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Analyzes pooled CRISPR screens with MAGeCK (Li et al 2014), covering count generation (mageck count), the RRA two-condition workflow (mageck test using alpha-RRA over per-sgRNA negative-binomial p-values), the MLE multi-condition workflow (mageck mle with explicit design matrix and beta-score output), normalization choice (median vs total vs control-sgRNA vs spike-in), sgRNA efficiency injection, paired-sample testing, time-course design, drug-screen versus dropout-screen design matrices, MAGeCKFlute and MAGeCK-VISPR downstream visualization, and decision logic for when to use MAGeCK vs JACKS / BAGEL2 / drugZ / Chronos. Use when running a fresh CRISPR screen analysis, picking RRA vs MLE for the experimental design, choosing a normalization method from QC signatures, debugging MLE convergence failure or NaN beta scores, comparing MAGeCK output across tools, or building a batch-aware multi-cell-line / multi-condition MLE design matrix.

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Creates DE-specific diagnostic and result visualizations using DESeq2/edgeR built-in functions and lightweight ggplot2 wrappers. Covers MA plot (with the shrunken-LFC compression effect), volcano (with the apeglm caveat that p-values are unchanged), PCA on VST/rlog (never raw counts), sample distance heatmaps, top-DE-gene heatmaps with the row-scaling trap, dispersion / BCV plot interpretation, p-value histogram diagnostics, plotCounts for individual genes, blind=TRUE vs FALSE rationale, and the n=3 visualization stake. Use when generating DE diagnostic plots, choosing VST vs rlog for visualization, troubleshooting suspicious plot patterns (shifted MA cloud, batch-dominated PCA, anti-conservative p-value histogram), or building a standard QC figure panel.

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Identify recurrent and driver copy number alterations across a tumor cohort with GISTIC2 (G-score, Ziggurat deconstruction, focal vs broad/arm-level analysis, q-values from permutation) and quantify copy-number signatures with the Steele 2022 COSMIC framework and the Drews 2022 CINSignatures framework. Covers driver-gene localization from recurrence peaks, distinguishing focal drivers from arm-level passengers, and the caller-sensitivity caveats of copy-number signatures. Use when finding recurrently amplified or deleted regions in a cohort, localizing driver genes, separating focal from broad events, running GISTIC2, or extracting copy-number mutational signatures.

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Run remote BLAST searches against NCBI servers using Biopython Bio.Blast.NCBIWWW. Use when identifying unknown sequences, finding homologs, picking the correct BLAST program (blastn/blastp/blastx/tblastn/tblastx/psiblast/megablast/dc-megablast), interpreting Karlin-Altschul E-values, avoiding the max_target_seqs trap (Shah 2019), choosing composition-based statistics, or limiting searches by organism. Covers RID lifecycle, database choice (nt/nr/refseq_select/swissprot), word-size and CBS taxonomy.

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Assesses patient’s understanding of how CGM data (trends, rates of change) differ from discrete SMBG fingerstick values. Triggered when clinicians hear requests to explain lag and trend arrows on CGM or teach the difference between CGM glucose and fingerstick glucose.

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This skill recommends that a multidisciplinary team, including an experienced endocrinologist, incorporates patient values and preferences while providing education about treatment options for Cushing’s syndrome. Use when planning treatment for a CS patient requiring coordinated care; triggers include persistent hypercortisolism after transsphenoidal surgery, consideration of bilateral adrenalectomy, radiotherapy, or medical therapy, and need for shared decision‑making.

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Expert-level linear algebra knowledge. Use when working with vectors, matrices, determinants, eigenvalues, linear transformations, vector spaces, inner products, decompositions, or applications in machine learning and physics. Also use when the user mentions 'matrix', 'determinant', 'eigenvalue', 'eigenvector', 'linear transformation', 'vector space', 'basis', 'orthogonality', 'SVD', 'PCA', 'least squares', 'null space', or 'rank'.

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Initialize BMad project configuration and load config variables. Use when any skill needs module-specific configuration values, or when setting up a new BMad project.

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Jev (TypeSafe System One) inner loop for ego-browser — one ~0.4 s typed decision per DOM step instead of an LLM turn. Use for multi-step clicking through a semantic page: fill known values into a form, set filters, open a row/card/menu by name, reach a page via nav or site search. Escalates login, payment, free text, canvas and content reading back to you.

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Finalize code changes for production readiness by removing duplicate logic, auditing hardcoded values, verifying branch/worktree intent, verifying build integrity, structuring clean commits with Conventional Commits format, and forcing a post-publish branch/worktree cleanup decision.

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Use when building or editing a spreadsheet or model — formulas over hardcoded values, read-back after writes, official-source data, pivot-first charts. Triggers on 'spreadsheet', 'build a model'.

