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Matter scoping across the full pre-execution arc — organise client data into a structured brief, capture the agreed baseline, or reconstruct scope mid-flight. Use when making sense of client information before a proposal, scoping a new matter, running a kickoff, defining scope, mapping stakeholders, or inheriting a matter mid-flight. Trigger on: 'make sense of this', 'structure this for the proposal', 'scope this matter', 'new matter', 'kickoff', 'what are we doing', 'who are the stakeholders', 'what does success look like', 'matter setup', 'intake', 'I've inherited this matter', 'organise this client data'.

日本語の概要は準備中です。原文の説明を表示しています。

Post-hoc verification and trust audit of AI-generated factual and interpretive claims. Classifies claims by type and salience, routes them to domain-appropriate sources, scores trustworthiness on a tiered scale with an Interpolated verdict for plausible-but-unsupported detail, and assesses rhetorical fairness on interpretive claims. Designed for clinical-negligence and healthcare-law practice in England and Wales, with general applicability beyond.

日本語の概要は準備中です。原文の説明を表示しています。

GDPR Legitimate Interest Assessment (LIA) — Guided assessment for Art. 6(1)(f) GDPR using the EDPB three-step test. Based on EDPB Guidelines 1/2024, EDPB Opinion 28/2024 (AI models), the OSS Case Digest on Legitimate Interest (McIntyre 2026), CNIL AI guidance (June 2025), ICO guidance, and key CJEU case law. Use when: (1) user needs to assess whether legitimate interest is appropriate, (2) user asks about Art. 6(1)(f), "Interessenabwägung", "berechtigtes Interesse", "balancing test", or "LIA", (3) user wants to document a legitimate interest assessment, (4) user mentions the three-step test, (5) user evaluates necessity or proportionality under Art. 6(1)(f), (6) user discusses direct marketing, fraud prevention, IT security, employee monitoring, AI training, web scraping, or credit checks in context of legal basis, (7) user handles a right to object (Art. 21), (8) user asks about children's data, profiling, or cross-border data sharing under legitimate interest, (9) user compares legitimate interest against other legal bases. Do NOT use for: consent assessments (Art. 6(1)(a)), contract performance (Art. 6(1)(b)), DPIA workflows, or pure ePrivacy/cookie compliance questions — though the skill does address the ePrivacy/GDPR overlap where relevant.

日本語の概要は準備中です。原文の説明を表示しています。

Complete prep for all three scored components of the California Bar Exam: the five written ESSAYS (13 subjects — Business Associations, Civil Procedure, Community Property, Constitutional Law, Contracts, Criminal Law & Procedure, Evidence, Professional Responsibility, Real Property, Remedies, Torts, Trusts, Wills & Succession), the PERFORMANCE TEST, and the multiple-choice MBE (7 subjects). Trigger whenever someone is studying for the California bar (or the MBE) and says things like: "give me the rule for [issue]", "big issues in [subject]", "drill/quiz me on [topic]", "MBE questions on [subject]", "teach me [doctrine]", "build an attack outline", "grade my essay", "memorizable paragraph for [issue]", "performance test approach", or "make a study plan" — or names any tested essay/MBE subject in a study, memorization, drilling, or practice-question context. Also trigger when a student uploads a practice essay, PT, or question set to be graded or turned into rule blocks. When in doubt during bar prep, trigger.

日本語の概要は準備中です。原文の説明を表示しています。

AI co-counsel for divorce and family-law attorneys — a jurisdiction-portable scaffold spanning the full matter lifecycle. Eight operating modes mirror how a matter actually moves: intake and onboarding, financial disclosure, children and support, property division and QDRO, discovery and document review, drafting, negotiation and mediation prep, and court prep — plus post-judgment modification and enforcement. The methodology is jurisdiction-agnostic: it forces real, verifiable research for every local form, formula, or rule rather than inventing one, handling common-law, community-property, civil-law, and MENA personal-status regimes as variables. Built around one non-negotiable: privilege. It drafts, analyzes, organizes, and pressure-tests. It is not the lawyer.

