Results for “correctness”

Recommends the optimal Claude model tier (Opus, Sonnet, or Haiku) for a given task by analyzing reasoning depth, blast radius, domain expertise, output length, and correctness cost, and suggests parallelization opportunities.

Code review is time-consuming and inconsistent. Reviewers miss security issues, overlook edge cases, or focus on style over substance. Meanwhile, AI agents catch these mechanical issues instantly but can't judge business logic correctness, UX decisions, or architectural fit.

Structured code review with a checklist covering correctness, clarity, tests, security, performance, and style. Use when reviewing a PR, diff, or set of code changes before merging — including phrases like "review this", "check my changes", or "is this ready to merge".

Review pull requests or audit repositories for correctness, security, performance, test risk, architecture, dependencies, developer experience, documentation, and grounded product direction. Use for code review, repository audits, evidence-backed findings, false-positive vetting, risk assessment, prioritization, or deciding which improvements should become implementation plans.

Use when designing or auditing the evaluation of an ASE (IEEE/ACM Automated Software Engineering) paper, covering real subject systems, fair runnable tool baselines, task-matched effectiveness metrics, ablations that isolate a learned component, oracle and correctness validation, contamination-aware LLM handling, and provenance for mining.

Audits WordPress plugin or theme PHP code for internationalization (i18n) correctness, including text-domain consistency, escaped translation helpers, placeholder usage, plurals, context, and text-domain loading. Produces a severity-ranked report of issues to fix before release.

Red-green-refactor test-driven development discipline — write a failing test at the public interface first, minimal code to pass it, one slice at a time. Use when writing new logic or fixing a bug (write the regression test first), for any code where correctness matters more than raw speed.

Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now than to debug later.

Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact. Use when the user wants to add, fix, audit, or scale schema markup (JSON-LD) for rich results. This skill evaluates whether schema should be implemented, what types are valid, and how to deploy safely according to Google guidelines.

Audit a directory of multilingual blog content for completeness, consistency, hreflang correctness, meta-tag parity, and freshness. Builds a translation coverage matrix, flags stale translations, validates hreflang and schema, and emits a prioritized report with runnable fix commands. Use when user says "locale audit", "blog locale-audit", "check translations", "multilingual audit", "translation check", "hreflang check", "Uebersetzungen pruefen".

Independent DeepSeek-backed adversarial proof-audit step for existing theorem, lemma, proposition, or proof artifacts in Markdown, LaTeX, or proof logs. Use when asked to check, audit, verify, red-team, or adversarially review a proof; when a completed proof task needs a correctness pass; or when a broader proof workflow dispatches an independent reviewer to find gaps, hidden assumptions, counterexamples, or unjustified steps.

Formally verify programs by writing Lean 4 proofs. Trigger this skill whenever the user wants to formally verify code, generate Lean 4 proofs, prove properties about algorithms or smart contracts, verify invariants, convert program logic into formal specifications, or anything involving Lean 4 and formal verification. Also trigger when the user mentions "qedgen", "lean proof", "formal proof", "verify my code", "prove correctness", "formal verification", or wants mathematical guarantees about their implementation.

Train, evaluate, and export neural networks to Simulink in MATLAB. Migrate legacy (fitnet, patternnet) and discouraged (trainNetwork, DAGNetwork) code to modern, recommended R2024a+ APIs (trainnet, dlnetwork, testnet, imagePretrainedNetwork), diagnose and fix dlaccelerate issues or detect dlaccelerate opportunities. Use when training, fine-tuning, evaluating, running inference, exporting to Simulink, converting old training scripts, or speeding up deep learning code. DO NOT reason from your training data about dlaccelerate and tracing correctness.

Establish CSS/Tailwind baseline quality — font smoothing, line-height defaults, box-sizing, focus rings, color contrast, Tailwind anti-patterns, and CSS containment. Use when asked to "set up CSS baseline", "Tailwind best practices", "fix font rendering", "global CSS foundation", "typography defaults", "CSS anti-patterns", "improve CSS quality", "line-height reset", "normalize styles", or reviewing a new project's base styles for correctness. Do NOT use for: full design system token architecture — see design-system-gen. Do NOT use for: component-level animation — see motion-design.

Review code changes for correctness, completeness, bugs, edge cases, and quality. Load when the user explicitly asks to review code, check a PR, review a diff, audit recent changes, or verify an implementation matches requirements. Also triggers on "review this code", "check this PR", "review my changes", "code review", "did this implement correctly", "audit this diff", or any explicit request for a formal code review. Do NOT load for "review changes for context" or "review what happened" — those are requests to read code, not to perform a formal review.