「sso」の検索結果

Task-sized forensic analysis of Vivado synthesis and routed timing evidence for processor RTL. Use when Codex must trace a named setup or hold path, audit whole-design timing populations, compare implementation runs, map primitive, LUT, CARRY, BRAM, DSP, MUXF, register, and routed-net stages back to Chisel or generated RTL, distinguish logic from routing pressure, identify missing DCP queries, or write an evidence-backed timing report. This skill is read-only by default and stops at ranked modification directions. Use optimize-chisel-fpga-timing for RTL edits and routed A/B implementation closure.

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Delimits putative species boundaries from molecular data within the de Queiroz 2007 unified-lineage framework using ASAP (Puillandre 2021 successor to ABGD), mPTP C++ (Kapli 2017 successor to bPTP; bPTP is Python NOT R), GMYC single/multi-threshold (Pons 2006; Fujisawa 2013), multilocus BPP v4 with prior calibration from data (NOT defaults; Yang 2015), SNAPP + BFD* for SNP delimitation, DELINEATE (Sukumaran 2021) speciation-process modeling to address Sukumaran & Knowles 2017 PNAS critique that MSC delimits structure not species, integrative-taxonomy congruence (Padial 2010; Carstens 2013), Dsuite for introgression testing before sister claims (Malinsky 2021), and Meyer & Paulay 2005 barcoding-gap-absence caveat. Use when delineating species from DNA barcoding data, resolving cryptic complexes, choosing among ASAP/mPTP/BPP/DELINEATE, calibrating BPP priors, distinguishing introgression from ILS, or applying the Sukumaran-Knowles oversplitting correction.

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Identifies essential genes from CRISPR-Cas9 fitness screens using BAGEL2 (Kim & Hart 2021 Genome Med), a Bayesian classifier scoring per-gene Bayes Factors via log-likelihood ratios over per-sgRNA fold changes, calibrated against CEGv2 core-essentials (Hart 2017 G3, ~684 genes) and NEGv1 non-essentials (Hart 2014, ~927 genes). Covers the fc + bf + pr workflow, the linear-extrapolation improvement over BAGEL1 truncation, multi-target off-target correction, tumor-suppressor sensitivity (BAGEL2 detects enrichment), and BF calibration (BF >6 ≈ 90% posterior per Hart 2017; ~5% FDR by BAGEL convention). Use when classifying essential vs non-essential genes, calibrating BAGEL2 thresholds against PR curves, identifying tumor suppressors alongside essentials, comparing BAGEL2 hits to MAGeCK / drugZ, or generating publication-quality essentiality calls.

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Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Colic et al. 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving greater sensitivity to small-effect hits than MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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Estimate causal effects of an exposure on an outcome from GWAS summary statistics using genetic instruments. Implements IVW (fixed/random), MR-Egger, weighted median/mode, MR-RAPS, CAUSE, GSMR-HEIDI, MR-PRESSO, MVMR, MR-Clust, LCV, and LHC-MR via TwoSampleMR, MendelianRandomization, MR-PRESSO, cause, and lhcMR. Use when testing causal direction between traits, evaluating drug-target effects via cis-pQTL/cis-eQTL, performing multivariable mediation MR, distinguishing causation from correlated horizontal pleiotropy, or producing STROBE-MR-compliant sensitivity batteries.

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Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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Comprehensive backend development guide for Langfuse's Next.js 14/tRPC/Express/TypeScript monorepo. Use when creating tRPC routers, public API endpoints, BullMQ queue processors, services, or working with tRPC procedures, Next.js API routes, Prisma database access, ClickHouse analytics queries, Redis queues, OpenTelemetry instrumentation, Zod v4 validation, env.mjs configuration, tenant isolation patterns, or async patterns. Covers layered architecture (tRPC procedures → services, queue processors → services), dual database system (PostgreSQL + ClickHouse), projectId filtering for multi-tenant isolation, traceException error handling, observability patterns, and testing strategies (Jest for web, vitest for worker).

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Save, load, update, delete, merge, and navigate entity objects using TMS Aurelius ORM (TObjectManager). Use when the user asks how to persist or retrieve data with Aurelius, manage object lifetime, work with associations or collections at runtime, handle transactions, use cached updates, or control concurrency. Does NOT cover query DSL or criteria/projections (those are in a separate skill). Triggers on requests like "save an entity with Aurelius", "load a customer from the database", "add items to a collection", "how do I update an object", "flush changes", "delete a record with Aurelius", "merge a transient object", "how do transactions work in Aurelius", "batch updates Aurelius".

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Map Delphi classes to a relational database using TMS Aurelius ORM attributes. Use when the user asks to create entity classes, add Aurelius mapping to existing classes, fix or review mapping attributes, explain how a class is mapped, or work with associations, inheritance, automapping, nullable fields, blobs, or composite identifiers. Triggers on requests like "create Aurelius entities for...", "map this class to Aurelius", "add ORM mapping", "fix the mapping on this class", "how do I map a one-to-many in Aurelius".

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Agentes autônomos são sistemas de IA que conseguem decompor objetivos, planejar ações, executar ferramentas e se auto-corrigir sem orientação humana constante. O desafio não é torná-los capazes - é torná-los confiáveis. Cada decisão extra multiplica a probabilidade de falha. Esta habilidade cobre loops de agentes (ReAct, Plan-Execute), decomposição de objetivos, padrões de reflexão e confiabilidade em produção. Insight-chave: taxas de erro compostas matam agentes autônomos. Uma taxa de sucesso de 95% por passo cai para 60% b

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Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

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Establish SAML 2.0 identity federation between on-premises Active Directory and Azure AD (Microsoft Entra ID) for seamless cross-domain authentication and SSO to cloud applications.

