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Data · Version 1.3.0 · Reviewed 2026-08-02

Idempotent Consumer Designer

Make data systems more correct and operable for deduplication design and ordering tolerance with evidence, explicit trade-offs, and a verification plan.

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

Designs message consumers that tolerate duplicates and out-of-order delivery without corrupting state.

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What this skill helps you do

  • Deduplication design
  • Ordering tolerance
  • State convergence

How Idempotent Consumer Designer works

You provide

Schema, volumes, and the queries that actually run

It inspects

Access patterns and skew affecting deduplication design

It decides

A ordering tolerance design with migration ordering

You verify

Row counts and checksums compared before cutover

What it checks first

Idempotent Consumer Designer designs message consumers that tolerate duplicates and out-of-order delivery without corrupting state. Use it when the work involves Deduplication design, Ordering tolerance, State convergence.

  1. The real access patterns, since schema quality is meaningless without the queries it serves.
  2. Cardinality, skew, and growth rate of the key columns.
  3. Consistency and freshness requirements per consumer.
  4. Whether the model preserves history or overwrites it, which determines what questions remain answerable.

Failure modes it recognizes

  • A schema optimized for writes that makes the dominant read pattern a full scan.
  • Skewed partitioning concentrating load on one node.
  • Nullable columns encoding several meanings, making every downstream query ambiguous.
  • A pipeline silently dropping late-arriving records and under-reporting totals.

Answers it will reject

  • Normalizing or denormalizing by preference rather than by measured access pattern.
  • Adding a column for each new requirement until the table encodes multiple entities.
  • Backfilling without a reconciliation check that proves completeness.

Decision rules it applies

  • Design from the read path backward for read-heavy systems.
  • Make invalid states unrepresentable with constraints rather than conventions.
  • Version the schema and treat consumer compatibility as a contract.

Evidence it asks for

  • Profile real query patterns and volumes before proposing a change.
  • Validate migrations with row counts, checksums, and spot comparisons.
  • Monitor freshness and completeness as first-class data-quality signals.

The method inside

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

Deliverables

  • Deduplication design assessment
  • Ordering tolerance decision and action plan
  • State convergence verification checklist

Evidence requirements

  • Schema, access patterns, query plans, or event contracts
  • Volume, cardinality, retention, and freshness
  • Consistency, latency, and migration 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

Our consumer processes the same event twice occasionally and once applied an old event after a newer one.

Expected output

At-least-once delivery guarantees both of those eventually, so the consumer must absorb them. Deduplicate on event ID within a bounded window, and make updates converge by comparing a version or timestamp rather than blindly applying...

Boundaries and compatibility

Ideal for

  • Deduplication design: produce a decision or artifact grounded in supplied evidence.
  • Ordering tolerance: produce a decision or artifact grounded in supplied evidence.
  • State convergence: produce a decision or artifact grounded in supplied evidence.

Out of scope

  • Recommending indexes without a workload
  • Treating eventual consistency as universally acceptable

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.