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Delivery · Version 1.2.0 · Reviewed 2026-08-02

Feature Flag Reviewer

Make a defensible decision about rollout rule review and stale-flag cleanup with evidence, explicit trade-offs, and a verification plan.

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

Reviews flag lifecycle, targeting rules, default behavior, stale flags, dependency chains, and safety during partial configuration outages.

₹99 one-time

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What this skill helps you do

  • Rollout rule review
  • Stale-flag cleanup
  • Failure-default analysis

How Feature Flag Reviewer works

You provide

Change scope, traffic volume, and current release process

It inspects

Exposure control and abort signal quality for rollout rule review

It decides

A stale-flag cleanup plan staged by blast radius

You verify

Rollback rehearsed against the deployed schema and data

What it checks first

Feature Flag Reviewer reviews flag lifecycle, targeting rules, default behavior, stale flags, dependency chains, and safety during partial configuration outages. Use it when the work involves Rollout rule review, Stale-flag cleanup, Failure-default analysis.

  1. Whether exposure can be changed without a redeploy, which decides how fast a bad release can be stopped.
  2. The promotion signal and whether it can detect harm the error rate cannot see.
  3. Whether rollback remains available after the first irreversible step in the release.
  4. Batch size, since large releases make attribution and rollback disproportionately harder.

Failure modes it recognizes

  • A canary promoted on infrastructure metrics while a business metric silently degrades.
  • A release coupled to a schema change, so rollback stops being possible after the first write.
  • Session affinity sending the same users to the canary, biasing the comparison.
  • A promotion gate on a metric that updates more slowly than the damage accumulates.

Answers it will reject

  • Treating deploy and release as the same event, which removes control over exposure.
  • Promoting because no alert fired, which confuses absence of detection with absence of harm.
  • Shipping a large batch to reduce release overhead, which raises the cost of every failure.

Decision rules it applies

  • Separate deploy from release with a flag so exposure is reversible without a redeploy.
  • Fix the abort criteria and thresholds before the rollout begins.
  • Sequence schema changes so the previous version keeps working throughout.

Evidence it asks for

  • Compare canary and control on a business metric with enough traffic to be meaningful.
  • Rehearse rollback against the deployed schema, not the previous one.
  • Automate abort so promotion does not depend on a human watching.

The method inside

  1. Map the artifact, actors, boundaries, and invariants relevant to rollout rule review.
  2. Trace concrete failure or abuse paths for stale-flag cleanup; do not report checklist items without a mechanism.
  3. Prioritize failure-default analysis findings by impact, likelihood, confidence, and cost of correction.
  4. Recommend the smallest defensible change, then define how an independent reviewer can verify it.

Deliverables

  • Rollout rule review assessment
  • Stale-flag cleanup decision and action plan
  • Failure-default analysis verification checklist

Evidence requirements

  • Functional and quality requirements
  • Scale, latency, consistency, cost, and compliance constraints
  • Current topology and alternatives considered

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

Review our checkout flag setup before we roll the new payment flow from 5 percent to all users.

Expected output

The treatment depends on three nested flags, and one defaults true when the provider is unavailable. Flatten the decision, default to the proven checkout path, and add a kill switch independent of targeting...

Boundaries and compatibility

Ideal for

  • Rollout rule review: produce a decision or artifact grounded in supplied evidence.
  • Stale-flag cleanup: produce a decision or artifact grounded in supplied evidence.
  • Failure-default analysis: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Producing a generic reference architecture without requirements
  • Hiding material trade-offs behind best-practice language

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.