SkillVaultskills Browse all 500 skills

Performance · Version 1.5.0 · Reviewed 2026-08-02

Caching Strategy Agent

Locate and remove the dominant bottleneck in layer selection and invalidation design with evidence, explicit trade-offs, and a verification plan.

4 method steps 6 documented failure modes 5 diagnostic checks 7 quality gates

Chooses caching layers, invalidation strategy, and key design based on read/write patterns and staleness tolerance.

₹99 one-time

Get this skill archive

What this skill helps you do

  • Layer selection
  • Invalidation design
  • Key design

How Caching Strategy Agent works

You provide

Prompts, model versions, evaluation data, and failures

It inspects

Failure class and context sufficiency for layer selection

It decides

A invalidation design change with one variable moved

You verify

Pass rate per case class against a pinned baseline

What it checks first

Caching Strategy Skill chooses caching layers, invalidation strategy, and key design based on read/write patterns and staleness tolerance. Use it when the work involves Layer selection, Invalidation design, Key design.

  1. Whether the failure is systematic across a class of inputs or random, which separates a capability gap from a sampling issue.
  2. Whether evaluation data overlaps training or prompt-development data, which invalidates the measurement.
  3. Token distribution of inputs and outputs, since cost and latency are driven by the tail, not the mean.
  4. Whether the system has a defined behavior for low confidence, or always produces an answer.
  5. Version pinning across model, prompt, retrieval, and tools, because an unpinned component makes regressions unattributable.

Failure modes it recognizes

  • Silent quality regression after a provider updates a model behind an unversioned alias.
  • Evaluation overfitting where the prompt was tuned on the same examples used to score it.
  • Cost and latency dominated by a small number of very long inputs that were never in the test set.
  • Tool-calling loops where the model retries a failing tool without a bounded attempt budget.
  • Confident fabrication when context is insufficient because no refusal path was defined.
  • Distribution shift where production inputs diverge from the evaluation set over time.

Answers it will reject

  • Judging quality by reading a few outputs, which cannot detect a regression of a few percent.
  • Using a larger model to fix a problem caused by missing context, paying more for the same failure.
  • Fine-tuning before exhausting prompting and retrieval, which is slower to iterate and harder to reverse.
  • Using an LLM judge without validating the judge against human labels on the same rubric.

Decision rules it applies

  • Establish a labeled evaluation set and a baseline before changing anything; without a baseline there is no improvement, only change.
  • Pin every version and change one component at a time.
  • Define and test the refusal path explicitly; a system that cannot say "I do not know" will fabricate.
  • Budget latency and cost on p95 token counts, not averages.

Evidence it asks for

  • Score per input class (easy, hard, adversarial, no-answer) so aggregate scores cannot hide a broken class.
  • Log model version, prompt version, and retrieval version on every request for regression attribution.
  • Track p50 and p95 tokens and cost per successful task, not per call.

The method inside

  1. Define the measured baseline and user-visible target for layer selection.
  2. Attribute the dominant cost or latency mechanism affecting invalidation design.
  3. Rank key design changes by expected impact, confidence, effort, and regression risk.
  4. Validate under representative load and retain guardrail metrics that detect a shifted bottleneck.

Deliverables

  • Layer selection assessment
  • Invalidation design decision and action plan
  • Key design verification checklist

Evidence requirements

  • Profiles, traces, timings, resource metrics, and workload shape
  • Baseline and target percentile
  • Environment, concurrency, and payload details

Quality gates

  • Every material claim traces to supplied evidence or is labeled as a hypothesis.
  • The response follows the declared deliverable contract.
  • No execution, access, measurement, or verification is invented.
  • Secrets and personal data are redacted rather than repeated.
  • The user receives a concrete independent verification step.
  • The relevant failure modes in this domain were considered rather than only the reported symptom.
  • No listed anti-pattern was recommended as a solution.

Example task

Input

Where should we cache in this read-heavy product catalog, and how do we avoid serving stale prices?

Expected output

Your data changes hourly but is read thousands of times per second, so the edge is the highest-leverage layer. Cache keys must exclude user identity or you will serve one customer a price computed for another, which is a data-leak defect rather than a performance one...

Boundaries and compatibility

Ideal for

  • Layer selection: produce a decision or artifact grounded in supplied evidence.
  • Invalidation design: produce a decision or artifact grounded in supplied evidence.
  • Key design: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Optimizing without a baseline
  • Using averages where tail latency determines experience

Agent compatibility

  • GitHub Copilot custom agents
  • Claude Agent Skills / SKILL.md
  • Any instruction-following chat model

Tool policy: Advisory by default. No tools are assumed. If the host provides tools, use read-only evidence gathering unless the user explicitly approves a scoped write or execution action.