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【穴埋め雛形】このプロジェクト固有の「存在設計・体験設計」のSSOT。judgment-harness のプロジェクト層スキルとして、立ち上げ時に本ファイルを埋めて使う（雛形のままなら未確定＝推測で決めない）。デザイン/UI/モーション/コピーの方針判断、見た目や体験のトーン決め、画面・コンポーネント設計の前段で必ず参照する。

METAINFORMANT rules for directory scripts/gwas/association. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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Grundlagen der lösungsfokussierten Kurztherapie nach Steve de Shazer und Insoo Kim Berg: Wunderfrage, Ausnahmen-Exploration, Skalierung, Ressourcenaktivierung.

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Summarises one sprint from the board export, sprint report and review or stand-up notes the user provides into a one-page DRAFT sprint review summary: the sprint goal as written, items delivered, items not delivered with the reasons the sources state, carry-over, impediments, metrics exactly as given, and numbered questions for the retrospective, every row referenced to a work item or note fragment and every missing fact marked UNKNOWN. Use when the user asks to "summarise the sprint", "write the sprint review", "what did we deliver this sprint", "list the carry-over and blockers" or "draft the sprint recap". Do not use for the retrospective write-up or themed lessons across several sprints, use lessons-learned-synthesis instead; for a weekly project status report, use project-status-tracker. Drafts for human review; never approves, authorises or signs off.

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プロジェクトのデザインシステムを SSOT として作成・監査・更新する。トークン、デザインシステムに関わるアクセシビリティ、Typography、Motion、aesthetic direction の作業に使う。デザインシステムに紐づかない単発UI実装や個別デザインレビューには使わない。ユーザーが /ai-design-system と入力したら使う。

Write an evergreen teaching longform for non-technical readers built from your own real material (systems you run, dated failures, numbers), structured as a stitched lesson that can live as a blog post, a GitHub guide hub and a newsletter issue. Use when turning lived engineering experience into a durable how-to. Triggers: /guide, make a guide, build a lesson, evergreen guide.

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Search, inspect, and record SIPS-owned Memory Fabric lessons. Use when a task needs prior lessons, recurring-fix memory, recall health, scoped historical context, or when a just-fixed bump or error should be recorded.

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Use WordPress' HTML API for safe server-side HTML inspection and mutation instead of regex, fragile string replacement, or DOMDocument. Covers WP_HTML_Tag_Processor, WP_HTML_Processor, set_attribute, remove_attribute, add_class, remove_class, set_modifiable_text, serialize_token, custom data attribute name mapping, and WP 6.9 behavior where attribute/text setters escape character references. Use when plugin code modifies rendered HTML, block output, shortcodes, content filters, widget markup, email fragments, or user-provided HTML.

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在授权渗透测试中挖掘开放重定向（open redirect）及其危害升级。当目标按用户可控参数做跳转（登录后 next、SSO returnUrl、注销、短链、回调）时使用——典型场景：绕过 URL 白名单跳到外站钓鱼、泄露 token/Referer、开放重定向作为 SSRF/OAuth token 窃取的跳板。适用目标类型 Web / REST API / SSO。触发场景包括用户说"测下开放重定向""这个 next/redirect 参数能跳外站吗""绕过重定向白名单""open redirect 偷 token"。输出：可跳外站的参数 + PoC + 危害升级的 finding（含 killed 记录）。

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在授权渗透测试中挖掘认证/授权绕过（authentication & authorization bypass）。当目标的登录、SSO/OAuth/SAML、2FA、会话、密码重置、邮箱验证等鉴权环节可能被逻辑缺陷或解析差异绕过时使用——典型场景：SAML 签名/entityId 绕过、OAuth `response_type`/`redirect_uri` 篡改窃取 token、邮箱规范化/预验证绕过、2FA 可预测/可跳过、会话类型混淆。适用目标类型 Web / REST API / SSO。触发场景包括用户说"测下认证绕过""这个 SAML/OAuth 有没有问题""能不能跳过 2FA""邮箱验证能绕吗"。输出：绕过路径 + 越权访问证据的 finding（含 killed 记录）。

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Configures deep linking in Flutter using Android App Links (assetlinks.json) and iOS Universal Links (apple-app-site-association) integrated with GoRouter v17.x navigation. Use this skill when setting up HTTPS domain association, handling magic links or email verification links, routing marketing campaign URLs (Google Ads, Facebook Ads) into the app, implementing password reset or invite flows via deep links, fixing deep link verification failures, troubleshooting GoRouter deep link integration, or any time the words 'deep link', 'universal link', 'app link', 'assetlinks', 'AASA', 'magic link', or 'deeplinking' appear in the request. Avoids deprecated custom URL schemes (myapp://).

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Social authentication and OAuth/SSO for Flutter supporting Google Sign-In, Apple Sign-In, Facebook Login, and LINE Login. Use when implementing social login, OAuth flows, sign-in with Google/Apple/Facebook/LINE, SSO integration, third-party authentication, platform credential configuration (SHA-1 fingerprint for Android, Service ID for Apple, App ID for Facebook, Channel ID for LINE), native auth flows, minimal viable examples, error handling patterns, or troubleshooting authentication failures, token handling, and sign-in errors (ApiException, PlatformException).

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