Use when a course needs numbered problem, solution, and explainer scaffolds. Not for a CLI or Next.js project scaffold: use scaffold-nextjs.
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。
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概要と使いどころ
Use when a course needs numbered problem, solution, and explainer scaffolds. Not for a CLI or Next.js project scaffold: use scaffold-nextjs.
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 learner is ready to apply cleared concepts in a real project. Not for exercises or quizzes: use drill.
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 concept needs practising: the user asks for an exercise, quiz, recall, or gap probe. Not for explanations: use explain-concept.
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 making an architectural decision, changing a public API, shipping a feature, or recording a codebase term. Not for domain language: use domain-modeling. Not for docs: use docs-writing.
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 non-trivial change lands or is abandoned and its decision, rejected alternatives, and reason must be captured. Not for trivial or corrective changes: those belong in CHANGELOG.
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 implementation is complete, the test suite is green, and an integration decision is needed for a development branch or worktree. Not for failing tests or starting new work.
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 independent proposals on a contested decision need cross-critique before choosing, reusing the original authors. Not for parallel multi-stance investigation: use council.
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 one reviewer is not enough because failure modes are heterogeneous, or a claim needs cross-lens pressure before it ships. Not for collapsing a decision field: use converge.
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 non-trivial decision sits under uncertainty and correctness matters more than speed. Not for patch review: use review. Not for plan attacks: use load-bearing-assumption-test.
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 code mixes units, fixed-point precisions, scaling factors, rates, prices, shares, or conversions. Not for type-level unit modeling: use type-driven.
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。
Apply one governed bounded Proto UI dependency update after pui-deps establishes the exact manifest, lockfile, consumers, compatibility evidence, and rollback boundary. Use for ordinary package or toolchain updates whose intended compatibility is already fixed. Route unresolved public compatibility, security disclosure, publication, release, or provenance exceptions to their dedicated decision path.
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。
Create or revise a governed Proto UI spec entity after identity, ownership, lifecycle intent, semantic scope, relations, and evidence plan are fixed by current authority. Use for Contract, Prototype, Module, Adapter profile, Decision, Host Capability, Test, Version, or Knowledge entity work. Do not use to decide an unresolved admission or lifecycle promotion.
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。
Repair one independently verified Proto UI maintenance finding when governing authority or an explicit product decision fixes the expected result. Use for the bounded remediation and its evidence packet before independent 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。
Investigate a bounded Proto UI Contract, Prototype, Module, Host Capability, Adapter, schema, or ownership question and turn a genuinely unresolved direction into one semantic decision packet. Use for spec brainstorming, admission questions, competing semantic models, uncataloged behavior, or unresolved cross-layer boundaries.
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。
Close one technically complete Proto UI autonomous-maintenance transition after adequate independent review and required validation. Use to synchronize the finding, remediation packet, mission, run ledger, residual-risk queue, and recorded integration decision.
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。
Independently review one Proto UI autonomous-maintenance remediation in a fresh read-only context. Use after pui-remediate produces a packet and actual diff. Classify technical-completion eligibility without making product or integration decisions.
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。
Integrate one exact-head Proto UI pull request through either the ordinary independent-review path or a separately explicit current-user connected-operation decision. Preserve source, DCO, live platform enforcement and all deferred criteria; never manufacture approval or acceptance.
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。
Produce a concise session brief grounded in current Odylith state.
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 current Odylith state needs a concise session brief.
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。
Distill books, documents, folders, or URLs into compact, practical Agent Skills. Use when source material should become either one reusable skill package or a split set of focused skills, each with a concise SKILL.md plus detailed references and examples.
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。
Extract knowledge (decisions, facts, session metadata) from the current Claude Code session into the Grafema Knowledge Base. Run after completing a task or at any point when substantive knowledge was produced. Follows runbook _ai/runbooks/02-claude-sessions.md.
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。
Fires after fixing any non-trivial bug or regression. Asks: could the graph have caught this as a guarantee? Pairs with reflection-in-and-on-action — picks up after "earliest catchable signal" and asks the next question: was that signal expressible in graph? Triggers: (1) after any non-trivial bug fix is verified working, (2) after a regression report (something used to work, broke), (3) during step 6 (knowledge extraction) of the workflow, (4) when reflection-on-action surfaces a "would have been catchable" signal. Outcome is a triage decision (graph-reachable? rule expressible? rule sound?) and either a draft Linear issue + guarantee proposal, or a recorded "graph capability gap" note. Never auto-creates guarantees.
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。
Create polished, code-driven Remotion motion explainers from scripts, narration, subtitles, product specifications, reference videos, UI recordings, diagrams, or abstract concepts. Use for product launches, AI demonstrations, UI interactions, algorithms, scientific principles, electrical systems, data flows, architecture, charts, equations, and concepts best expressed with React, SVG, Canvas, WebGL, or Three.js. Prioritize continuous spatial logic, visible cause and effect, precise interaction states, editable parameters, deterministic rendering, and evidence-backed visual QA.
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。
Elicit and challenge data-analysis requirements before querying. Use when the user asks ambiguous data questions, requests business/report answers, needs metric definitions clarified, or may be drawing decisions from incomplete data.
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。