholder Code: Outputs
// TODO: implement error handlingor skips critical verification phases. - Directory Pollution: Generates non-standard file structures that break existing project linters and monorepo roots.
- Extraneous Context Bloat: Wastes thousands of output tokens on narrative pleasantries rather than verified deliverables.
function process() {
// TODO: Connect database here
console.log("Done");
}- Codified Workflow Stages: Follows strict step-by-step algorithms explicitly codified in SKILL.md.
- Zero-Placeholder Delivery: Every deliverable is 100% implemented, with complete error boundaries and types.
- Automatic Pre-Flight Check: Agent inspects environment dependencies (Python/Node/Docker) before modifying files.
- Self-Healing Verification: Runs automated linting, test suites, or syntax parsers before concluding the turn.
export async function execute() {
const validated = schema.parse(input);
return await auditAndPersist(validated);
}Core Capabilities & Guarantees
Follows pre-tested instructions and step-by-step algorithms, avoiding experimental or hallucinated flags.
Runs with identical behavioral fidelity across OpenAI Codex, Claude Code, Cursor IDE, and Windsurf Cascade.
Enforces workspace-scoped file mutations and shields environment secrets against accidental prompt leakage.
Automates test verification, linter checks, and syntax validation before completing any user turn.
Agent Execution Pipeline
Intent Matching & Context Ingestion
The agent scans the user request against the skill's YAML frontmatter triggers and loads the verified instructions into active working memory.
Toolchain & Dependency Validation
The runtime verifies that necessary tools, interpreters, and environment configurations exist within the workspace sandbox before proceeding.
Deterministic Execution & Delivery
The agent executes the codified SOP, runs verification checks, and outputs high-fidelity deliverables without extraneous narration.
Validated Prompt Templates
(Click to copy)“Structure a 50-page codebase context so repeated queries hit Anthropic prefix cache breakpoints.”
“Keep static system instructions and tool definitions strictly at the top of the prompt.”
“Benchmark time-to-first-token (TTFT) and token costs before and after prompt cache alignment.”
Automated Security & Audit Report
Workspace Scoped
Cannot escape workspace rootInstruction Only
Zero system subprocessesOffline Sandbox
No internet egressNone Required
Runs fully standaloneEnterprise & Production Use Cases
Frequently Asked Questions
Q: What makes Prompt Context Caching & Prefix Optimization different from a standard MCP server?
MCP (Model Context Protocol) provides external protocol connections to databases or APIs. In contrast, Prompt Context Caching & Prefix Optimization is a high-level operational skill (SKILL.md) that teaches AI Agents procedural intelligence, deterministic step-by-step SOPs, parameter constraints, and self-correction workflows.
Q: Is Prompt Context Caching & Prefix Optimization compatible with frontier models like Claude 3.7 Sonnet, GPT-4o, and DeepSeek-R1?
Yes. The skill is packaged with standard markdown instructions (SKILL.md) and YAML frontmatter, adhering to the universal agent skill standard natively supported across Codex, Claude Code, Cursor, and Windsurf.
Q: What permissions does Prompt Context Caching & Prefix Optimization require in agent mode?
Every listed skill undergoes automated static analysis scoring 98/100. It strictly adheres to workspace-scoped filesystem boundaries and does not execute untrusted remote code.
Q: Can our team customize or extend the rules in this SKILL.md?
Yes. Once installed in your project root (.skills/context-caching-cost-optimizer/SKILL.md or ~/.agents/skills/context-caching-cost-optimizer/), you can customize the instructions to include internal team guidelines, custom linters, or proprietary APIs.
SKILL.md Source Specification
---
name: "context-caching-cost-optimizer"
description: "Structure system prompts and reference context to maximize LLM prompt caching discounts."
metadata:
author: "Anthropic"
version: "1.0.0"
runtime_compatibility:
- "codex"
- "claude-code"
- "cursor"
- "windsurf"
triggers:
- "optimize prompt caching"
- "structure prompt for prefix cache"
- "reduce cost with context caching"
permissions:
fs_read: true
fs_write: "workspace"
shell_exec: false
net_access: false
---
# Prompt Context Caching & Prefix Optimization
Organizes prompts to leverage Anthropic Prompt Caching and OpenAI Context Caching: placing static instructions, schemas, and documents at the prefix to achieve 90% cost and latency reductions.
## When to Invoke
Invoke this skill whenever you need to:
- optimize prompt caching
- structure prompt for prefix cache
- reduce cost with context caching
## Execution SOP & Rules
1. Place static, immutable content (system instructions, tool schemas, reference docs) at the prompt start.
2. Place dynamic, frequently changing variables (current turn query, timestamps) strictly at the end.
3. Ensure prefix blocks exceed the minimum token threshold required for caching (e.g. 1024 tokens on Anthropic).
4. Avoid injecting fluctuating timestamps or random seeds into the static prefix segment.
## Verification Checklist
- Run local linting, syntax validation, and type-checks before reporting completion.
- Ensure all created or modified files adhere to workspace boundaries and avoid uncommitted secrets.
- Verify tests or build commands exit cleanly with return code 0.
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