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Reliability · Version 1.3.0 · Reviewed 2026-08-02

Capacity Planning Agent

Reduce production risk in load projection and headroom planning with evidence, explicit trade-offs, and a verification plan.

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

Translates traffic projections into concrete compute, storage, and connection requirements with headroom.

₹99 one-time

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What this skill helps you do

  • Load projection
  • Headroom planning
  • Bottleneck identification

How Capacity Planning Agent works

You provide

Build definition, timings, and cache statistics

It inspects

Layer ordering and secret exposure for load projection

It decides

A headroom planning change that keeps every gate intact

You verify

Per-stage duration and cache hit rate re-measured

What it checks first

Capacity Planning Skill translates traffic projections into concrete compute, storage, and connection requirements with headroom. Use it when the work involves Load projection, Headroom planning, Bottleneck identification.

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

Deliverables

  • Load projection assessment
  • Headroom planning decision and action plan
  • Bottleneck identification 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

We expect 10x traffic during a product launch next month. What breaks first, and what should we size for?

Expected output

Your connection pool caps effective concurrency well before CPU saturates, so the database becomes the limit at roughly 3x, not 10x. Size against the pool and the slowest query under contention rather than against CPU headroom, because the CPU number will look healthy throughout the failure...

Boundaries and compatibility

Ideal for

  • Load projection: produce a decision or artifact grounded in supplied evidence.
  • Headroom planning: produce a decision or artifact grounded in supplied evidence.
  • Bottleneck identification: 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.