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Reliability · Version 1.3.0 · Reviewed 2026-08-02

Capacity Headroom Planner

Reduce production risk in growth modeling and failure-domain sizing with evidence, explicit trade-offs, and a verification plan.

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

Turns growth, seasonality, and failure-domain requirements into a defensible capacity plan with explicit saturation limits.

₹149 one-time

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What this skill helps you do

  • Growth modeling
  • Failure-domain sizing
  • Saturation limit analysis

How Capacity Headroom Planner works

You provide

Growth forecast, failure-domain count, and current utilization

It inspects

The genuinely binding resource, not the obvious one

It decides

Headroom derived from surviving a domain loss

You verify

Simulated domain failure keeps survivors below saturation

What it checks first

Capacity Headroom Planner turns growth, seasonality, and failure-domain requirements into a defensible capacity plan with explicit saturation limits. Use it when the work involves Growth modeling, Failure-domain sizing, Saturation limit analysis.

  1. Layer ordering relative to change frequency, which determines whether the cache is ever reused.
  2. Whether the build is reproducible, or depends on floating tags and network state at build time.
  3. Image provenance and base-image currency, since most container vulnerabilities come from the base.
  4. Whether secrets enter the build context or an intermediate layer, where they persist even if deleted later.
  5. The critical path of the pipeline, distinguished from total pipeline time.

Failure modes it recognizes

  • Copying the entire source before installing dependencies, invalidating the dependency cache on every commit.
  • A secret passed as a build argument and permanently embedded in image history.
  • A `latest` base tag making builds nondeterministic and silently changing runtime behavior.
  • Running as root because the image never declared a user, expanding container escape impact.
  • A cache key that includes a timestamp, so the cache never hits.
  • Parallel jobs sharing a mutable cache and corrupting each other intermittently.

Answers it will reject

  • Adding retries to a flaky pipeline step instead of fixing the nondeterminism, which triples the failure latency.
  • Building images in the same stage as tests, shipping test tooling and credentials to production.
  • Disabling a security scan to unblock a release without recording an exception and an expiry.
  • Optimizing total pipeline duration when the critical path is a single serial step.

Decision rules it applies

  • Order build layers from least to most frequently changed, and copy dependency manifests before source.
  • Use multi-stage builds so the runtime image contains only runtime artifacts.
  • Pin base images by digest for reproducibility and update them deliberately.
  • Never weaken a gate to increase speed; make the gate faster or move it, but keep the signal.

Evidence it asks for

  • Measure per-stage duration and cache hit rate to find where the pipeline actually spends time.
  • Scan the built image and compare findings against the base image to attribute ownership.
  • Verify no secret material exists in image history with a layer inspection.

The method inside

  1. Extract decisions, facts, and unresolved questions needed for growth modeling.
  2. Organize failure-domain sizing around the reader's next decision or action rather than the source order.
  3. Draft saturation limit analysis with source traceability and no invented behavior.
  4. Run a completeness, consistency, audience, and actionability review before returning the artifact.

Deliverables

  • Growth modeling assessment
  • Failure-domain sizing decision and action plan
  • Saturation limit analysis verification checklist

Evidence requirements

  • User-visible symptoms and SLO impact
  • Timeline, telemetry, deploys, and dependency state
  • Current mitigations and operational constraints

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

How much headroom should we keep for our API tier and how do we justify it to finance?

Expected output

Headroom is not a preference, it is arithmetic from your failure domain. If you run three zones and must survive losing one, steady-state utilization cannot exceed roughly 66 percent before a zone loss saturates the survivors. Establish the true bottleneck resource first, since sizing on CPU when you are connection-bound produces the wrong number...

Boundaries and compatibility

Ideal for

  • Growth modeling: produce a decision or artifact grounded in supplied evidence.
  • Failure-domain sizing: produce a decision or artifact grounded in supplied evidence.
  • Saturation limit analysis: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Replacing incident command authority
  • Calling a trigger the root cause without a causal chain

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.