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A soft light rotates around a pill/button border — a gentle animated outline for a single CTA. One per view; keep it slow and low-contrast. Pure CSS; static border under prefers-reduced-motion.

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Give one card or CTA a lively accent: a small bright comet of light travels around its border. Brighter/livelier than shine-border — use for a single "new"/"featured" element, one per view. Pure CSS; fully static under prefers-reduced-motion.

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A slider with a springy fill and knob — playful, tactile input. Use for a single prominent control (volume, brightness). Under prefers-reduced-motion it moves without the spring.

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Stagger a headline's words (or letters) in on load, one after another. Use for a single hero headline or a title card in a launch video. Honors prefers-reduced-motion. One per screen; never use on body copy or must-read content.

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Act as experienced opposing counsel to attack, undermine, and expose weaknesses in a legal argument, submission, witness statement, or structured reasoning. Produces a six-part adversarial analysis: 1. A core theory of attack identifying the single most effective way to defeat the argument; 2. A reconstructed version of the opposing argument stripped of rhetoric to expose its fragility; 3. Primary lines of attack grouped by category (legal misstatement, evidential gaps, causation failures, internal inconsistency, over-reliance on assertion, procedural weakness); 4. An "if I were the judge" section showing how a sceptical tribunal would dismantle the argument; 5. Surgical strikes - 3 to 5 high-impact points ready for oral submissions; and 6. An analysis of what the argument is trying to hide. Written in formal, adversarial British English for a legally trained audience.

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AI-assisted generation of MCQs (multiple-choice questionnaires) with export to Moodle (GIFT, XML), Wooclap (Excel), Kahoot!, and Word. Supports light MCQs (gamified) and in-depth MCQs (evaluative), with single-answer and multiple-answer formats.

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Restructure any rough build, research, or legal-drafting request into an IRAC-shaped prompt — Issue, Rule, Analysis, Conclusion — optimized for a frontier model. It's the bar-exam framework, repurposed as prompt engineering. The skill leads with the issue and ends with the conclusion (where models weight attention most), forces you to name your constraints and non-goals, and specifies what "good" looks like before a single token is generated. Use it before any non-trivial build, or whenever a vague ask deserves a precise brief.

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Builds a high-fidelity interactive legal assessment as a single self-contained HTML artifact. Output includes a live countdown timer, contract review tasks with hover-annotated problem clauses, candidate answer textareas, model answers hidden behind reveal blocks, scenario-based legal memo tasks, strategy and function-building questions, and a pre-submission checklist that encodes the marking criteria. Use when the user needs to (1) assess a legal candidate with a realistic timed exercise, (2) train or onboard junior lawyers using problem sets rather than doctrine, (3) help a candidate prepare for a real take-home assessment they are facing, (4) build educational materials for law students, in-house teams, or compliance training, or (5) produce scenario-based training modules on specific legal topics. Triggers on "legal test", "take-home", "mock exam", "contract redline exercise", "candidate assessment", "legal training exercise", "practice test", or similar phrasing even when informal.

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Research a US employment law topic across federal, state, and city jurisdictions and produce structured research notes with proper source attribution. Use this skill any time the user asks about US employment laws, regulations, or pending legislation - from a single jurisdiction question to a 50-state survey. Triggers include phrases like "research [employment law topic]", "what are the laws on [employment topic]", "state-by-state [employment topic]", "help me understand [topic] across the US", or any prompt that asks for a legal landscape overview before another deliverable. Always run this BEFORE the employment-law-dashboard skill if a dashboard is the eventual output. Output is a structured research note that distinguishes primary sources (statutes, regs, agency guidance) from secondary sources (law firm alerts, tracker orgs).

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Analyze any OFAC or OFSI enforcement action — by URL, pasted text, or uploaded document — and produce a structured root cause analysis as a formatted Excel (.xlsx) spreadsheet. Use this skill whenever a user names, links to, pastes, or uploads an OFAC or OFSI enforcement action and asks for any of the following: root cause analysis, compliance gaps, what went wrong, lessons learned, organizational self-assessment, or remediation planning. Also trigger when a user asks "analyze this enforcement action", "what were the root causes", "turn this into a checklist", or "how do I make sure this doesn't happen to us". Outputs a single-sheet .xlsx table with six columns: Root Cause | What Went Wrong | How It Went Wrong | What Could Have Stopped It | Is my organization immune to this? (Yes/No/Partial) | Notes.

