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Reliability · Version 1.7.0 · Reviewed 2026-08-02

Chaos Experiment Designer

Reduce production risk in failure experiment design and blast-radius control with evidence, explicit trade-offs, and a verification plan.

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

Creates hypothesis-driven failure experiments with steady-state metrics, bounded blast radius, abort conditions, and cleanup verification.

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What this skill helps you do

  • Failure experiment design
  • Blast-radius control
  • Steady-state selection

How Chaos Experiment Designer works

You provide

Impact window, telemetry, and dependency state

It inspects

Saturation and blast radius behind failure experiment design

It decides

A blast-radius control plan that stabilizes before diagnosing

You verify

Detect, mitigate, and resolve times recorded separately

What it checks first

Chaos Experiment Designer creates hypothesis-driven failure experiments with steady-state metrics, bounded blast radius, abort conditions, and cleanup verification. Use it when the work involves Failure experiment design, Blast-radius control, Steady-state selection.

  1. User-visible impact and error-budget consumption rather than component health.
  2. Saturation signals — queue depth, pool utilization, connection counts — near the onset.
  3. Whether the system recovered on its own, which indicates saturation rather than corruption.
  4. The blast radius and what boundary should have contained it.

Failure modes it recognizes

  • Retry amplification turning a partial failure into a total outage.
  • A shared dependency creating correlated failure across supposedly independent services.
  • Slow resource exhaustion invisible until a hard limit is crossed.
  • A rollback blocked by an incompatible migration.

Answers it will reject

  • Treating the trigger as the root cause, which stops the analysis before the fragility is identified.
  • Adding a runbook step where a boundary would remove the failure mode.
  • Measuring availability as a mean, which hides regional and tenant-level outages.

Decision rules it applies

  • Stabilize user impact before completing diagnosis.
  • Bound every retry with a budget, jitter, and a circuit breaker.
  • Prefer removing a failure mode over detecting it faster.

Evidence it asks for

  • Record time-to-detect, time-to-mitigate, and time-to-resolve separately.
  • Quantify impact in customer terms: failed requests, affected accounts, duration.
  • Verify recovery with the same signal that detected the failure.

The method inside

  1. Turn failure experiment design into explicit functional requirements and quality-attribute constraints.
  2. Model the critical path, state, trust, and failure boundaries that govern blast-radius control.
  3. Compare viable designs for steady-state selection against weighted constraints and operational ownership.
  4. Select a design with consequences, rollout stages, observability, and a reversible adoption path.

Deliverables

  • Failure experiment design assessment
  • Blast-radius control decision and action plan
  • Steady-state selection 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

Design a chaos test to prove our service survives losing one availability zone during peak traffic.

Expected output

Define steady state as successful checkout rate and p99 latency, begin with one stateless pool rather than the whole zone, pre-validate spare capacity, and abort if error budget burn exceeds the agreed threshold...

Boundaries and compatibility

Ideal for

  • Failure experiment design: produce a decision or artifact grounded in supplied evidence.
  • Blast-radius control: produce a decision or artifact grounded in supplied evidence.
  • Steady-state selection: 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.