「sso」の検索結果

統計的推論（線形回帰 / 重回帰 / OLS / Lasso / Ridge / 分位点回帰、ロジスティック回帰 / ポアソン / GLM / オッズ比、 混合効果 / マルチレベル / パネル固定効果、仮説検定 / t 検定 / χ² / ANOVA / 有意差、生存時間分析 / Kaplan-Meier / Cox、 ベイズ推定 / MCMC / Stan）を実行したら必ずセットで出す図と値のルーター。sm.OLS, smf.ols, LinearRegression, LassoCV, QuantReg, sm.Logit, sm.GLM, LogisticRegression, mixedlm, PanelOLS, ttest_ind, mannwhitneyu, chi2_contingency, pingouin, lifelines, CoxPHFitter, cmdstanpy, arviz がコードに現れたとき、またはユーザーが「回帰して」「検定して」 「有意差はあるか」「生存曲線を描いて」「ベイズで推定して」と言ったときに使う。診断・残差・図に言及がなくても適用する。 SKILL.md のルーティング表で手法を特定し、対応する references/<手法>.md を読んでから実行する。A/B テスト・因果推論は causal-inference-diagnostics、予測 ML・時系列は predictive-modeling-diagnostics を使う。

Design a lesson that embeds PERMA wellbeing elements alongside academic learning objectives. Use when planning lessons that intentionally support both content mastery and student flourishing.

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。

Modify a lesson plan using SIOP sheltered instruction principles to support language learners across all four skills. Use when planning lessons for classes that include EAL students.

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。

Operate a reusable technical book manuscript workspace with writing structure, reader persona SSOT, review rules, and optional Markdown to Re:VIEW/PDF support. Use when organizing a book manuscript repo, defining or reviewing target readers, standardizing chapter/section files, setting writing/review agents, handling publisher proof comments, or assessing an existing writing workspace. Triggers on book writing workspace, reader persona, reader audience, 読者ペルソナ, persona SSOT, technical book project, 執筆ワークスペース, manuscript workflow, Re:VIEW workspace, proof PDF, annotated proof, publisher proof comments, 校正, 校正注釈, and 初校.

Reviews JUCE audio plugin code for JUCE-specific correctness issues: thread safety, APVTS parameter patterns, MessageManager usage, ValueTree, and MIDI handling. Use when the user asks to review a JUCE plugin, check a processBlock, audit parameter handling, or asks "is this JUCE code safe?". Trigger on phrases like "review my JUCE plugin", "check my AudioProcessor", "is this APVTS usage correct?", or when you see AudioProcessor, AudioProcessorEditor, or AudioProcessorValueTreeState in the code.

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。

Reviews YUP audio plugin code for YUP-specific correctness issues: AudioProcessor lifecycle, AudioParameterBuilder and AudioParameterHandle usage, editor gestures, AudioBusLayout handling, CLAP/VST3 wrapper contracts, state recall, and MIDI safety. Use when the user asks to review a YUP plugin, check a processBlock, audit parameter smoothing, or asks "is this YUP code safe?". Trigger when you see yup::AudioProcessor, yup_audio_plugin, AudioParameterBuilder, AudioParameterHandle, AudioProcessorEditor, or createPluginProcessor in the code.

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。

Drafts a board resolution authorizing voluntary corporate dissolution via unanimous written consent of directors. Covers Plan of Complete Liquidation adoption, shareholder approval directive, officer authorizations, and execution provisions. Use when initiating voluntary dissolution, preparing corporate wind-down records, or drafting pre-shareholder dissolution authority under state corporation law.

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。

Develop, challenge, and document Chisel processor microarchitecture designs before implementation. Use when discussing or writing design documents for processor pipelines, queues, issue logic, rename, ROB, LSU, caches, MSHRs, forwarding, wakeup, replay, flush, privilege, exceptions, or other cycle-accurate hardware mechanisms; when converting design conversations into stable Markdown specifications; or when reviewing a proposed Chisel CPU design for correctness, timing, area, verification cost, and cross-document consistency.