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Author a 3-layer DTCG token system (primitive → semantic → component) with light/dark theming; generate CSS vars + Tailwind colors and lint hardcoded values. Use on design tokens / CSS variables.

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Load this skill whenever the project contains text, UI components, icons, form controls, data visualisations, or focus indicators — in short, almost every project. Under no circumstances hard-code colour values without verifying contrast ratios. Absolutely always ensure text meets 4.5:1, large text meets 3:1, and non-text UI elements meet 3:1 against adjacent colours. Test in light mode, dark mode, and forced-colors (high contrast) mode.

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CRM-lite for managing GEO agency prospects and clients. Track leads through the full sales pipeline: Lead → Qualified → Proposal Sent → Won → Lost. Store audit history, notes, deal values, and generate pipeline summaries. Use when user says "prospect", "lead", "client", "pipeline", "crm", "nuovo prospect", "aggiungi cliente", or when managing the business side of GEO services.

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Culture assessment and monitoring with three modes — Diagnostic for a deep session surfacing the gap between declared culture and actual culture, Review for periodic check-ins on whether behavior is shifting, and Alert Triggers for setting tripwires around culture-strategy misalignment, regression, and integration risk. Not a culture change programme — a mirror. Updates company-context.md after every run. Triggers on questions about culture, values, team dynamics, how decisions really get made, why change efforts stall, merger integration, or whether the organisation can actually execute its strategy. Based on practice by Eterdis (eterdis.com).

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Pre-share leak scanner for Claude Code skills (or any folder). Scans BEFORE you publish for secrets (API keys, tokens, private keys, .env values, high-entropy strings), PII (emails/phones), local machine paths (C:\Users\<name>, /home/<name>, /Users/<name>, UNC, internal hostnames), and client/proper-noun names from a LOCAL private dictionary; also flags service-specific code (jira/github/gitlab/slack/salesforce/notion/linear/ stripe/aws/google) that should be split into a dedicated service plugin. Use when the user says "sanitize this skill", "before sharing a skill", "check for leaks", "is this skill clean", "is this safe to commit/publish/ open-source", "scrub secrets", "redact before commit", or "pre-push leak check". Cross-platform, stdlib-only Python, fail-closed.

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Set up notebook-connector configuration for Exasol AI workflows through the Secrets Python API. Covers secure config store (`scs`) creation, the `AILabConfig` and `StorageBackend` key sources, backend-specific config values such as `db_host_name`, `db_schema`, `storage_backend`, and `huggingface_token`, Python validation, and handoff to downstream notebook-connector skills.

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Audit a dataset or access rule before it joins an identity-graph build (access rules behave like datasets in NQL here). Enumerates failure modes (hub identifiers, high-degree nodes, suspicious values, over-connected identifiers), tests hypotheses in parallel, quantifies damage by rows / edges / entities, and proposes minimal filters ranked by severity. When issues are found, returns a validated `CREATE MATERIALIZED VIEW` NQL the caller can run to produce a graph-ready clean source; if the data passes, says so and recommends it unchanged. Plans and authors the clean-view NQL; does not execute it. Use when: "audit this dataset before the graph build", "find bad edges in <source>", "check identity data quality", "recommend filters for the graph build", "quantify damage from <identifier_type>", "pre-graph DQ". (narrative-identity)

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Produce a coverage & quality profile of a Narrative dataset (or access rule): row count, per-column null/fill rate, cardinality, ranges, top-values, inferred column shape, and quality flags. Reads bundled stats + sample first, recovers missing/stale stats by configuring and recalculating them, and escalates to a cheap `/write-nql` query only for a measure no stat can provide. Descriptive, not prescriptive. Use when: "profile dataset N", "what does dataset N look like", "coverage and quality of <dataset>", "what id types does N emit", "null rates / cardinality for <dataset>", "is this dataset's stats fresh". (narrative-common)

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Full type inference philosophy (never cast, never annotate inferred values), Register module declaration, from narrowing on hooks and Link, strict:false for shared components, getRouteApi for code-split typed access, addChildren with object syntax for TS perf, LinkProps and ValidateLinkOptions type utilities, as const satisfies pattern.

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史蒂芬·海斯（Steven Hayes）的接纳承诺疗法（ACT）：包含心理灵活性六大核心过程的 Hexaflex 模型。

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Terapia de Aceptación y Compromiso (ACT) según Steven Hayes: modelo Hexaflex con los seis procesos nucleares de la flexibilidad psicológica.

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Терапия принятия и ответственности (ACT) Стивенса Хейса: модель Гексафлекс с шестью ключевыми процессами психологической гибкости.

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Поведенческая активация при депрессии: разорвать порочный круг, мониторинг активности, планирование недели и действия на основе ценностей.

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