capacity

# Capacity Structured guidance for **capacity planning** (load, headroom, scaling): confirm triggers, propose the stages below, and adapt if the user wants a lighter pass. ## When to Offer This Workflow **Trigger conditions:** - User mentions **capacity**, **headroom**, **scaling plans**, or closely related work - They want a structured workflow rather than ad-hoc tips - They are preparing a review, rollout, or stakeholder communication **Initial offer:** Explain the four stages briefly and ask whether to follow this workflow or work freeform. If they decline, continue in their preferred style. ## Workflow Stages ### Stage 1: Clarify context & goals Anchor on **growth assumptions and seasonality**. Ask what success looks like, constraints, and what must not break. Capture unknowns early. ### Stage 2: Design or plan the approach Translate goals into a concrete plan around **headroom and bottlenecks**. Compare alternatives and explicit trade-offs; avoid implicit assumptions. ### Stage 3: Implement, validate, and harden Execute with verification loops tied to **cost vs performance trade-offs**. Prefer small steps, measurable checks, and rollback points where risk is high. ### Stage 4: Operate, communicate, and iterate Close the loop with **load tests and forecasts**: monitoring, documentation, stakeholder updates, and lessons learned for the next cycle. ## Checklist Before Completion - Goals and constraints are explicit for **capacity** planning - Risks and trade-offs are stated, not hand-waved - Verification steps match the change’s impact (tests, canary, peer review) - Operational follow-through is covered (monitoring, docs, owners) ## Tips for Effective Guidance - Be procedural: stage-by-stage, with clear exit criteria - Ask for missing context (environment, scale, deadlines) before prescribing - Prefer checklists and concrete examples over generic platitudes - If the user declines the workflow, switch to freeform help without lecturing ## Handling Deviations - If the user wants to skip a stage: confirm and continue with what they need. - If context is missing: ask targeted questions before strong recommendations. - Prefer concrete examples, trade-offs, and verification steps over generic advice. ## Quality Bar - Each recommendation should be **actionable** (what to do next). - Call out **failure modes** relevant to capacity (security, scale, UX, or ops). - Keep tone direct and respectful of the user’s time.