'sso' 검색 결과

Creates a lesson plan, student-facing materials, and observation template. Load this skill BEFORE asking the teacher any clarifying question about grade, subject, topic, standard, or timing. Use when a K-12 teacher needs a math, ELA, science, or social studies lesson built from scratch — even if grade, subject, or topic isn't yet stated. Do NOT load for grading, a rubric, assessment feedback, a quiz, or a standards lookup — answer those directly. Triggers on explicit requests (lesson plan, mini-lesson, unit plan, daily plan) and implicit teacher intent: "I'm teaching long division to 5th graders," "need to teach photosynthesis tomorrow." Core signal: teacher needs new instructional content created. A new lesson that asks for differentiated, tiered, or leveled materials is still ONE planning request — this skill produces those materials inside the lesson package; do not also invoke k12-lesson-differentiation. Not for differentiating an existing lesson (use k12-lesson-differentiation) or passage rewrites.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan and build a connected series of lessons around learners’ experience and goals. After the user approves the complete lesson list, it creates lessons in order, checks continuity, and organizes them in one folder.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a seven-day introductory Python course
• Creating a four-week onboarding program
• Turning book chapters into separate lessons

Add or verify model support in SGLang's Rust processor so chat requests produce the same prompt text and token IDs as Python. Use reference-generated test cases to find differences in tools, reasoning settings, conversation history, and errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding model support to sglang-processor
• Porting model rendering from sgl-router
• Rechecking prompt behavior after a Dynamo update

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Adapts an existing K-12 lesson (math, ELA, science, or social studies) for students at different proficiency levels (below / at / above grade level). Load this skill BEFORE asking the teacher any clarifying question about the lesson, tiers, or student levels. Triggers on explicit asks to differentiate, tier, or scaffold a lesson, and on implicit signals like "my students are at different levels". Produces 1 teacher-facing differentiation plan + 3 student-ready tier documents as editable, printable documents in a single output turn (shared content is written once, so tiers cannot drift). Uses the Learning Commons Knowledge Graph when connected; works without it. This skill adapts a lesson the teacher brings or names. Not for creating a new lesson from scratch — a new-lesson request that asks for differentiated or leveled materials is k12-lesson-plan-creation's job, one package. Not for grading, rubrics, assessment feedback, or quizzes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Helps a teacher prepare to teach an existing K-12 lesson (math / ELA / science / social studies) — a prep partner that thinks through the lesson's key student task with the teacher and leaves a short teacher-only prep note. Triggers on asks to internalize, prepare for, get ready to teach, or understand a lesson, including implicit ones ("help me get ready for tomorrow's lesson"), and right after another skill built a lesson, which it can follow. Not for building or adapting a lesson, grading, rubrics, assessment feedback, or generating quizzes or student-facing material, and not for single-fact lookups a sentence would answer ("what standard is this?", "how long is this lesson?").

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Creates GDPR Article 30(2) Records of Processing Activities for data processors with all four mandatory fields: processor and controller names and contact details, categories of processing, third country transfers, and security measures description. Activate for processor RoPA, Art. 30(2), processor records, sub-processor documentation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Check your understanding of one Claude Code tutorial lesson with a 10-question quiz. Get feedback after each round, a final score, and specific topics to review, whether you take it before, during, or after studying.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check prior knowledge before a lesson
• Check understanding while studying
• Review weak areas after a lesson

Turns user corrections and recurring mistakes into actionable rules with confidence scores. It records when each rule applies and why it matters, so relevant lessons can be retrieved before similar work begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording a rule after a user correction
• Saving a lesson from a recurring mistake
• Keeping rules specific to one project

Check the AWS profile, region, account ID, and caller identity before starting AWS work. Read local configuration and validate credentials, then flag missing settings, expired access, or possible IAM restrictions associated with SSO authentication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check the AWS account and region before deployment
• Identify the active profile when none was specified
• Detect expired credentials or missing configuration

CLAUDE.mdの「Lessons Learned」セクションを管理するスキル。`/lessons` で学びを追記、`/lessons review` で既存ルールのレビュー・整理を行う。セッション中の修正・失敗・発見を構造的に蓄積し、チーム全体の知見として共有する。「CLAUDE.mdを更新して」「学びを記録して」等の自然言語でも起動可能。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Study AI engineering through interactive terminal lessons with explanations, coding exercises, and quizzes. The tutor follows your recorded progress, revisits previous material, adapts to your answers, and logs results and topics that need review.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Resuming lessons from recorded progress
• Learning concepts through coding exercises
• Checking understanding with lesson quizzes

Find relevant lessons in AI Engineering from Scratch from a topic, question, or bug. Get a short selection of lessons, necessary prerequisites, and the next learning command, with dedicated routes for specialized subjects.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding lessons on a topic you want to learn
• Locating lessons that explain a bug's underlying cause
• Choosing what to study next based on your progress

Assess authorized single sign-on systems using SAML, OIDC, and OAuth2. Map the login flow, check signatures, redirects, and token validation, then document reproducible findings and their impact, including identity or tenant confusion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review redirect and token checks in a single sign-on flow
• Assess SAML signatures and assertion handling
• Investigate tenant confusion and logout failures

Sign in to command-line tools through a browser or device verification code, then save the login to a keychain. Later commands can request the saved credentials by handle, an identifier, without exposing token contents in the conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Sign in to AWS CLI through AWS SSO
• Authenticate gh or glab through a browser
• Save a gcloud login for later commands

Look up one genetic variant in UKB-TOPMed and summarize its associations with traits. Accept rsID identifiers or GRCh37 and GRCh38 coordinates, resolve the required query format, and report association totals and any result truncation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Look up trait associations for a variant identified by rsID
• Query UKB-TOPMed using GRCh37 coordinates
• Review a limited set of associations and the total count

Retrieve GTEx associations between a genetic variant and gene expression in individual tissues. Supply one rsID or genome coordinate, and receive JSON with the resolved variant, association rows, counts, and warnings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Retrieve tissue-specific eQTL associations for an rsID
• Query GTEx using GRCh37 coordinates
• Limit association rows returned as JSON

Retrieve concise FinnGen association results for one genetic variant. Accepts rsID identifiers or GRCh37/GRCh38 genome coordinates, resolves the query format, and reports association counts, truncation, and optional raw output files.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Look up FinnGen associations for a single rsID
• Query a variant using GRCh37 or GRCh38 coordinates
• Review association counts and whether results were truncated

Discover and configure Bluetooth and Wi-Fi accessories using AccessorySetupKit. Use when presenting a privacy-preserving accessory picker, defining discovery descriptors for BLE or Wi-Fi devices, handling accessory session events, migrating from CoreBluetooth permission-based scanning, or setting up accessories without requiring broad Bluetooth permissions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Design a complete document-based history lesson using the Reading Like a Historian four-part structure. Use when planning a primary source inquiry lesson or converting a textbook lesson into document-based investigation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.