日本語の概要は準備中です。原文の説明を表示しています。

Expert CSRD (Corporate Sustainability Reporting Directive, EU 2022/2464) compliance advisor. Use this skill whenever a user asks about CSRD, European Sustainability Reporting Standards (ESRS), double materiality assessment, sustainability reporting obligations, ESG disclosure, CSRD scope and thresholds, value chain reporting, XBRL digital tagging, third-party assurance, CSRD gap assessments, CSRD implementation timelines, ESRS E1–E5 environmental standards, ESRS S1–S4 social standards, ESRS G1 governance, CSRD vs GRI/TCFD/SASB alignment, or any EU corporate sustainability reporting question. Trigger even if the user only mentions "ESG reporting Europe", "sustainability disclosure EU", or "non-financial reporting".

日本語の概要は準備中です。原文の説明を表示しています。

When you want to create, adapt, or update an Agent Skill (Claude Code, Codex, Cursor, and other hosts) in one of your repos (listed in ~/.config/makerskills/skillify/repos.yaml; defaults to makerskills). Modes — CREATE (from chat, video, dump, or scratch) turns a workflow into a new skill. ADAPT ports an external skill with keep/adapt/add classification, license check, and attribution. UPDATE improves existing skills from learnings with cross-skill propagation, memory-vs-skill triage, and semver. Checks new skills for host portability. Triggers on "/skillify," "create a skill," "make this a skill," "skill from this chat," "adapt this skill," "port this skill," "fork this skill," "update X skill," "apply this to the relevant skills," "propagate this learning," "improve [skill]," "fix [skill]." Part of the -ify trifecta (skillify / toolify / loopify).

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for programmatic animation using Tween for smooth property transitions, UI effects, camera movements, and juice. Covers easing functions, parallel tweens, chaining, and lifecycle management. Use when implementing UI animations OR procedural movement. Keywords Tween, easing, interpolation, EASE_IN_OUT, TRANS_CUBIC, tween_property, tween_callback.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for stealth games (Splinter Cell, Hitman, Dishonored, Thief) covering AI detection systems, vision cones, sound propagation, alert states, light/shadow mechanics, and systemic design. Use when building stealth-action, tactical infiltration, or immersive sim games requiring enemy awareness systems. Keywords vision cone, detection, alert state, sound propagation, light level, systemic AI, gradual detection.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for inventory systems (Diablo, Resident Evil, Minecraft) covering slot-based containers, stacking logic, weight limits, equipment systems, and drag-drop UI. Use when building RPG inventories, survival item management, or loot systems. Keywords inventory, slot, stack, equipment, crafting, item, Resource, drag-drop.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for digital card games (CCG/Deckbuilders) including card data structures (Resource-based), deck management (draw/discard/reshuffle), turn logic, hand layout (arcing), drag-and-drop UI, effect resolution (Command pattern), and visual polish (godot-tweening, shaders). Use for CCG, deckbuilders, or tactical card games. Trigger keywords: card_game, deck_manager, card_data, hand_layout, drag_drop_cards, effect_resolution, command_pattern, draw_pile, discard_pile.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for programmatic animation using Tween for smooth property transitions, UI effects, camera movements, and juice. Covers easing functions, parallel tweens, chaining, and lifecycle management. Use when implementing UI animations OR procedural movement. Keywords Tween, easing, interpolation, EASE_IN_OUT, TRANS_CUBIC, tween_property, tween_callback.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for digital card games (CCG/Deckbuilders) including card data structures (Resource-based), deck management (draw/discard/reshuffle), turn logic, hand layout (arcing), drag-and-drop UI, effect resolution (Command pattern), and visual polish (godot-tweening, shaders). Use for CCG, deckbuilders, or tactical card games. Trigger keywords: card_game, deck_manager, card_data, hand_layout, drag_drop_cards, effect_resolution, command_pattern, draw_pile, discard_pile.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for inventory systems (Diablo, Resident Evil, Minecraft) covering slot-based containers, stacking logic, weight limits, equipment systems, and drag-drop UI. Use when building RPG inventories, survival item management, or loot systems. Keywords inventory, slot, stack, equipment, crafting, item, Resource, drag-drop.

