複数ベンダーの年次サイバーセキュリティ報告書を探して取得し、脅威やランサムウェア、侵害統計の傾向を出典付きで比較・分析します。
- 脅威レポートをカテゴリ別に探したいとき
- 特定ベンダーの報告書を取得したいとき
- ランサムウェア動向を複数社で比較したいとき
大きなシステムや戦略の課題で、意図する範囲を定めたうえで理想的な解決策を考え、現実に実行できる改善へ落とし込むスキルです。
Thinking skill for big problems that swaps the solver: reads the scope it was given, infers the scope the asker most likely means, maps that, then asks how a vastly smarter, fully informed intelligence with none of our habits or invented limits would do it, and turns every gap between its approach and ours into a change we can make. Built for large scope (how a whole system is implemented, an architecture, a strategy); when the intended scope is unclear or too small, a short interview settles it first. USE WHEN superior, how would a smarter intelligence do this, what would a genius or superintelligence do, if you knew everything, are we thinking too small, best possible approach, how should this whole system be built, analyze how this big thing is implemented. NOT FOR constraint teardown (FirstPrinciples), many options (BeCreative), attacking a plan (RedTeam).
実行スコア 未検証
インストール方法を見る大きな課題について、依頼された範囲から利用者が本当に解決したいことを推定し、適切な検討範囲を定めます。その範囲を地図のように整理したうえで、既存の計画に引きずられず、関連分野の知識や現在の手法も踏まえた独立の解決策を考えます。今の案との差を洗い出し、それぞれを実行できる変更にするか、実際に越えられない制約として明示します。範囲が曖昧な場合は短い質問で確認し、依頼者が範囲を指定している場合はその範囲を保ちます。
システム全体の構成、アーキテクチャ、複数要素を含む開発、戦略など、判断が多く積み重なる課題に向いています。小さな関数や単一の作業では得られる差が少ないため、より大きな目的や決定を見直したいときに使います。思い込みで設けた制約がないかを見直すきっかけにもなります。
検討範囲が小さすぎたり曖昧だったりする場合は、範囲の確認が先に行われます。依頼者が設定した制約は維持します。
この紹介文は、公開されている SKILL.md をもとに AI(OpenAI Codex)が作成しました。正確な仕様は下の原文を確認してください。
SKILL.md の記述から、注意が要る操作と導入の前提を AI が確認しました。点数は実際に動かした結果(実行検証)だけで付けています。
点数には入れていません。 2026年10月28日 時点で AI(OpenAI Codex)が判定しました。目安なので、入れる前に原文も確認してください。
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Every approach we take is shaped by the mind taking it: what it happens to know, what it has seen done before, what it assumes is expensive or impossible, how much effort it expects to spend. Superior holds the task fixed and replaces that mind. It asks how an intelligence far beyond ours would do this exact task — one that knows everything relevant including the current frontier, carries no habits or inherited conventions, invents no constraints, reasons cleanly from first principles, and cares only about the real goal behind the request. Then it measures the distance between that approach and the one we were about to take, and closes as much of it as reality allows.
Superior is built for big problems: how a whole system is implemented, an architecture, a strategy, a multi-part build. On a one-function question the superior mind and ours give the same answer, so the pass earns nothing. The value appears at scale, where habits and invented limits compound across many decisions.
FirstPrinciples changes what we believe about the problem. Superior changes who is solving it. They pair well: Superior often surfaces a constraint we invented, and FirstPrinciples is the tool for proving it was invented.
| Intent | Trigger | File |
|---|---|---|
| Elevate | any Superior request; invoked as a thinking skill on a hard task | Intents/Elevate.md |
The lens is defined by what that mind has and lacks, never by adjectives like "more careful" or "more thorough":
The pass begins from the scope it was given (the named file, feature, system or question) and works out what the asker most likely means by it. The literal ask is evidence, not the boundary: "how is the login implemented" usually means the auth flow end to end, not the one handler that was opened, and almost never means the whole product.
Abstract up to the proper scope, and no further. The proper scope is the smallest unit that contains the real goal: the level where the decision the asker cares about actually lives, reasoned from what was named, the surrounding conversation, the project, and what the answer will be used for. "The whole system" is the scope only when the ask points there. The result is stated before mapping, in one line: Scope: <inferred scope>, from <what was asked>, because <reason>.
When the inference is not confident, Elevate's scope interview settles it. Its questions find the right level: what this belongs to, what decision the answer feeds, and how far to reach (this piece, the feature, the subsystem). A scope the caller already fixed is the scope.
User customizations, when present, live at <configRoot>/LIFEOS/USER/CUSTOMIZATIONS/SKILLS/Superior/ (configRoot is $CLAUDE_CONFIG_DIR when set, else ~/.claude). Any PREFERENCES.md, configurations, or resources there override the defaults; when the directory does not exist, proceed with skill defaults.
Example 1: Hard design work
User: "Design the sync layer for the notes app. Run Superior on it."
→ Elevate, full mode: scope map of the app, server API and clients first; a fresh agent gets the map, goal and constraints, never our draft design
→ It returns a CRDT-based local-first design; we had planned server-authoritative REST with conflict prompts
→ Gap table: knowledge (CRDT libraries are mature), convention (server-as-truth), invented constraint ("offline is an edge case")
→ Changed plan adopts local-first; the one real limit (existing server API) is kept as a migration path
Example 2: Ask too small to run
User: "Superior on this retry helper."
→ The helper alone is too small, and it has callers in two places, so two scopes are plausible
→ Elevate asks: "I'd read this as the job runner's retry path; is that right, or is it the HTTP client?"
→ Principal: "Job runner; jobs keep double-firing."
→ Scope: the job runner's retry and dispatch path, not the whole runner; the map shows retries in three layers; the superior approach is idempotent jobs, which makes the helper unnecessary
Example 3: Inline lens during a max-effort run
A depth turn ("think deeply") on a strategy question; Superior runs beside FirstPrinciples
→ Elevate, light mode: one inline pass, stated before the current plan is reread
→ Superior approach dissolves half the task by reframing who the audience is
→ Two gaps adopted, one blocked by a principal-set budget, recorded in the ISA Decisions
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複数ベンダーの年次サイバーセキュリティ報告書を探して取得し、脅威やランサムウェア、侵害統計の傾向を出典付きで比較・分析します。
個別の機能や部品の設計と、システム全体の目的を同じ問いで比較し、両者の食い違い、適切な実装範囲、全体との整合性を3段階で整理するスキルです。
文章のテーマに合う格言や引用を探し、著者・出典と選定理由を添えて提案します。引用の追加、思想家の調査、利用履歴の管理を通じて、同じ引用の繰り返しも防ぎます。
ブログの挿絵や技術図解、比較図、マンガ、アイコンなどを用途別の手順で制作します。形式に合う構図や配色を組み立て、確認用の画像を出力します。
画像や映像、スライド、サイト画面の品質を複数の判定役で評価し、修正点の整理から基準を満たした成果物の引き渡しまで進めます。
arXivの論文をテーマ・分野・論文IDで探し、著者や要旨、リンクをまとめます。新着論文の確認や、用意されている場合はAlphaXivのAI生成概要の取得にも対応します。