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Draws original cosy animal scenes as hand-authored SVG cards (480x360) in a grainy gouache, risograph-like style. Identifying traits are flat shapes with no outlines on a hot-pink card; soft 3-4 stop gradient shading on every rounded form, darker toward edges and base; a fine dark multiply grain that gathers at each shape's edges; a chalky purple-pink shadow pooled under the group; and big glossy eyes with cream star highlights, framed by grained autumn leaves and sparse cream sparkles. It covers design and coding moments with one cute animal at a laptop, monitor, desk, window, mug or book stack. Use when someone asks for grainy, gouache, riso, risograph, textured, cosy, cozy, autumn, hygge, storybook or children's-book style animal art, or for an empty state, onboarding step, 404 page, blog header, feature spot or marketing card with a warm handmade feel.

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Draws original hand-written SVG spot illustrations in the One-line style, joyful action figures doing design and coding work. One continuous uniform black line (2.3 px at 480 wide) loops and doubles back to draw the whole figure. Flat coral, periwinkle and white patches sit behind the line, wobbly and deliberately knocked 2–6 px out of register like a misprinted riso. Tops carry coral stripe lines, and there is no ground plane, only a squiggle, a curl or a few speed strokes, all on warm beige paper. Covers the palette, a route-building toolkit (tube limbs, hand and shoe loops, wobble patches, masks), poses, composition and a render-critique loop. Use when asked for one-line, single-line, continuous-line, misregistered, riso-style or playful doodle illustrations, for empty states, onboarding, 404 pages, blog headers, feature spots, launch announcements or marketing cards.

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Draws hand-authored SVG spot illustrations in a halftone line style for design and coding scenes. Identifying traits are a confident black line with a slight hand wobble and contours left open, grey tones made only of fine halftone dots clipped to shapes, a few solid black masses (hair, leaves, a bag, a black tee), warm off-white paper and no colour at all. Covers the two-ink palette, line weights, the open-contour and stipple techniques, a seeded compiler that turns tone tags into dot fills, rubbery long-limbed characters and expressive hands, doodles, floor pools and a render-and-critique loop. Use when someone asks for halftone, stipple, dotted, risograph-like, newsprint, black-and-white, monochrome line, editorial line or zine-style illustrations, or for empty states, onboarding, 404 pages, blog headers, feature spots and marketing cards in that look.

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Generates professional infographics with various layout types and visual styles. Analyzes content, recommends layout and style, and generates publication-ready infographics. Use when user asks to create "infographic", "信息图", "visual summary", or "可视化".

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DO NOT INVOKE when the primary request explicitly asks to convert, migrate, modernize, or rewrite a legacy/old-style project to SDK style; use msbuild-modernization. Migration prompts often mention ToolsVersion, explicit Compile/Reference entries, packages.config, or Microsoft.CSharp.targets, but merely reviewing or auditing a file that contains those patterns remains in scope here. USE FOR broad review, audit, lint, or maintainability/correctness checks of project/build files, including custom targets; prioritized cross-cutting findings; discrete anti-patterns; F# ordering/FS0039; cross-platform paths; and focused Import safety verdicts. Review/audit is analysis-only unless fixes are requested. For concrete property/item defects use property-patterns/item-management; use target-authoring for implementation and extension-points for NuGet auto-import/layout discovery. Exclude non-MSBuild systems.

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Add, configure, or review Ignite UI for Blazor Lite component support in Blazor applications. USE FOR: installing IgniteUI.Blazor.Lite or IgniteUI.Blazor.GridLite, registering AddIgniteUIBlazor() in Blazor Server, WASM, Hybrid, or split Blazor Web App projects, adding @using IgniteUI.Blazor.Controls, wiring the theme stylesheet, picking the right host page, locating the GridLite stylesheet path, explaining single-project vs split Server/Client Web App setup differences, and checking where an interactive render mode is needed for Ignite UI components to work. DO NOT USE FOR: general Blazor component authoring without Ignite UI, choosing app architecture or render mode from scratch (see create-blazor-project), JavaScript interop (see use-js-interop), authentication (see configure-auth), prerendering (see support-prerendering), or layout/component design questions that need no Ignite UI setup.

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Publication-grade correctness and legibility rules for final-deliverable scientific figures, not exploratory plots. Use for a figure that will ship in a report, paper, export, or kept artifact. Covers data fidelity, label economy, color threading, chart choice, layout, and render-then-verify QA without imposing a visual house style. For multi-panel composition use `figure-composer`; for whole-paper ordering use `paper-narrative`.

