「correctness」の検索結果

Review Yuzu C++ source changes for C++23 correctness, idiomatic standard-library use, ABI boundaries, threading primitives, and cross-compiler portability across GCC, Clang, MSVC, and Apple Clang. Use for any governance Gate 3 review when `.cpp`, `.hpp`, or `.h` files change.

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Run dialyzer over the Yuzu Erlang gateway. Sources the Erlang toolchain and runs rebar3 dialyzer. Use after any .erl change in gateway/ — CLAUDE.md mandates dialyzer be clean before commit because compilation success does not imply type correctness.

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Explore and debate options with fresh documentation and research before committing. Use when facing a real decision with multiple plausible approaches — library/framework choice, architecture call, API or schema design, algorithm selection, or any communication / strategy call where being wrong has cost. Enumerates relevant research domains, looks up current docs and evidence-based methods, weighs options on correctness and elegance, resists bloat. Do NOT use for divergent ideation (brainstorm), extracting user intent (elicit), or reviewing already-written code (quality-review).

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Use when completing a TDD step and wanting a quality check. Reviews test quality after RED, implementation correctness after GREEN, and scenario completeness after REFACTOR.

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Perform thorough code reviews with actionable, prioritized feedback. Use when a user asks to review code, check code quality, find bugs, review a pull request, audit code for issues, or get feedback on implementation. Covers correctness, security, performance, readability, and best practices across languages.

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Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

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Mechanical documentation hygiene. Spawns a doc-maintainer agent to audit all project documentation and fix correctness, completeness, freshness, and consistency issues autonomously within its authority. Surfaces anything requiring user judgment for approval.

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Use when large data ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, or table synchronization needs to become much faster while preserving data correctness.

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Professional typography rules for UI design, web applications, software interfaces, and all screen-based text. Enforces timeless typographic correctness that LLMs consistently get wrong: proper quote marks, dashes, spacing, hierarchy, and layout. ENFORCEMENT MODE: When generating ANY HTML, CSS, React, JSX, or UI code containing visible text, auto-apply every rule in this skill silently — do not ask, do not explain, just produce correct typography. AUDIT MODE: When reviewing or improving existing interfaces or legacy code, flag violations and provide fixes. Trigger on: any HTML/CSS/React artifact creation, "build a landing page", "create a component", "design a UI", "fix the typography", "make this look professional", "review this layout", web design, presentation design, dashboard creation, document generation, or any task producing visible text for humans. Even if the user doesn't mention typography, apply these rules whenever generating UI output.

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Conducts multi-axis code review across correctness, readability, architecture, security, and performance. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Triggers on "review my PR", "review this diff", "code review", "review this changeset", "is this ready to merge", "pre-merge review".

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Use when reviewing or auditing a library of Claude Code skills, agents, slash commands, and workflows — frontmatter correctness, routing quality, tool allowlists, command arg-hints, workflow meta/phase coherence, cross-reference coherence, single-responsibility, file structure, and anti-pattern detection. Triggers on mentions of "review skills", "audit agents", "skill library", "agent definition review", "review this command", "review this workflow", "is this skill right", or when iterating on `.claude/skills/`, `.claude/agents/`, `.claude/commands/`, or `.claude/workflows/` directories. For code review of source code see code-review-and-quality.

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Scientific research engine v6.0 NEXUS — adversarial review (Reviewer 2), 32 quality gates, tree search, serendipity tracking, confounder harness, cross-session learning. Use for ANY scientific analysis, hypothesis testing, data validation, literature review, or task where correctness > speed.

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Code auditor that grades software projects and the agents/developers who built them. Produces structured JSON + Markdown reports with a standardized rubric. Evaluates architecture, correctness, testing, authorship cohesiveness, ghost code, and more. Optionally ingests tmux session logs for process evidence and interrogates agents about specific problems found. Use when asked to "audit", "grade", "evaluate", "review code quality", "rate this project", "score the agents", or "assess the codebase". Especially useful after Marcus multi-agent experiments to grade agent output and track quality over time.

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Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

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Diagnose agent operational failures through default-policy checks, multi-gap analysis, explicit service ownership, request-context lifetime, and degradation-versus-correctness checks.

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Evaluate repeated code and consolidate duplication when a shared abstraction improves correctness or maintenance. Use after introducing similar logic or when repeated changes reveal a missing source of truth.

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Review changed code for correctness, security, maintainability, and verification gaps. Use when code is ready for independent review or before handoff, staging, or merge.

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Reviews a GitHub PR with a shallow correctness/security/breaking/AI-slop checklist and emits a verdict; optionally posts via gh. NOT for own-diff audit (mk:review); NOT for replying (mk:respond-pr).

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Reviews a GitHub PR with a single shallow correctness/security/breaking/AI-slop checklist and emits a verdict; optionally posts it via gh pr review. Use to review an external or teammate PR. NOT for deep pre-landing audit of your own diff (see mk:review); NOT for responding to received review comments (see mk:respond-pr).

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Develop, challenge, and document Chisel processor microarchitecture designs before implementation. Use when discussing or writing design documents for processor pipelines, queues, issue logic, rename, ROB, LSU, caches, MSHRs, forwarding, wakeup, replay, flush, privilege, exceptions, or other cycle-accurate hardware mechanisms; when converting design conversations into stable Markdown specifications; or when reviewing a proposed Chisel CPU design for correctness, timing, area, verification cost, and cross-document consistency.

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Review a small code or documentation change and return only evidence-backed correctness findings. Use in the Codex How To local plugin lab; do not modify files or post remote comments.

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Review code changes for correctness defects, regressions, security risks, data-integrity problems, concurrency hazards, and missing meaningful tests. Use for pull requests, branches, commits, patches, or working-tree reviews; do not use for implementation unless the user separately requests fixes.

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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.

日本語の概要は準備中です。原文の説明を表示しています。

Grounds every implementation decision in official documentation. Use when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.

日本語の概要は準備中です。原文の説明を表示しています。