日本語の概要は準備中です。原文の説明を表示しています。

Expert blueprint for stealth games (Splinter Cell, Hitman, Dishonored, Thief) covering AI detection systems, vision cones, sound propagation, alert states, light/shadow mechanics, and systemic design. Use when building stealth-action, tactical infiltration, or immersive sim games requiring enemy awareness systems. Keywords vision cone, detection, alert state, sound propagation, light level, systemic AI, gradual detection.

日本語の概要は準備中です。原文の説明を表示しています。

SwiftUI's actual mental model — view identity, lifetime, and dependencies (the Demystify canon), state ownership decision rules, Observation's per-property tracking, body-performance discipline, and the main-actor concurrency contract. Use when state resets mysteriously, views re-render too often, animations glitch between branches, choosing @State vs @Bindable vs plain property, or debugging "why did body run."

日本語の概要は準備中です。原文の説明を表示しています。

The discovery surfaces outside the App Store app — your apps.apple.com product page ranking on Google, an owned landing page with a Smart App Banner, and the deal-site ecosystem that auto-amplifies price drops. Use when building a landing page, improving Google visibility for an app, planning a price-drop promotion, or auditing off-store discovery.

日本語の概要は準備中です。原文の説明を表示しています。

An implementation, which takes input data and makes it available for proper batch effect removal by ComBat or Limma. The implementation appropriately handles missing values by dissecting the input matrix into smaller matrices with sufficient data to feed the ComBat or limma algorithm. The adjusted data is returned to the user as a rebuild matrix. The implementation is meant to make as much data available as possible with minimal data loss.

日本語の概要は準備中です。原文の説明を表示しています。

Query a user's TrailSnap photo library through scoped MCP tools to find photos, reconstruct memories, build person timelines, create evidence-backed narratives, or propose an album plan for explicit in-app approval. Use for the user's own TrailSnap library; never claim a proposal was executed or changed the library.

日本語の概要は準備中です。原文の説明を表示しています。

Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).

日本語の概要は準備中です。原文の説明を表示しています。

Build a brand's messaging hierarchy — taglines, value propositions, key messages, and proof points for each audience. Use when the user says "brand messaging", "messaging framework", "value proposition", "tagline", "brand tagline", "key messages", "messaging hierarchy", "what should we say about our brand", "brand copy foundation", "messaging architecture", "elevator pitch", "one-liner", "brand statement", or needs consistent language across all communications. Also use when messaging feels inconsistent across channels or teams.

日本語の概要は準備中です。原文の説明を表示しています。

Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats. Triggers on: "create original data", "proprietary data", "survey design", "benchmark study", "original research", "data-driven content", "create a survey", "industry benchmark", "aggregated data", "unique data", "first-party data", "data moat", "generate research data", "create a study", "original statistics", "data nobody else has", "competitive data advantage".

日本語の概要は準備中です。原文の説明を表示しています。

Monitor existing content for ranking drops and trigger refresh workflows. Triggers on: "check for content decay", "which content needs updating", "content refresh", "ranking drops", "traffic decline", "stale content", "content audit", "what to refresh", "outdated content", "content performance check", "update old articles", "declining rankings", "content maintenance", "refresh priority list", "which articles are losing traffic", "SEO content audit".

日本語の概要は準備中です。原文の説明を表示しています。

Audit and tend to existing travel reservations. Reads a user-provided reservations file, then systematically checks every booking for price drops, better cabin availability, improved routings, schedule changes, nearby airport alternatives, and award repricing opportunities. Invoke proactively on a regular cadence or when the user says "garden", "check my bookings", "audit reservations", "price drop", "better routing", "did my flight change", "tend reservations", "reservation maintenance", or "check my trips".

日本語の概要は準備中です。原文の説明を表示しています。