「correctness」の検索結果

Review code against a standard checklist covering security, correctness, performance, and readability. Use when asked to review code, check a PR, or audit changes.

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。

Mint a new YYYY-MM-DD-exact-coding-baseline snapshot under research/workflow-dev/export/. Detects the current best correctness- oriented workflow from research/workflow-dev/workflow-construction.md (or takes an explicit source-workflow argument) and transforms it from a lab workflow into a consumer-ready one on three axes: strips lab-only measurement content, re-enables human-in-the-loop checkpoints, and converts auto-loading config into an explicitly-invoked skill. Supports both promoted EXACT Coding lines: Opus/Hybrid and SOL/Predictive TDD. Exports Claude Code, pi, OpenCode, cursor-agent and, for SOL, Copilot. Trigger when the user says "exact-coding baseline export", "neue exact-coding baseline", "exact-coding-baseline-export", or asks to refresh the baseline snapshot.

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。

Use when the user asks for a multi-agent .NET code review - five parallel reviewers (correctness, performance, security/observability, data/messaging/integration, generalist) plus an adversarial maintainer that refutes weak findings. Requires the review agent roles registered by scripts/install-codex.sh. Keywords: dotnet review pipeline, multi-agent review, parallel reviewers, maintainer pushback, review this branch, cynical review.

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。

Use when reviewing PRs/diffs/branches/documents for .NET quality, correctness, performance, security, data access, messaging, and observability. Includes adversarial critical-thinking mode for skeptical/cynical review requests. Keywords: code review, review PR, review diff, critical review, cynical review, adversarial review, production readiness, low GC, AOT, security review.

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。

Review gate for correctness, reuse, and simplification. Produces a structured verdict with actionable findings. Trigger on any PR before merge, or on any agent-generated code before it is committed.

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。

Use when a diff needs correctness review — null-safety, edge cases, off-by-one, races, error handling — dispatched by /review-changes, /do-and-judge, /judge, even without 'judge'.

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。

Use when the user says "review this", "check my code", or wants feedback on changes. Reviews for correctness, quality, security, and coding standards.

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。

[Code Quality] Use when you need to prove fix correctness with adversarial code proof traces — a skeptic tries to DISPROVE the fix and trace all related paths first, with confidence scoring and stack-trace-style evidence chains, so confidence is earned by surviving attack, not by confirming it works.

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。

[Planning] Use when you need to auto-review a plan for validity, correctness, and best practices — recursive: review, validate findings with why-review, fix validated findings, full re-review until no findings.

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。

Use when reviewing, fixing, or verifying a pull request. Focus on correctness regressions, missing tests, permission mistakes, unsafe behavior, and minimal fixes.

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。

Test API endpoints for correctness, edge cases, auth handling, and OpenAPI contract compliance.

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。

Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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。

Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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。

Run convex-doctor static analysis, interpret findings, and fix issues across security, performance, correctness, schema, and architecture categories. Use when running convex-doctor, fixing convex-doctor warnings or errors, improving the convex-doctor score, or when asked about Convex code quality, static analysis, or linting Convex functions.

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。

Static analysis checklist for Convex backends covering 72 rules across security, performance, correctness, schema, architecture, configuration, and client-side patterns. Use when writing, reviewing, or auditing Convex code. Trigger on mentions of "convex-doctor", "health score", "static analysis", "anti-patterns", "audit convex", or before shipping backend changes.

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。

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.

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。

Use this skill for critical code review that focuses on whether the code actually works correctly and achieves the expected results - not just whether it matches a design document. This skill assumes designs can be wrong, finds bugs the design didn't anticipate, and verifies end-to-end correctness. Triggers include "critical code review", "deep code review", "verify this implementation works", "find bugs in this code", or reviewing implementation against requirements. NOTE - Security review is OUT OF SCOPE; use a dedicated security review skill for that purpose.

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。

Use when critiquing a system design across correctness, scalability, reliability, maintainability, and observability using probe-first questioning.

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。

Use when evaluating a completed or in-progress project across scope, correctness, quality, testing, and learning outcomes using structured Praise-Critique-Grow feedback.

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。

Turn 'should never happen' into 'cannot happen' by defining owned inductive invariants and enforcing them at parse/construct/API/DB/lock/txn boundaries with a verification signal. Use when prompts mention invariants, impossible states, validation sprawl, cache/index drift, idempotency/versioning, retries/duplicates/out-of-order events, race/linearization bugs, loop correctness, or hardening another workflow (for example /tw-fix) with invariant checks first.

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。

Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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。

Comprehensive, measurement-driven performance optimization for latency, throughput, memory/GC, and tail behavior. Use when the user asks to optimize/speed up, reduce latency (p95/p99), increase throughput/QPS, lower CPU/memory/allocations/GC pauses, profile hot paths, or run a benchmarked perf pass (including JSONL/query-heavy code). Requires before/after measurement on a runnable workload (or an explicit `UNMEASURED` plan) plus a correctness gate.

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。

Load when asked to review a local diff/change set for correctness, regressions, blast radius, affected flows, and missing tests using the knowledge graph; use `review-pr` for intent/checklist/merge-gate evidence.

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。

Use when the user wants to send the same prompt to several LLMs and compare the answers - sequentially (stopwatch, clean per-lane timing) or in parallel (true race), with optional repetitions per model, judged by the starting model across quality, correctness, completeness, instruction fidelity and latency (time is only one dimension). Triggers on /compare-race, 'model race', 'vergleiche modelle', 'gleicher prompt an mehrere modelle'.

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。