51 skills · sorted by relevance
This skill should be used when the user asks about "bun build", "Bun.build", "bundling with Bun", "code splitting", "tree shaking", "minification", "sourcemaps", "bundle optimization", "esbuild alternative", "building for production", "bundling TypeScript", "bundling for browser", "bundling for Node", or JavaScript/TypeScript bundling with Bun.
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"Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say 'should we bundle these products', 'bundle pricing', or 'package deal pricing'.".
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Helps build JavaScript and TypeScript projects with Bun, covering setup, package management, testing, and bundling code for distribution. It also guides migration from Node.js and use of Bun’s built-in APIs.
Configure Vite with vite-plugin-singlefile for mandatory single-file HTML bundling of MCP Apps. All assets (JS, CSS, images, fonts) must be inlined into a single HTML file for sandboxed iframe compatibility.
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Product bundling strategy — virtual bundles, multi-pack pricing, cross-sell bundles, bundle listing optimization
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Bun — fast all-in-one JavaScript runtime, bundler, test runner, and package manager. Use when speeding up Node.js projects, using Bun as a drop-in Node replacement, bundling with Bun, or running tests faster. Covers runtime APIs, package management, bundling, and Bun-specific features like Bun.serve, Bun.file, and Bun.sqlite.
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Configure and troubleshoot Vite projects, covering environment variables, API proxies, plugins, and production builds. The skill also guides library packaging, dependency compatibility, and development server performance, with checks for exposed secrets and missing TypeScript validation.
Guide development and debugging in Next.js 16 and later using Turbopack, a tool that bundles application code. Review slow startup, live updates, caching, webpack alternatives, bundle analysis, and version-specific proxy file naming.
Build complex interactive artifacts for claude.ai with React, Tailwind CSS, and shadcn/ui. Start from a configured project, develop interfaces with state management or navigation, and bundle the result into one shareable HTML file.
Organize a GitHub issue backlog by shared root cause, with one main tracking issue and design document per group. Route new reports into existing plans and prepare pull requests that address related bugs together with regression checks.
Add a text-to-speech engine to Voicebox through dependency research, backend implementation, interface updates, and packaging. The workflow covers development checks and verification of the packaged application before handing the build over for local testing.
Package a local MCP server, which connects an AI host to tools, as a .mcpb file. Include dependencies and runtime support, define installation settings, and validate, test, and sign the bundle for distribution.
Turn a supplied PowerPoint template into a reusable agent skill. Analyze layouts and content boundaries, bundle the template with self-contained instructions, validate it with a sample presentation, and package the skill for later presentation creation.
Turn recurring procedures and specialized knowledge into reusable agent skills. It guides the design of SKILL.md, scripts, reference documents, and output assets, then validates the structure and packages the files for distribution.
Guides development across the Kilo JetBrains plugin’s shared, frontend, and backend modules. Explains remote calls between modules, UI thread rules, dependency packaging, and testing practices, including serialization failures that require checks in a running IDE.
Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/TRIPOD reporting) — OR in ML causal inference style (DML, S/T/X/R/DR meta-learners, causal forest, Dragonnet/TARNet/CEVAE, BCF, CATE distribution, policy learning, conformal causal, fairness audit, causal discovery) — OR in distributional / gap-decomposition style (Oaxaca–Blinder `sp.oaxaca`, Kitagawa `sp.kitagawa_decompose`, DiNardo–Fortin–Lemieux `sp.dfl_decompose`, Gelbach `sp.gelbach`, Fairlie `sp.fairlie`, RIF / FFL `sp.rif_decomposition`, all reachable through the `sp.decompose` dispatcher). Also covers exporting multi-column regression tables to Word / Excel / LaTeX (Stata outreg2 / esttab / R modelsummary equivalent) and bundling an entire replication appendix into one .docx / .xlsx / .tex file. Triggers on keywords "StatsPAI", "statspai", "AER empirical analysis", "applied micro pipeline", "Table 1 balance", "event study", "first-stage F", "Oster bound", "honest_did", "spec_curve", "callaway_santanna", "dragonnet", "text as treatment", "outreg2 in Python", "regression table to Word/Excel", "sp.regtable", "sp.collect", "sp.paper_tables", "sp.feols", "summary_col", "modelsummary", "AER style table", "QJE style table", "epidemiology pipeline", "target trial emulation", "g-formula", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "TRIPOD", "公共健康", "流行病学", "DML", "double machine learning", "causal forest", "meta-learner", "CATE", "conformal causal", "policy learning", "因果机器学习", "ML causal", "decomposition", "Oaxaca-Blinder", "Kitagawa", "DiNardo-Fortin-Lemieux", "DFL", "Gelbach", "RIF decomposition", "wage gap decomposition", "sp.decompose", "sp.oaxaca".
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Comprehensive verification checklists for MCP Apps. Tests with basic-host reference, validates handler-before-connect, text fallback, resource URI linking, single-file bundling, host styling, CSP, and legacy pattern detection.
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Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
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Desktop JVM and build traps: JNA natives, bundling, memory, packaging, code signing, R8, deep links, Gradle and CI releases. Use when a desktop build fails packaged, signed, on another OS or arch, or in CI.
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Visualize biological networks (PPI, gene-regulatory, co-expression, pathway) with layout algorithm choice (ForceAtlas2, Fruchterman-Reingold, Kamada-Kawai, hive plots), edge bundling, community-based coloring, and reproducible seeds using NetworkX, PyVis, igraph, and Cytoscape automation. Use when rendering biological networks for static publication, interactive HTML exploration, or Cytoscape-format export.
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Pricing strategy for TikTok — competitive pricing, commission impact, bundling, discount psychology
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Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.
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Configure Laravel ecosystem with custom Artisan commands, Vite asset bundling, Pint code styling, and Horizon queue monitoring. Use when creating Artisan commands, migrating from Mix to Vite, or configuring Pint code standards.
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Use when building, running, testing, or bundling JavaScript/TypeScript applications. Reach for Bun when you need to execute scripts, manage dependencies, run tests, or bundle code for production. Bun is a drop-in replacement for Node.js with integrated package manager, test runner, and bundler.
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