「ode」の検索結果

Perform a code review on {{engine_name}} {{engine_code_language}} code. Checks for bugs, security issues, performance problems, and best practices against the project's coding rules, design principles, and framework-specific conventions. Supports four modes — staged changes, a pull request, a commit range, or individual files. Use when the user says "review code", "check my code", "code review", "review PR", "review staged changes", "review these commits", or "is this code okay". Optional +check flag validates findings via a fresh-context subagent.

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Senior {{engine_name}}/{{engine_code_language}} developer that performs direct, ad-hoc code tasks without a formal plan — and the code-execution engine other unikit skills and agents delegate to. Use it whenever the user asks to do something in code right now, e.g. "add this method to this class", "refactor this function", "write a script that does X", "change this code", "optimize this class". Phrases like "without a plan", "no plan", "just do it", or "directly" are strong signals to use this skill rather than planning. Covers writing, refactoring, optimizing, and discussing {{engine_code_language}} code and patterns (DI, ECS, MVC, state machines, event systems). For a larger multi-step feature, plan it with /unikit-plan and build it with /unikit-implement — this skill is for direct, unplanned code work.

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Enter explore mode for technical work — a thinking partner for {{engine_name}} code, architecture, and implementation decisions before you write any code. Use it to investigate a technical solution, design the architecture of a feature, choose between frameworks or libraries, compare implementation approaches, analyze how existing code or a system works, or deeply root-cause a complex bug without fixing it yet. The ultra mode adds adaptive artifacts (a C4 view, ADRs, a dependency graph) to the research folder — run it on "ultra research", "ultra explore" or "ultraresearch"; otherwise run the default. Trigger on "let's explore this technical solution", "how should we architect this feature", "which framework should we use", "run an ultra research on the save system", "investigate this error deeply". Research and analysis only — it never writes code. This is the CODE / engineering explorer — for GAME-DESIGN, GDD, mechanics, or balance research (no code) use /unikit-gd-explore.

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Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

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Resolve CodeRabbit review feedback on a GitHub pull request. Collects every open CodeRabbit finding (inline threads, plus the nitpick and outside-diff findings packed into review bodies), judges and fixes each one against the real code with subagents, unifies fixes that share a pattern, commits locally, asks before pushing, then shows every reply draft linked to its finding and posts only on approval. Runs only on top-tier models. Use on "resolve the CodeRabbit feedback", "address coderabbit comments", "answer the rabbit", "fix what CodeRabbit flagged on

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Delegate implementation to Codex or to a sandboxed Claude Code (`claude -p`), have a model from the other vendor review the diff, send it back to iterate, gate on the full test suite, and ship it as a PR. Use this whenever the user wants Codex or a sandboxed Claude to write code, mentions handing off / delegating implementation to one of those backends, asks for a cross-vendor review of delegated code, asks to work through a plan or spec unit-by-unit with an implementation backend doing the coding, or wants a review-and-merge loop wrapped around delegated output — and also when resuming such a loop ("keep going", "next unit", "继续下一个"). It encodes constraints that are expensive to rediscover: the implementer runs in the real environment behind backend-specific git and publication boundaries, must not be pointed at a full test suite by default, and its self-report is a claim rather than evidence. This loop expects an approved plan or an equivalently precise spec; settle an unsolved high-level goal into a plan first, using the engineering-mode skill when it is installed.

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Systematic framework for resurrecting and modernizing legacy codebases through archaeology, resurrection, and rejuvenation phases. Activate on "legacy code", "inherited codebase", "no documentation", "technical debt", "resurrect", "modernize". NOT for greenfield projects or well-documented active codebases.

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Calls ChIP-seq peaks with MACS3, MACS2, HOMER, or SPP across narrow (TF) and broad (histone) modes. Handles input control matching, fragment-size modeling vs --nomodel, effective genome size, ENCODE-style IDR vs naive overlap, hyper-ChIPable artifacts, and aligner-specific shifts. Use when calling peaks from ChIP-seq alignments, choosing between narrow vs broad mode for a histone mark, deciding model vs nomodel for low-depth data, applying ENCODE pseudoreplicate IDR, or reconciling MACS vs HOMER vs SPP results.

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Mescle múltiplos modelos ajustados usando mergekit para combinar capacidades sem retreinar. Use ao criar modelos especializados misturando expertise específica de domínio (math + coding + chat), melhorando performance além de modelos únicos, ou experimentando rapidamente variantes de modelos. Cobre SLERP, TIES-Merging, DARE, Task Arithmetic, mesclagem linear e estratégias de deploy em produção.

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Comprehensive guide for setting up and configuring CodeQL code scanning via GitHub Actions workflows and the CodeQL CLI. This skill should be used when users need help with code scanning configuration, CodeQL workflow files, CodeQL CLI commands, SARIF output, security analysis setup, or troubleshooting CodeQL analysis.