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VS-Enhanced Academic Style Humanizer - Transforms writing patterns to achieve authentic scholarly voice Applies transformations from G5 analysis to create natural academic prose Use when: improving AI-assisted writing quality, preparing manuscripts, enhancing scholarly voice Triggers: humanize, transform, make natural, improve writing quality, improve style

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Prose quality checker for Quarto (.qmd) files, grounded in William Zinsser's *On Writing Well* (30th Anniversary Edition). Checks for clutter, weak verbs, hollow qualifiers, clichés, inflated academic voice, poor leads and endings, pronoun and tense inconsistency, and unclear explanation. Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to improve prose quality, tighten writing, reduce clutter, or apply Zinsser's writing principles to a draft. For grammar and punctuation, use the proofread skill. For APSA style rules, use the apsa-style skill. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.

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Expert copy editor for Quarto (.qmd) files. Checks grammar, spelling, punctuation, and academic writing quality. Produces a structured markdown report organized by document section — never modifies the source file. Use when asked to proofread, check grammar, fix typos, or review prose in a .qmd document. For APSA style rules (numbers, citations, capitalization, abbreviations, neutral language), use the apsa-style skill instead. Supports an optional output-file argument and an optional @sec-label argument to restrict checking to one section.

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Orchestrate the complete post-first-draft polishing pipeline for an academic LaTeX paper by invoking five existing skills in fixed order: (1) paper-polish, (2) paper-self-revise, (3) paper-style, (4) paper-polish again, (5) reference-verify. Trigger when user says "paper pipeline" / "paper-pipeline" / "论文流水线" / "全流程打磨" / "一条龙打磨" / "初稿打磨" / "full polish pipeline" / "run the whole pipeline", or wants the entire post-draft polishing sequence run on a paper folder. Use this skill whenever the user asks for several paper-finishing steps (polish + revise + style + reference check) on one manuscript in one go, even if they don't name every individual skill.

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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".

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Detect and humanize AI-generated Chinese text. 20+ rule detection categories plus statistical features (sentence-length CV, short-sentence fraction, comma density, perplexity, GLTR, DivEye) plus scene-aware LR fusion (rule × 0.2 + LR × 0.8) trained on three scenes: general / academic / longform 长文本 (≥1500 字)。Unified CLI: ./humanize {detect,rewrite,academic,style,compare}. 8 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo/novel)。 Multi-paragraph rewriting (paragraph length CV、跨段 trigram 重复) plus best-of-N humanize (默认 N=10 取最低 LR)。165 replacement patterns + CiLin 同义词词林 38873 with collision blacklist。 Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重)。 Pure Python, no dependencies, offline。v5.0.0 — HC3 fused 准确率 95%、学术 hero 100→35 (-65)、 工作汇报 96→13 (-83)、长篇博客 96→41 (-55)。 Use when user says: "去AI味", "降AIGC", "人性化文本", "humanize chinese", "AI检测", "AIGC降重", "去除AI痕迹", "文本改写", "论文降重", "知网检测", "维普检测", "AI写作检测", "让文字更自然", "detect AI text", "humanize text", "reduce AIGC score", "make text human-like", "去ai化", "改成人话", "去机器味", "降低AI率", "过AIGC检测", "长文本改写", "小说改写"

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Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **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 R pipeline an applied economist runs on every paper — (1) data import & cleaning (read_dta/read_csv, naniar, janitor, validate-merges), (2) variable construction (mutate/across/winsorize/group_by + lag/lead with dplyr), (3) descriptive statistics & Table 1 (gtsummary, modelsummary::datasummary, tableone), (4) classical diagnostic tests (shapiro/jarque.bera.test/bptest/dwtest/bgtest/vif/adf.test/kpss.test/Hausman), (5) baseline modeling (fixest::feols, ivreg, did::att_gt, eventstudyr, sun_ab, did_imputation, synthdid, rdrobust, MatchIt, WeightIt, grf::causal_forest, DoubleML, mediation), (6) robustness battery (modelsummary stack, clubSandwich CRSE, fwildclusterboot, ri2, robomit Oster, bacondecomp, HonestDiD), (7) further analysis (interactions + marginaleffects, mediation::mediate, gsem via lavaan, dose-response splines, grf CATE), (8) publication-ready tables & figures (modelsummary, kableExtra, gt, stargazer, texreg, flextable to LaTeX/Word/HTML; ggplot2 + ggpubr + cowplot + binsreg + iplot for figures). **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 `WeightIt` / `gfoRmula` / `tmle` / `ltmle`, Mendelian randomization via `MendelianRandomization` / `TwoSampleMR` / `MRPRESSO`, KM / Cox / AFT / RMST survival via `survival` / `survminer` / `flexsurv`, E-value sensitivity via `EValue`, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `DoubleML`, S/T/X/R/DR meta-learners via `causalweight` / `grf`, causal forest via `grf::causal_forest`, BART/BCF via `bartCause` / `bcf`, matrix completion via `MCPanel`, CATE distribution + policy tree via `policytree`, off-policy evaluation, conformal causal via `conformalInference` / `cfcausal`, fairness audit via `fairmodels`, DAG learning via `pcalg` / `bnlearn` / LLM-assisted). Use when the user asks for a complete R empirical analysis, wants a tidyverse-style reproducible R script / Quarto workflow, prefers fixest over reghdfe, needs the R counterpart to StatsPAI / 00.1 / 00.2, or names a specific R step in isolation ("feols with cluster", "MatchIt nearest neighbor", "bacondecomp in R", "gtsummary table 1", "modelsummary to Word"). Mode A triggers on "target trial emulation R", "tmle ltmle", "MendelianRandomization", "TwoSampleMR", "MRPRESSO", "survival cox AFT", "STROBE R", "EValue R", "公共健康 R", "流行病学 R". Mode B triggers on "DoubleML R", "grf causal forest", "policytree", "bartCause bcf", "conformal causal R", "fairmodels", "pcalg NOTEARS", "因果机器学习 R".

