performance-workload-modeling git:20261011.7d9502bA

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---
name: performance-workload-modeling
description: Use this skill when you need to model realistic performance workload, traffic, and acceptance assumptions; triggers include performance workload modeling.
---

# Performance Workload Modeling

## When to Use

- Use this skill when you need to translate production traffic and business behavior into a reproducible performance-test workload model.
- Use it to review an existing plan, result, or evidence set and produce actionable improvements.
- Use it when context is incomplete but a bounded first pass is still valuable.

## Output Format Options

- Default to Markdown for review, execution, and incremental refinement.
- When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
- For machine-consumed output, confirm the schema, enums, and required fields first.

## How to Use

1. Read and follow `prompts/performance-workload-modeling.md`, including its input contract, execution rules, minimum coverage, and output order.
2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.

## Reference Files

- When calibrating domain outcomes, valid controls, or evidence gaps, read [references/domain-decision-example.md](references/domain-decision-example.md) for sanitized input, per-item oracles, and reproducible calculations.

- Always read `prompts/performance-workload-modeling.md`; it is the complete execution specification for this skill.
- For evaluation or regression, read `evals/eval.yaml` and the relevant cases under `evals/cases/`.
- Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them.

## Domain Execution Essentials

1. Describe each input with value, unit, timezone/window, source, and coverage. Requests/transactions/sessions/users are distinct; convert through transaction mix and requests per transaction with retries/background traffic noted.
2. Daily volume gives an all-day average only. Peak rate requires peak-share volume and window width; missing width yields a conditional formula/range, never invented peak duration/multipliers.
3. Choose open arrival or closed concurrency explicitly. Use Little's Law L=λW only within a steady window and consistent units; online/daily-active users are not concurrency.
4. Provide candidate curves, sourced session lengths/think times, and verification. Check observed arrivals/completions/mix; unconfirmed values are no production-capacity conclusion.

## Core Constraints

- do not equate online users with concurrency
- distinguish open and closed models
- use tunable ranges when production data is missing
- Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
- Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
- Explain priority using business impact, likelihood, or detectability.

## Delivery Checklist

- [ ] Covered: transaction mix, arrival rate, concurrency, think time, peaks and bursts, data distribution, cache warmup, dependency constraints.
- [ ] Separated facts, assumptions, gaps, and recommendations.
- [ ] Gave high-risk items a priority, evidence basis, owner or next action.
- [ ] Defined verifiable decision criteria instead of generic advice.
- [ ] Performed no unauthorized production writes or destructive actions.

## Common Pitfalls

- Listing checks without preconditions, expected outcomes, or evidence.
- Marking everything high priority and avoiding tradeoffs.
- Substituting tool names or generic theory for domain reasoning.
- Refusing incomplete input, or pretending incomplete evidence supports certainty.

## Best Practices

- Start with paths most likely to cause business loss, safety issues, or release blockage.
- Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
- Make the artifact executable and independently reviewable by another engineer.