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Explain any topic, code, concept, or error tailored to a specific audience's level of understanding. Use this skill whenever the user says 'explain like I am', 'ELI5', 'explain this to my', 'break this down for', 'dumb it down', 'simplify this for', or asks you to explain something to a specific person or audience type (e.g., 'explain this to a manager', 'how would I explain this to my mom', 'make this understandable for a 5th grader'). Also trigger when the user mentions wanting to understand something at a particular level, or asks for an explanation targeting a non-technical audience. Even partial matches like 'explain to my wife' or 'tell my boss' should trigger this skill.

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Für Beweisverwertungsverbote prüfen: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Für Beweismittel-Katalog: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Für Change Reqüst: ordnet Akte, Belege und Lücken; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Für Beweislast und Zugang 309: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Für Clickwrap Beweisakte: ordnet Akte, Belege und Lücken; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Für Battle of Forms AGB Kollision: ordnet Norm, Beweislast und Gegenargument; Ergebnis: Beweislast- und Substantiierungsmatrix.

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。

Writing plays and screenplays in the Chekhovian mode (契诃夫戏剧法) — distilled from the complete plays (没有父亲的人/普拉东诺夫, 伊凡诺夫, 林妖→万尼亚舅舅, 海鸥, 三姊妹, 樱桃园, nine one-acts) plus Chekhov's own letters, Stanislavsky's and Nemirovich-Danchenko's rehearsal notes and 焦菊隐/童道明 criticism. Covers the four-act mood-based structure without central climax, off-stage events, "correct answer stated in Act 1 and refused", the announced event that never happens, three-layer endings (hope monologue + nihilist line + household action), no-villain conflict with environment, every character's object and catchphrase, one-way love chains, half-step-off dialogue, philosophy cut by thirst, the shortest words for the deepest pain, stage-direction and sound discipline, and how Chekhov revised The Wood Demon into Uncle Vanya. Use when the user wants an ensemble drama, a "nothing happens" play, subtext-heavy dialogue, a family estate / provincial story, an anti-climactic structure, or asks how to "Chekhov-ize" a script.

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。

Scene and sequence craft (场景/段落/细节/道具/悬念/情趣) — merged from Robert McKee's scene design and five-step scene analysis (value turn, beats, text/subtext, turning points, pacing, image systems), McKee's Dialogue scene types (balanced, comic, asymmetric, indirect, reflexive, minimal), Syd Field's sequence and scene elements, Neill Hicks (less is more, enter late leave early, energy), Richard Walter (action over talk, small gestures, avoid phones/cars/restaurants, list twelve alternative locations), Wendy Henson (action paragraphs, transitions, story-doing), Julian Hoxter (scene beats, objectives and tactics), Mei Feng's value test, and Lu Jun's chapters 意趣要足 (suspense, delay, wit), 细节要妙, 道具要精, 场景要当. Use when writing or fixing a single scene, choosing a location, building a sequence, planting a prop or detail, designing suspense and delay, cutting a scene that "nothing happens" in, or turning talk into action.

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。

Story structure for screenplays and stage plays (故事结构) — merged from Syd Field's paradigm and plot points, Blake Snyder's Save the Cat beat sheet and board, Robert McKee's Story (event/scene/sequence/act, inciting incident, progressive complications, crisis-climax-resolution, subplots, story triangle), Julian Hoxter's nine-beat model, Neill Hicks' three acts as attraction/anticipation/satisfaction, and Lu Jun's Chinese short-play structure (凤头猪肚豹尾, 起承转合, eight openings, eight endings, 入戏要快/脉络要清/高潮要高/转变要顺/变化要多). Use when outlining a script, placing plot points and midpoint, fixing a saggy second act, designing an opening or ending, diagnosing "nothing happens", choosing between linear and non-linear designs, or converting a premise into beats and cards.

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。

Validate bibliography entries against citations in all lecture files. Structural checks (missing/unused entries, malformed fields) by default; `--semantic` adds citation-drift detection, DOI verification, and style-consistency checks.

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。

Research-brief + claim-record discipline for delegated or AI-assisted research work. Use when starting any substantive research task or long autonomous run (write the brief first), and when reporting results that will support a claim in a paper or decision (produce the claim record). Keeps faster execution from being confused with credible 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。

Produce a polished local talking-head or narrated screen-recording rough cut without a cloud editor. Use when Codex must clean Chinese or mixed Chinese-English speech, correct product terminology before semantic editing, compress pauses without making speech breathless, preserve source resolution and frame rate, normalize dialogue loudness, generate final-audio subtitle artifacts, or benchmark local output against ChatCut/video-use/chengfeng/AI剪口播.

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。

Motion-first AI video director for topic-to-video creation. Use when the user gives a theme, idea, article, script, or brief and wants AI to freely create a real animated video instead of a PPT/slideshow-style explainer; when deciding visual metaphors, motion grammar, scene beats, component choices, generated image needs, HyperFrames/Remotion implementation strategy, and anti-PPT QC.

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 forecasting, estimating, or sizing risk — anchor on base rates, give ranges, update prior→likelihood→posterior on evidence, and factor unmeasured quantities into order-of-magnitude bounds.

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。

Map a LookML view/model/explore into ktx semantic layer sources. Covers the LookML to ktx primitive table, provenance tagging, and three worked examples (overlay, standalone from derived_table, standalone with sql_always_where). Load when the turn contains `.lkml` content.

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。

How to capture new reusable patterns into ktx's semantic layer - when a measure, segment, or join belongs in the catalog and how to write it generically so it stays small and useful over time. Loaded by the post-turn memory-agent only. The research agent does not write to the SL.

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。

Map dbt `schema.yml` / `properties.yml` models and sources into ktx semantic-layer overlays and column notes. Covers `sources:` vs `models:`, column `data_tests` (not_null, unique, accepted_values, relationships), and how bundle-time writes complement manifest backfill from git sync. Load when the WorkUnit's `skillNames` includes `dbt_ingest` or when raw files are dbt YAML under `models/` / `sources/`.

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。

Capture semantic-layer and knowledge updates from a live database schema 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。

Extract durable ktx knowledge and semantic-layer contribution proposals from staged Looker runtime dashboard, Look, and explore JSON. Load for WorkUnits whose raw files are under explores/, dashboards/, or looks/.

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。

ktx's semantic layer - a structured catalog of sources (tables/views), measures, joins, and segments expressed as YAML. Covers the schema and how to query it via `sl_query`. Use when the task involves querying pre-defined metrics (ARR, churn, retention, LTV, MAU) or reading SL source YAML to understand the catalog. Capture is handled by the `sl_capture` skill (memory-agent only).

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。

ktx's knowledge base - wiki pages for durable, reusable business knowledge. Covers capture workflow for user preferences, metric definitions, organizational conventions, and cross-references between wiki pages and semantic-layer sources. Loaded by the post-turn memory-agent only. The research agent reads wiki via `wiki_read`/`wiki_search` but does not write it.

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。

Synthesize durable ktx wiki pages and semantic-layer sources from staged Notion pages, databases, data-source rows, and clustered Notion evidence. Load when a WorkUnit contains Notion raw files or Notion evidence chunks.

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。

Convert Metabase questions, models, and metrics into ktx Semantic Layer source definitions. Covers result-metadata to KSL column type mapping, FK/PK detection, near-duplicate deduplication, pre-aggregation decomposition, join-graph connectivity, and how to react to priorProvenance from earlier ingest syncs. Load when the WorkUnit contains `cards/<id>.json` files under a Metabase bundle.

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。