Identifies translationally meaningful paths for bioinformatics findings by mapping omics or computational discoveries to diagnosis, stratification, prognosis, treatment-response, monitoring, or target-nomination use cases, while auditing bridge evidence, assayability, and validation burden. Use this skill when a user wants to know whether a bioinformatics finding can be framed as a stronger translational topic without overclaiming clinical relevance. Always separate statistical signal from translational value, and never imply clinical utility, targetability, or validation depth without explicit evidence support.

Makes AI-generated content sound genuinely human — not just cleaned up, but alive. Use when content feels robotic, uses too many AI clichés, lacks personality, or reads like it was written by committee. Triggers: 'this sounds like AI', 'make it more human', 'add personality', 'it feels generic', 'sounds robotic', 'fix AI writing', 'inject our voice'. NOT for initial content creation (use content-production). NOT for SEO optimization (use content-production Mode 3).

Finds translational opportunities that connect basic-research discoveries to clinically meaningful use cases such as diagnosis, stratification, prognosis, treatment response prediction, monitoring, or therapeutic development. Use this skill when a user wants to turn a mechanism finding, pathway signal, cellular phenotype, experimental observation, or omics discovery into a stronger translational research direction. Always separate mechanistic relevance from translational usability, and never present a basic finding as clinically actionable unless the evidence supports that level.

Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.

Compliance OS — meta-orchestrator that lets compliance teams CONFIGURE which frameworks apply, COMPUTE cross-framework control overlap, SIMULATE internal audits, and CONSOLIDATE evidence across multiple frameworks. Four decisions: (1) Given a company profile, which of the 12 supported frameworks apply (ISO 27001/13485/42001/14971, EU AI Act, MDR 745, GDPR, SOC 2, FDA QSR, NIST CSF 2.0, NIS2, HIPAA)? (2) Across selected frameworks, which controls overlap and how much evidence reuses? (3) For a given framework + scope, what does a realistic mock audit produce — drawing from the 205-scenario library? (4) Across selected frameworks, what's the unified evidence checklist with reuse map? Use when standing up a multi-framework program, planning the annual audit calendar, or preparing for certification stage 1. Does NOT replace per-framework skills (it orchestrates them).

Use when running prognostic survival analysis on a clinical cohort with time-to-event data to estimate univariate and multivariable Cox proportional hazards models, export result tables, and generate forest plots. NOT for: nomogram construction, calibration curves, time-dependent ROC analysis, or model training/feature selection beyond the built-in univariate screening rule.

The meta-framework for how a company runs — the connective tissue between all C-suite roles. Covers operating system selection (EOS, Scaling Up, OKR-native, hybrid), accountability charts, scorecards, meeting pulse, issue resolution, and 90-day rocks. Use when setting up company operations, selecting a management framework, designing meeting rhythms, building accountability systems, implementing OKRs, or when user mentions EOS, Scaling Up, operating system, L10 meetings, rocks, scorecard, accountability chart, or quarterly planning.

Use when performing sample-level dimensionality reduction and visualization on abundance or OTU-style matrices with a companion group file, generating UMAP and/or t-SNE coordinates and plots for group separation assessment. NOT for: differential expression testing, single-cell workflows requiring dedicated embeddings pipelines, or analyses without a sample grouping file.

Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.

Use when analyzing transcription factor (TF) regulatory networks using Dorothea database. Input gene list, identify regulating transcription factors, generate TF-Target network visualization. For: transcription factor enrichment analysis, gene regulatory network research.

Use when you need a standardized R CLI workflow to run two-class SVM-RFE feature ranking on an expression-like matrix, choose an informative feature count from cross-validated error, and generate reproducible ranking and error plots. NOT for regression, multi-class classification, missing-value imputation, or remote data fetching.

Store a learning, pattern, or decision in the memory system for future recall

Deploy a temporary n8n test instance (or generate a local docker run command) via the internal "Nathan" bot, from the repo instead of Slack. Use after opening a PR to offer the user a live test instance, or whenever someone asks to spin up / deploy a test instance for a branch.

Fetch and display the full transcript from a Loom video URL. Use when the user wants to get or read a Loom transcript.

Reviews a GitHub pull request like a thoughtful human reviewer and writes the feedback to a markdown file. Prioritizes context, architecture fit, solution complexity, bugs, security edge cases, and missing tests. Use when given a PR URL to review, or when the user says /human-like-code-review.

Manage stacked branches and pull requests with the gh-stack GitHub CLI extension. Use when the user wants to create, push, rebase, sync, navigate, or view stacks of dependent PRs. Triggers on tasks involving stacked diffs, dependent pull requests, branch chains, or incremental code review workflows.

Authors n8n database migrations. Use when creating or modifying files under packages/@n8n/db/src/migrations/, when the user asks to add a column, table, index, foreign key, or backfill, or when the user mentions DB migrations or TypeORM migrations.

Unified read / write / stats / recalc / chart capability for Excel / CSV (.xlsx / .xlsm / .csv / .tsv). Trigger scenarios: reading cells, summarizing, grouping, highest/lowest values, total revenue, editing cell values or formulas, inserting rows/columns, adding total rows, inserting charts (bar/line/pie), freezing panes, auto-filter, conditional formatting, CSV ↔ XLSX conversion, building rosters / sales sheets / financial models, diagnosing #REF! / #DIV/0! formula errors, triggering recalculation. Typical phrasings: "who has the highest sales in this sheet", "total Q1 revenue", "change B2 to 500", "add a total row", "create a new roster", "convert csv to xlsx", "freeze the first row", "highlight negatives in column A red". Load this skill whenever the user mentions an .xlsx / .csv file or the final deliverable is a spreadsheet.

Excel / CSV（.xlsx / .xlsm / .csv / .tsv）读/写/统计/重算/图表一体化。触发场景：阅读单元格、汇总分组、谁最高谁最低、总收入、改单元格或公式、加行列、加合计行、插入图表（bar/line/pie）、冻结首行、加筛选、条件格式、CSV ↔ XLSX 互转、新建花名册/销售表/财务模型、排查 #REF!/#DIV/0! 公式错误、触发重算。典型说法："这表里谁销量最高"、"Q1 总收入"、"把 B2 改成 500"、"加一行合计"、"新建花名册"、"csv 转 xlsx"、"冻结首行"、"A 列负数标红"。用户提到 .xlsx / .csv 文件或最终产出是表格都加载本技能。

Triggered when the user wants to add, modify, view, or delete widgets (desktop components / mini apps / dashboard cards) on the OpenLoaf workbench / desktop. Typical phrasings: "add a weather widget", "build a pomodoro component", "change the color of this widget". **Not for**: one-off chart rendering inside a chat message (→visualization-ops-skill), AI image generation (→cloud-media-skill), casually mentioning "clock / countdown" in conversation (→answer directly).