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Apply Marin house style when producing or revising commit, pull-request, issue, comment, documentation, report, or blog prose.

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Generate comprehensive market research reports and industry/competitive analysis in the style of top consulting firms. Use for market sizing, competitive landscape, market entry, investment thesis, strategic recommendations, and consulting-style business reports. Do not use for scientific reports, paper writing, LaTeX submission/PDF builds, EDGAR/FRED/Treasury/Data Commons data retrieval, or biomedical literature evidence work.

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Design and build native-feeling macOS application UIs. Use this skill whenever the user asks to create a desktop app, macOS app, Mac-style interface, Apple-style UI, system utility, or anything that should look and feel like a native Mac application. Also trigger when users mention "native feel", "desktop app design", "Apple design patterns", "sidebar layout", "traffic lights", or want to build tools/utilities that feel like they belong on macOS. This skill covers layout, composition, interaction patterns, animations, light/dark mode, and all the subtle details that make an app feel like Apple built it.

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Turn a photo into a specific AI look (new hairstyle, collectible figurine, royal pet portrait, wedding photo, 90s yearbook, sticker, HD restore) or a photo into a dance video, using Kavel (www.kavel.ai). Use when the user wants to restyle/transform a photo, asks "how do I make an AI X from my photo", or wants a photorealistic image edit that keeps the same face. Gives the right tool, a model-tuned prompt, and the generate link.

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文案风格迁移：把一段文案从一种风格改写成另一种风格（严肃→搞笑、书面→口语、文艺→直白、正式→社交媒体感）， 支持风格参考（给一段目标风格的示例文本）。 使用时机：用户说"改成搞笑风格"、"换个风格"、"风格迁移"、"改成口语化"、"改成文艺风"、 "像 XX 那样写"、"风格改写"、"换个腔调"。 和 text-polisher 的区别：polisher 提升质量不改风格，style-transfer 改变风格不做质量评估。

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【META SKILL】分析当前剪辑逻辑与风格，总结并生成一个新的可复用 Skill 文件，存入剪辑技能库。Analyze the current editing logic and style, summarize and generate a new reusable Skill file, and store it in the editing skill library.

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【CAPABILITY SKILL】基于用户提供的参考文案样本，对视频素材内容进行深度文风仿写，生成风格化脚本。Based on user-provided reference text samples, the video material is deeply rewritten in terms of writing style to generate a stylized script.

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Golang refactoring — safe, at-scale restructuring of existing Go code: a coverage-adaptive safety net, behavior-preserving transforms (gopls Rename/Extract, `gofmt -r`, `gopatch`), the Fowler catalog mapped to Go, breaking import cycles, and small stacked PRs. Apply when a function or type has grown too large, a code smell blocks a feature, or the user asks to refactor Go code — also for renaming at scale, extracting functions or interfaces, moving code between packages, or planning a multi-step refactor. Target styles owned elsewhere → See `samber/cc-skills-golang@golang-naming` (renames), `samber/cc-skills-golang@golang-project-layout` (splits), `samber/cc-skills-golang@golang-modernize` (idioms), `samber/cc-skills-golang@golang-code-style` (control flow), `samber/cc-skills-golang@golang-design-patterns` (patterns/DI).

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[omh] Dictated voice note about project work or status: terse voice and mobile-style requests - turn short spoken-style asks into clarify, plan, status, handoff, or confirmation actions. Use when the user says: voice-operator, voice operator, voice-first, voice command, mobile command, short command, dictated command, dictated request.

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Create Ian Xiaohei-style HTML/SVG motion illustrations for Chinese articles, scripts, storyboard scenes, workflow explanations, concept metaphors, and short video visual aids. Use when the user asks for 小黑 SVG、漫画感 HTML、分层 SVG 动效、把小黑生图做成 HTML/SVG、可编辑矢量动效、口播流程动画、手绘漫画动效、正文配图动画, or wants a Xiaohei illustration style adapted into controllable HTML/SVG/GSAP output instead of raster image generation.

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