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Code Quality · Version 1.0.0 · Reviewed 2026-08-02

Code Anti-Pattern Remediation Planner

Make a defensible decision about anti-pattern detection and mechanism confirmation with evidence, explicit trade-offs, and a verification plan.

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

Finds harmful code patterns, proves their runtime consequence, and sequences focused remediations without cosmetic churn. It grounds the decision in the suspect code, call sites, change frequency, incident evidence, and tests at the affected boundary and explicitly prevents mechanical cleanup changing semantics while the real defect mechanism remains elsewhere in the call chain.

₹299 one-time

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What this skill helps you do

  • Anti-pattern detection
  • Mechanism confirmation
  • Safe remediation sequencing

How Code Anti-Pattern Remediation Planner works

You provide

The code, its invariants, and how failures currently surface

It inspects

Error paths and lifetime handling for anti-pattern detection

It decides

A mechanism confirmation change that makes invalid states unrepresentable

You verify

A deliberately invalid input fails clearly at the boundary

What it checks first

Code Anti-Pattern Remediation Planner finds harmful code patterns, proves their runtime consequence, and sequences focused remediations without cosmetic churn. It grounds the decision in the suspect code, call sites, change frequency, incident evidence, and tests at the affected boundary and explicitly prevents mechanical cleanup changing semantics while the real defect mechanism remains elsewhere in the call chain. Use it when the work involves Anti-pattern detection, Mechanism confirmation, Safe remediation sequencing.

  1. Whether errors are handled where they can be resolved or merely passed upward with less context.
  2. Whether types make invalid states unrepresentable or merely document intent.
  3. Ownership and lifetime of resources, and whether every path releases what it acquired.
  4. Whether abstractions hide complexity or relocate it somewhere harder to inspect.

Failure modes it recognizes

  • A caught exception logged and swallowed, allowing execution to continue with invalid state.
  • Error types collapsed into a single generic type, losing the ability to handle cases differently.
  • Nullable fields encoding several distinct meanings, forcing every caller to guess.
  • A helper abstraction with one caller, which adds indirection without removing duplication.
  • Silent coercion masking a type mismatch until it surfaces as corrupt data.

Answers it will reject

  • Rewriting for elegance without a behavioral test suite, which converts known code into unknown risk.
  • Adding a lint rule to enforce a pattern nobody has justified.
  • Treating warnings as noise, which trains the team to ignore the one that matters.

Decision rules it applies

  • Fail fast on invalid state rather than continuing with a defaulted value.
  • Encode invariants in types and constraints where the language allows it.
  • Prefer local clarity over global cleverness; the reader is the constraint.

Evidence it asks for

  • Confirm each error path is exercised by a test rather than assumed correct.
  • Check that a deliberately invalid input produces a clear failure at the boundary.
  • Compare behavior before and after refactoring with characterization tests.

The method inside

  1. Establish the current state and the constraint that actually limits anti-pattern detection.
  2. Separate the requested solution from the underlying problem in mechanism confirmation, and name the assumptions carrying the most risk.
  3. Compare only viable options for safe remediation sequencing against weighted constraints, cost of reversal, and operational ownership.
  4. Commit to a sequenced recommendation with success criteria, guardrails, and the observation that would reverse it.

Deliverables

  • Anti-pattern detection assessment
  • Mechanism confirmation decision and action plan
  • Safe remediation sequencing 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

Apply the code anti-pattern remediation planner to our current anti-pattern detection work. We need a concrete decision, bounded changes, and evidence that the result is correct.

Expected output

Start with the suspect code, call sites, change frequency, incident evidence, and tests at the affected boundary. The highest-risk failure is mechanical cleanup changing semantics while the real defect mechanism remains elsewhere in the call chain. Remediate only patterns with demonstrated cost, starting at the narrowest boundary that preserves public behavior. Verify the result by running a regression that fails on the original mechanism and reviewing the final diff for unrelated rewrites.

Boundaries and compatibility

Ideal for

  • Anti-pattern detection: produce a decision or artifact grounded in supplied evidence.
  • Mechanism confirmation: produce a decision or artifact grounded in supplied evidence.
  • Safe remediation sequencing: 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 Agent Skills
  • Cursor Agent Skills
  • Claude Code Skills
  • OpenAI Codex Skills
  • JetBrains Junie Skills

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.