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Analyzes how multiple regulations interact for a specific product, service, or business model. Identifies where obligations overlap, reinforce, complement, duplicate, or conflict; builds a priority matrix; produces an integrated compliance timeline; and estimates the total compliance burden. Use when (1) scoping a new product or service against the full regulatory landscape before launch, (2) conducting M&A due diligence on a target's multi-regulation exposure, (3) building a strategic compliance roadmap where single-regulation analyses miss the interactions, (4) advising on complex situations where regulations touch the same conduct from different angles, or (5) estimating budget and resourcing for multi-regulation compliance. Primary coverage of EU digital regulation (GDPR, Data Act, AI Act, CRA, NIS2, DORA, DMA, DSA, ePrivacy) and national implementations; the framework extends to any jurisdiction where overlapping regulatory regimes apply to the same activity.

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Gary Vaynerchuk's jab-jab-jab-right-hook framework applied to a personal portfolio rotation on X and LinkedIn. Jabs = build-in-public + educational (value). Hooks = promo (the ask). Each property in the user's configured portfolio (see `~/.config/makerskills/jab-hook/properties.yaml`) gets a hook at least once every ~3 weeks; jabs fill the rest. Drafts go into the user's Typefully workspace via MCP. Modes — plan (7-day plan), pick-next (single post), audit (coverage report), draft (specific post). Triggers on "/jab-hook," "what should I post," "plan my socials," "next promo," "next jab," "next hook," "social rotation," "promote [property]," "BIP post," "audit my socials," "what haven't I posted about."

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When you want to manage projects across your businesses using a kanban + Eisenhower methodology. One kanban per business (whatever portfolio of businesses, projects, or initiatives you run). Tool-agnostic — connects via API/MCP to whatever PM tool each business uses (Notion, GitHub Projects, Plane, Linear, Obsidian file-based, or manual mode). Async-first output. Six modes — setup (scaffold a new board for a business), triage (Eisenhower-sort the backlog), next (pick the next thing to work on, single board or across all), status (paste-ready async snapshot), unblock (diagnose Review/Blocked column), weekly (Friday pulse + week planning). Triggers on "/pm," "/pm setup," "/pm triage," "/pm next," "/pm status," "/pm unblock," "/pm weekly," "what should I work on next," "kanban status," "Eisenhower this," "triage my backlog," "what's blocked."

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Generates multiple means of engagement, representation, and action/expression for a given learning goal. Produces specific, practical alternatives — not generic options — and recommends the highest-impact single change.

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Clean up translation-ese and AI-writing tics in Korean prose so it reads like a person wrote it — content (facts, numbers, proper nouns, quotes) stays 100% unchanged, only style and rhythm are touched. Single-pass, done in this session. Triggers — AI 티 없애줘, 번역투 고쳐줘, 한글 자연스럽게 윤문해줘, sounds like a machine translation, remove AI tells from this Korean text, make this Korean read naturally. Not for: marketing copy generation (write it clean the first time — see rules/korean-writing-quality.md), English text, plain spell-check (see korean-spell-check), or book-length manuscripts.

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Best-practices reference for preparing and instrumenting applications on Temps. Covers the app runtime contract (`.temps.yaml` health, HOST/PORT, readiness, SIGTERM, replicas, migrations) and production observability (errors, traces, metrics, logs, analytics, privacy, sampling, cardinality, credential boundaries). Use whenever building or reviewing an app for Temps, configuring health checks, adding telemetry, diagnosing missing/noisy signals, or checking OTLP/Sentry/analytics ingestion. Triggers include "temps best practices", "prepare this app for Temps", ".temps.yaml health", "temps health check", "ignore health checks in otel", "temps observability", "wire up telemetry", and "instrument this app for temps". Prefer focused setup skills for a single SDK. Use temps-cli for executing deploy and resource-management commands.

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Implement a playbook from its PROMPT specification files. Supports both single-framework (PROMPT-REACT.md, etc.) and 3-framework (PROMPT-3-FRAMEWORKS.md) modes.

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Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI systems.

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Comprehensive SEO analysis for any website, page, business, or topic. Full site audits, SEO checks, single-page analysis, technical SEO (crawlability, indexability, Core Web Vitals with INP), schema markup, content quality (E-E-A-T), image optimization, sitemap analysis, GEO for AI Overviews/ChatGPT/Perplexity, and SEO best-practice planning. Industry detection for SaaS, e-commerce, local, publishers, agencies. Triggers on: SEO, audit, full SEO check, SEO best practices, optimize my site, ranking, organic search, schema, Core Web Vitals, sitemap, E-E-A-T, AI Overviews, GEO, technical SEO, content quality, page speed, structured data.

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Revenue beyond the single-app price tag — own-app bundles, Family Sharing as a conversion lever, cross-developer bundles & suites, and institutional licensing via Group Purchases / Apple School & Business Manager. Use when a developer has multiple apps, a subscription worth sharing, complementary indie partners, or school/clinic/business buyers.

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Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progre

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A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization.

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The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly fou

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Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and

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