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。

Author a complete both-seats coaching lesson for a BBA-NATIVE bidding scenario and produce all served artifacts in one consistent pass: curate the pool → tokenized coaching-curated/<scn>.pbn → gated promote to served coaching/<scn>.pbn → resolve to South=student coaching-non-rotated/<scn>.pbn (bridge-classroom) → update both toc.json files. Invoke as `/coach-lesson <Scenario>` (e.g. `/coach-lesson Fourth_Suit_Forcing`). Use ONLY for conventions BBA bids natively (its bba/<scn>.pbn [Auction] is the answer key — Basics, New_Minor_Forcing, Fourth_Suit_Forcing). For conventions BBA CANNOT bid (Spiral Raises), use the py/spiral_auction.py substitution path instead — not this skill.

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 working with Aws Sso — aWS IAM Identity Center (SSO) management. Covers SSO instance configuration, permission sets, account assignments, user and group provisioning, session policies, and access auditing. Use when managing single sign-on access to AWS accounts, reviewing permission sets, auditing account assignments, or configuring identity source integration.

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。

The one association system (platform.associations) and schema-qualified table refs. Use when touching an M2M junction table, an associate_*/attach_*/tag_* RPC, FK columns used as tags, a 'things attached to X' list or card, or a PGRST205 / 42703 table-not-found on a bare .from().

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。

[73] CLOSE. Record and maintain Lessons in MEMORY.md after a problem is solved or the user confirms success. Use when capturing a new lesson, moving lessons through the pipeline, or enhancing Project Architecture Quick Reference with new insights.

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。

Claude Codeでの修正や繰り返し作業から、確信度付きの小さな行動ルールを抽出し、プロジェクト別に管理してスキルやコマンドへ育てるスキル。

• 修正指示や反復作業の知識化
• プロジェクト別の規約管理
• 行動ルールのスキル・コマンド化

Interactive lesson tutor for the AI Engineering from Scratch curriculum. Reads LEARNING.md, fetches the next lesson, teaches it section by section in the terminal, quizzes at the end, and records progress. Works cloned or entirely over raw.githubusercontent.com — no setup required. Trigger phrases: "next lesson", "teach me", "continue the course", "let's learn", "resume learning"

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。

Test a learner on a single Claude Code tutorial lesson (01-10) with 10 questions, scoring answers and flagging weak spots. Use before, during, or after a lesson. Don't use for whole-tutorial assessment or explaining a topic instead of testing 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。

Design a coherent assessment-and-practice lesson using zone-of-proximal-development principles: independent diagnosis, targeted review, supported practice, fresh independent checks, and an evidence-based next step. Use for 习题课（最近发展区）, 最近发展区, 测验习题课, diagnostic review lessons, or guided-to-independent practice. Can also run one practice cycle in chat; do not turn a request for a single exercise or theory explanation into a full lesson.

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。

Multi-stage series — a request for several classrooms that belong together, like 「7 天学 Python」, a four-week onboarding track, a semester unit split across lessons, or "turn this book into a lesson per chapter". Clarifies the series brief, gets the user's explicit sign-off on the full lesson list before spending anything, then builds the stages one at a time into a shared folder. Use when the ask is a sequence of classrooms; not for a single stage, however large.

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 writing or debugging integration tests for error processors in packages/core/src/processors/. Covers the MockLanguageModelV2 pattern for simulating API errors and verifying retry behavior, plus the build prerequisites for focused vitest runs.

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。

Professor's review queue — student output waits here for professor approval before going to clients or courts. Only active if "formal review queue" supervision style was chosen at setup; otherwise dormant. Use when the professor wants to see what's waiting for review, approve, edit-then-approve, or return an item.

