Prompt Caching Strategy

Arrange reusable prompt content to improve cache reuse, then measure input cost and latency. Savings depend on model, platform, cache eligibility, writes, retention, reuse and output volume. Runtime cache hits and savings for this skill are unmeasured; the examples below validate arithmetic without provider calls.

When to Activate

Use for repeated API requests that share authorized instructions, tools or reference material. First identify the exact provider endpoint, model, service tier and caching mode. Consult its current documentation before selecting rates, minimum prefix length or retention. Caching does not reduce context-window use.

How It Works

Preserve a stable prefix

Keep reusable instructions, tool schemas and examples stable; place changing questions, timestamps and retrieved results after that content where the API allows. Respect the provider's serialization order: Claude uses tools, system, then messages. Matching prefixes need identical content, including tool schemas and relevant request settings. A changed prefix can prevent reuse from that point. A dynamic suffix may itself be cached later if reused unchanged.

Reusable prefix: approved tools, instructions, stable examples/reference
Changing suffix: current question, fresh retrieval results, request metadata

Choose only relevant context. Padding prompts or broadening access just to meet an eligibility threshold needs its own cost and quality justification.

Preserve user and workspace boundaries

Authorize content access before cache lookup or reuse. Scope application-managed cache handles and accounting keys to the approved user, workspace, provider account, model and content revision. Share only material explicitly approved for that scope. A cache key is a routing/accounting hint, not an authorization gate or a sandbox. Provider cache isolation does not replace application access checks. Invalidate application references when permissions or content change; follow provider retention/deletion controls. Avoid logging private prompt contents. Context selection, capabilities, sandbox enforcement and evidence remain independent of cache optimization.

Provider Requirements and Usage Evidence

The linked documentation is authoritative; endpoint and SDK versions can expose different usage field names. Record that version alongside counters.

Provider	Requirements to verify	Counters to record
OpenAI	Model-specific automatic/explicit modes, minimum length, retention and write/read prices; earlier and newer models differ	Responses usage.input_tokens_details.cached_tokens and, when present, cache_write_tokens; input and output totals. Chat Completions uses prompt_tokens_details.cached_tokens
Claude	Supported platform, model minimum, cache_control support and TTL; cache writes may cost more than ordinary input	input_tokens, cache_creation_input_tokens, cache_read_input_tokens, output_tokens; split writes by TTL when rates differ
Gemini	Model minimum and API: Interactions supports implicit caching; explicit cache objects require GenerateContent	Interactions usage.total_cached_tokens; GenerateContent usage_metadata cache/input/output counters plus explicit-cache token count and retained duration

Use OpenAI's prompt caching guide and pricing for the selected model. For Chat Completions, check the usage schema. Derive ordinary input from the total minus cached reads and any separately reported writes; do not count the same tokens twice.

Use Claude's prompt caching guide for current model/platform limits and TTL rates. Its input_tokens counts ordinary input separately from cache reads and writes. A short prefix can produce zero cache counters even when marked for caching. Do not assume every Claude model or hosted platform uses the same minimum or read price.

Use Gemini's caching guide, GenerateContent caching guide and pricing. Explicit-cache storage is charged by token count and retained duration, separately from reads, ordinary input and output. Implicit hits are not guaranteed. Verify billing categories for the chosen endpoint rather than copying another provider's rates.

Cost Comparison Calculator

Normalize actual usage into nonoverlapping categories and use prices in the same currency per million tokens. Split write categories when TTL, modality, tier or model prices differ. Storage uses million-token-hours; other applicable charges such as tools and grounding need their own lines. Include all warmup, miss, expiration, refresh and retry requests in the measured period.

baseline = (ordinary + written + read) * ordinary_input_rate / 1e6
           + output * output_rate / 1e6
actual   = ordinary * ordinary_input_rate / 1e6
           + written * cache_write_rate / 1e6
           + read * cache_read_rate / 1e6
           + output * output_rate / 1e6
           + storage_token_hours * storage_rate / 1e6
savings  = baseline - actual

This counterfactual holds prompt and output volumes fixed. For explicit-cache creation, include any separately billed creation input in the write category; use the endpoint's documented rate. Set nonexistent categories to zero. Compare actual before/after invoices separately if warmup requests or output differ. Never apply a cache discount to the total bill or to output tokens.

The executable calculator takes normalized counters, not raw provider responses:

function compareCacheCost(usage, rates) {
  const counts = ['uncached', 'written', 'read', 'output', 'storageTokenHours'];
  const prices = ['input', 'write', 'read', 'output', 'storage'];
  for (const [values, keys] of [[usage, counts], [rates, prices]]) {
    for (const key of keys) {
      if (!Number.isFinite(values[key]) || values[key] < 0) {
        throw new Error(`${key} must be a finite nonnegative number`);
      }
    }
  }
  const outputCost = usage.output * rates.output / 1e6;
  const storageCost = usage.storageTokenHours * rates.storage / 1e6;
  const baseline = (usage.uncached + usage.written + usage.read)
    * rates.input / 1e6 + outputCost;
  const actual = (usage.uncached * rates.input + usage.written * rates.write
    + usage.read * rates.read) / 1e6 + outputCost + storageCost;
  return { baseline, actual, savings: baseline - actual, outputCost, storageCost };
}

Examples

These are hypothetical prices and counts, not provider quotes or measured hits. Ten calls each contain a 10,000-token stable prefix, 2,000-token changing suffix and 2,000 output tokens. Assume one prefix write and nine full prefix reads.

Category	Tokens	Example rate per million	Cost
Ordinary input	20,000	$3.00	$0.0600
Cache write	10,000	$3.75	$0.0375
Cache read	90,000	$0.30	$0.0270
Output	20,000	$15.00	$0.3000
Storage	0 token-hours	$0.00	$0.0000

Baseline: 120,000 input tokens at $3 plus the same output = $0.6600. With caching: $0.4245, savings $0.2355. The write premium is included; the $0.3000 output bill remains unchanged. An output-heavy workload has the same absolute input savings under these assumptions, a smaller fraction of total cost.

If an explicit cache retains 10,000 tokens for two hours at a hypothetical $1 per million-token-hour, add $0.0200 storage. Actual cost becomes $0.4445. Use the selected Gemini endpoint's prices for a real estimate.

For a one-off call, that prefix write costs $0.0375 rather than $0.0300 ordinary input: $0.0075 more, with no read savings. For a 500-token prefix below a selected model's documented minimum, treat the request as uncached; claiming future hits without counters would overstate savings. Repeated expiry or changing prefixes can also turn expected savings into higher cost.

Verification Workflow

1. Capture eligible prefix size and model/platform requirements from current docs.
2. Run the offline example and edge cases with node tests/skills/prompt-caching-strategy.test.js in the ECC repository.
3. If provider traffic is authorized, compare matched requests and aggregate actual cached-token counts, all input categories, output, storage, latency and realized cost by approved user/workspace. Record misses and rewrites too.
4. Report cached reads divided by total input tokens as a token hit rate; report request hit rate separately. Keep denominators and sampling period explicit.
5. If no traffic was measured, label hit rate, latency improvement and realized savings unmeasured. Offline arithmetic establishes no runtime cache behavior.

Relationship to Other Skills

• prompt-optimizer improves prompt quality before optimizing reuse.
• context-budget identifies unnecessary context; caching still consumes it.
• cost-aware-llm-pipeline compares caching with routing, retries and other costs.