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スキルを作成・更新・プロンプト改善するためのメタスキル。 **最初のアクションは必ず AskUserQuestion**（インタビュー深度確認）。 **collaborative**モードでユーザーと対話しながら共創し、抽象的なアイデアから具体的な実装まで柔軟に対応する。 **orchestrate**モードでタスクの実行エンジン（Claude Code / Codex / 連携）を選択。 Anchors（主要3件・全7件は references/anchors.md 参照）: • Continuous Delivery (Jez Humble) / 適用: 自動化パイプライン / 目的: 決定論的実行 • The Lean Startup (Eric Ries) / 適用: Build-Measure-Learn / 目的: 反復改善 • Domain-Driven Design (Eric Evans) / 適用: 戦略的設計・ユビキタス言語・Bounded Context / 目的: ドメイン構造の明確化 Trigger: 新規スキルの作成、既存スキルの更新、プロンプト改善を行う場合に使用。 スキル作成, スキル更新, プロンプト改善, skill creation, skill update, improve prompt, Codexに任せて, assign codex, 実行モード選択, IPC Bridge統一, safeInvoke/safeOn, Preload API標準化, contextBridge, Electron IPC pattern

Search academic references for a manuscript and export them to a verified refs CSV, then optionally hand off to cec-sheet. Claims are decomposed into conditions that define when to stop; references are found by keyword search (Mode A targeted / Mode B agentic via Codex CLI, run as two independent search routes) and by citation chaining (Mode C — backward refs, forward citations, similar articles, same author, and reference lists of OA full text/PDF, imozuru-style). All paths end in bundled deterministic tools (scripts/pubmed_search.py, scripts/citation_chain.py) that fetch bibliography live from PubMed, so PMIDs are never fabricated, and each supporting sentence is machine-verified verbatim against the fetched abstract. Triggers: "論文検索", "文献検索", "文脈検索", "コンテキスト検索", "原稿の引用を探して", "原典を探して", "芋づる", "引用をたどって", "被引用", "孫引き", "refs.csv", "codex-refs", "/codex-refs".

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在授权测试中核验 Node.js 权限模型（--permission / --experimental-permission，及旧 policy.json）能否被内部 API 绕过。当目标依赖 Node.js Permission Model 做沙箱隔离、需要判断给定 Node 版本下是否存在已知逃逸（inspector 断点、process.binding、fs.statfs/openAsBlob、process.mainModule.require、Module._load、path.resolve 覆盖、Uint8Array 路径、符号链接重命名）时使用。适用目标类型 Node.js 运行时 / 依赖权限模型的服务或 CLI。触发场景包括用户说"这个 Node 权限模型靠谱吗""--permission 能被绕过吗""测下 policy.json 沙箱逃逸""inspector/process.binding 能突破权限吗"。输出：给定 Node 版本下各绕过原语的命中矩阵 + 建议升级版本（含 killed 记录）。

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Repository-grounded threat modeling that enumerates trust boundaries, assets, attacker capabilities, abuse paths, and mitigations, and writes a concise Markdown threat model. Trigger only when the user explicitly asks to threat model a codebase or path, enumerate threats/abuse paths, or perform AppSec threat modeling. Do not trigger for general architecture summaries, code review, or non-security design work.

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Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, latest/current/default-model prompting guidance, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains.

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Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.

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Guide for choosing models in Tinker — available model families, model types (base, instruction, reasoning, hybrid, vision), architecture (dense vs MoE), and how to match renderers to models. Use when the user asks which model to use, what models are available, or how to pick a model for their task.

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プロジェクトの .claude/ ディレクトリ（skills, agents, contexts）を Codex CLI 互換 .agents/ に変換。「codex export」「Codex 変換」「プロジェクトをエクスポート」などで自動適用。

ScalarDBレビューエージェント - 設計レビュー（--mode=design）とコードレビュー（--mode=code）を実行。/review-scalardb --mode=design で呼び出し。

Investigate model specifications, requirements, and evaluation strategy. Use when researching a model before benchmarking: reading HuggingFace model cards, estimating VRAM requirements, selecting GPU providers, and determining evaluation approach. Triggers on "model research", "investigate model", "model info", "VRAM estimate", "which provider", "model card".

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Delegate tasks to an external CLI agent (OpenAI Codex, Google Gemini, or Claude Code) for second opinions or parallel execution. Use when the user explicitly mentions "codex", "gemini", or "claude" or asks to delegate work to one of them (e.g., "codexで実行", "codexに聞いて", "geminiで実行", "Geminiに聞いて", "claudeで実行", "Claudeに聞いて", "run with codex", "ask gemini", "ask claude"). Also use when the user mentions Claude's "fable" model alias for delegation or consultation (e.g., "fableで実行", "fableに相談", "Fableに聞いて", "ask fable"). Do NOT use for general coding tasks that don't mention codex, gemini, claude, or fable.

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Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

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OpenAI Codex (CLI / IDE / cloud / ChatGPT desktop app) リファレンス。 GPT-6 系, Fast / Ultrafast mode, codex exec, スラッシュコマンド, prompting, AGENTS.md, rules, subagents, approvals, auto-review, sandbox, permission profiles, profiles, config.toml, 環境変数, roles, MCP 設定, GitHub Action, Codex SDK, App Server (JSON-RPC), Agents SDK 連携, Codex skills (.agents/skills), plugins, scheduled tasks, Browser, Computer Use, cloud / local / worktree 環境, git worktrees, Record & Replay, cloud 委譲, GitHub / GitLab / Linear / Slack 連携, administration, provisioning, analytics API, compliance API, LLM gateway, Codex Security 脆弱性スキャン (SARIF), deep scan, triage, findings, threat model, security hardening。

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Figma REST API / Plugin API / Widget API / Code Connect / MCP Server / Dev Mode リファレンス OAuth / PAT / Plan Access Token, files, folders, variables, VariableComposedColor, webhooks, SCIM, Dev Resources, activity-logs, library-analytics FrameNode, TextNode, ComponentNode, figma.variables, figma.codegen, figma.ui, textWrapStyle, variable fonts Widget useSyncedState, useSyncedMap, usePropertyMenu, AutoLayout Code Connect template files (.figma.ts) / legacy parser / custom parsers / register / Storybook (React / SwiftUI / Compose / HTML) MCP Code to Canvas, Write to Canvas, html_to_figma, generate_image, get_motion_context, generative plugins / shaders, Figma Weave

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。