'sentence' 검색 결과

Points to the standalone module law-checker ("Legal Department"): source-grounded AI first-look legal assessments for German law with a statute registry and a statute-embodiment agent. Use this skill when a situation, contract, official notice, or legal question under German law should be checked with exact citations (article/section, paragraph, sentence) -- with a clear boundary: AI-assisted first orientation, not a substitute for a lawyer.

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{{One-sentence what-it-does + when to use it. Mention it bypasses the MCP via the REST API with {{ENV_VAR}}. Use when the user wants X, Y, Z programmatically without the {{service}} MCP installed.}}

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when the user asks to "map our positioning", "name our competitive alternatives", or "pick a beachhead segment for the launch"; produces a Dunford-style positioning canvas — named competitive alternatives (including spreadsheet and status quo), unique attributes (verifiable, or routed to the claims ledger), value themes (attribute→benefit→value chains), a target beachhead segment scored on serviceability / pain intensity / reachability, and a one-sentence onlyness statement — the sole upstream of the message house and the entity-signal source for the canonical entity profile. Not for the message house or per-channel launch copy — use message-house-builder; not for audience/persona profiling itself — use audience-mapper; not for SEO keyword positioning — use keyword-research. 定位画布/竞争替代品/独特价值/滩头细分

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

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Turn one sentence into a task line in the right to-do list, with project, priority and optional due date. Use for "add a to-do", "remember to ...", "put this on my list", "quick task". Nothing leaves your computer.

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Strips AI fingerprints from written text by enforcing banned vocabulary, forbidden sentence structures, and authentic prose patterns. Use when writing articles, documentation, social content, emails, or any text that should read as human-authored rather than AI-generated.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Writes in ASD-STE100 simplified technical English — short sentences, plain words, active voice. Use when the user wants terse technical prose, names this skill, or asks for STE100.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Grilling session that mines the user for fragments — heterogeneous nuggets of writing (claims, vignettes, sharp sentences, half-thoughts) — and appends them to a single document as raw material for a future article. Use when the user wants to develop ideas before imposing structure, or mentions "fragments", "ideate", or "raw material" for writing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Assemble a Japanese shokumu-keirekisho (career history document) from an experience inventory, choosing between reverse-chronological, chronological, and skills-based layouts, mapping the target posting's requirements to the experiences that can support them, and drafting the summary, work history, skills, and self-PR so that each sentence stays within what its evidence supports and confidential details are abstracted. Use when a user wants to write or rebuild a whole shokumu-keirekisho for a specific posting or as a general version, or wants to decide which experiences to include and in what order; do not use to invent experience, numbers, titles, or qualifications, to predict whether it will pass screening, to fill in a rirekisho form, or to submit it anywhere.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

One sentence - what this skill does and when to invoke it

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

One sentence - what this skill does and when to invoke it

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `$numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Targeted deep research to fill a specific gap in an existing thesis. Use when user says "deepen", "flesh out", "expand on", "fill in", or specifies a thesis section to improve. Also supports --sync-metrics mode — detects stale financial metrics across every in-scope analytical section of a thesis (not just Key Metrics or Summary) and coherently updates every sentence that depends on them, using a Key-Metrics-table / FMP / web-search waterfall. Use when user says "sync metrics", "update the numbers throughout the note", or "fix stale figures across the whole thesis". The batch form `--sync-metrics --all-flagged` syncs every thesis flagged by the latest `/numbers --all-open` in one operation — detection fanned out to capped READ-ONLY agents, one consolidated confirmation gate, direct sequential writes (agents never write).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Get a barebones Convex + web template running from a one-sentence idea.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Comprehensive toolkit for writing high-quality computer science research papers (conference, journal, thesis). Provides narrative construction guidance, sentence-level clarity principles (Gopen & Swan), academic phrasebank, CS-specific conventions, and section-by-section quality checklists. Use when assisting with academic paper writing, revision, or structure planning across all stages from drafting to submission.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

One sentence - what this skill does and when to invoke it

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

One sentence - what this skill does and when to invoke it

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Provides bidirectional translation (Chinese/English), grammar checking, and sentence polishing. Supports specific tense constraints, context-aware tone adjustment, and natural expression optimization. Outputs direct text without formatting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Activate this skill when a student provides study material (PDF or pasted notes) and a syllabus, and wants to prepare for an exam. Extracts key definitions, points, keywords, diagrams, exam-ready sentences, and practice questions strictly from the provided material.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Count words, characters, sentences, paragraphs, and reading time for any text using AceToolz.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Performs paragraph-by-paragraph translation and deep linguistic analysis. Explains English syntax, grammar, and sentence structure in Chinese. References assets from the raw source directory.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.