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。

Change between pages, chapters or scenes with a paper-burn transition, a ragged ember line that sweeps across the frame, chars the old page ahead of it and reveals the next scene live behind it, written as one WebGL composite pass over a frozen frame and a scissored live scene. Covers the front's silhouette, the luminous ember gradient with a beading gold thread, torn porous charcoal that is alpha-masked, baked noise, finite-difference slopes, the 0.75-scale live scene, sweep direction, timing, reduced motion and visibility pausing. Use for a burn transition, paper burn, cinder wipe, chapter change by fire, charring page reveal, burn-away section change, ember dissolve, or when a fade, slide or generic dissolve between scenes reads as cheap.

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。

Econometrics skill for machine learning methods in causal inference. Activates when the user asks about: "causal forest", "generalized random forest", "GRF", "double machine learning", "DML", "debiased machine learning", "LASSO for variable selection", "post-LASSO", "heterogeneous treatment effects", "CATE", "conditional average treatment effect", "BLP analysis", "CLAN analysis", "causal tree", "honest estimation", "因果森林", "双重机器学习", "异质性处理效应", "条件平均处理效应", "LASSO变量选择", "机器学习因果推断", "去偏机器学习"

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Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step Stata pipeline an applied economist runs on every paper — (1) data import & cleaning (use/import, destring, misstable, duplicates, merge assert), (2) variable construction (gen/egen/winsor2/xtile/xtset with L./F./D.), (3) descriptive statistics & Table 1 (tabstat/balancetable/asdoc), (4) classical diagnostic tests (sktest/swilk/hettest/imtest/xtserial/xttest3/vif/dfuller/kpss/hausman/estat overid), (5) baseline modeling (reg/xtreg/reghdfe/ivreg2/ivregress/csdid/did_imputation/eventstudyinteract/sdid/rdrobust/synth/psmatch2/teffects/heckman/qreg/ppmlhdfe), (6) robustness battery (bacondecomp/honestdid/rwolf/ritest/wildbootstrap/oster), (7) further analysis (subgroup/triple-diff/interactions/medsem/marginsplot/binscatter by group), (8) publication-ready tables & figures (esttab/outreg2/estout/coefplot/marginsplot/rdplot/twoway combined). **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation, IPTW + g-formula + TMLE doubly-robust triplet via `teffects ipw` / `teffects ipwra` / `teffects aipw` / `eltmle`, Mendelian randomization via `mrrobust` (IVW / Egger / weighted median) and `mregger` / `mrpresso`, KM / Cox / AFT / RMST survival via `sts` / `stcox` / `streg` / `strmst2`, E-value sensitivity via `evalue` (Linden-Mathur), principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `ddml` / `pdslasso`, S/T/X/R/DR meta-learners via `crforest` and `ddml interactive`, causal forest via `crforest` / `cforest`, BART/BCF via `bart` / `bartCause`-style externals, CATE distribution + policy tree via `crforest`, off-policy evaluation, conformal causal externals, fairness audit, DAG learning via `pcalg` / external Python callouts). Use when the user asks for a complete Stata empirical analysis, wants a reproducible .do-file pipeline, needs a Stata counterpart to the Python StatsPAI / Full-empirical-analysis-skill, or names a specific Stata step in isolation ("run reghdfe with two-way clustering", "csdid event study", "winsor2 at 1%", "esttab to LaTeX", "coefplot with CI", "ivreg2 weak-IV test", "synth_runner placebos", "teffects psmatch balance check"). Mode A triggers on "target trial emulation Stata", "teffects ipw aipw", "eltmle", "mrrobust", "mregger weighted median", "stcox AFT survival", "strmst2", "evalue Stata", "STROBE Stata", "公共健康 Stata", "流行病学 Stata". Mode B triggers on "ddml Stata", "pdslasso", "crforest causal forest Stata", "policy tree Stata", "因果机器学习 Stata".

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。

Analyze recent code changes via git history and extract software engineering lessons. Use when the user asks 'what is the lesson here?', 'what can I learn from this?', 'engineering takeaway', 'what did I just learn?', 'reflect on this code', or wants to extract principles from recent 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。

SAML SSO assertion attack playbook. Use when testing signature validation, assertion wrapping, audience restrictions, ACS handling, XML trust boundaries, and enterprise SSO flaws.

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。