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概要と使いどころ
Evaluate, compare, and manage vendor relationships. Trigger with "evaluate this vendor", "compare vendors", "vendor review", "should we renew", "RFP", or when the user is making procurement or vendor decisions.
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。
Conduct comprehensive market research including competitive analysis, customer persona development, market sizing (TAM/SAM/SOM), survey design, and trend analysis. Synthesize insights for strategic decision-making.
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。
Identify, assess, and mitigate operational risks. Trigger with "what are the risks", "risk assessment", "risk register", "what could go wrong", or when the user is evaluating risks associated with a project, vendor, process, or decision.
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。
ACTIVATE when building executive dashboards, tracking business metrics, creating executive scorecards, monitoring organizational KPIs, setting up health metrics, building dashboards for real-time monitoring, or establishing systems to track and measure business performance. Critical for any executive who needs to monitor company health, make data-driven decisions, or track progress toward strategic goals.
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。
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
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。
Generate a concise 4-5 page equity research earnings preview for a single company. Analyzes the most recent earnings transcript, competitor landscape, valuation, and recent news to produce a professional HTML report.
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。
Production-grade TypeScript reference for React & Next.js frontend development. Covers type narrowing, component Props, generic hooks, discriminated unions, as const, satisfies, Zod validation, TanStack Query, server/client boundaries, forms, state management, performance, accessibility, debugging, and code review. Use when the user works with TypeScript in React or Next.js: type errors, Props design, generics, API typing, SSR/CSR boundaries, hydration issues, form validation, state management, performance, or code review. Also use for "how should I type this?", "why does this type error happen?", or any architectural decision involving TypeScript in a frontend context.
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。
Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.
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。
Architecture-first UI/UX design skill for Codex that helps agents reason from product truth, user journeys, causal design decisions, art direction, interaction, responsive behavior, imagery, iconography, and motion before implementation, then validate the result through rendered browser critique.
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。
Use this skill whenever the user wants to create or improve a presentation for an academic context — conference papers, seminar talks, thesis defenses, grant briefings, lab meetings, invited lectures, or any presentation where the audience will evaluate reasoning and evidence. Triggers include: 'conference talk', 'seminar slides', 'thesis defense', 'research presentation', 'academic deck', 'academic presentation'. Also triggers when the user asks to 'make slides' in combination with academic content (e.g., 'make slides for my paper on X', 'create a presentation for my dissertation defense', 'build a deck for my grant proposal'). This skill governs CONTENT and STRUCTURE decisions. For the technical work of creating or editing the .pptx file itself, also read the pptx SKILL.md.
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。
Define a set of actionable design principles that guide decision-making and resolve trade-offs.
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。
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
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。
Use for writing, reviewing, debugging, or testing Pandera schemas and runtime validation for Polars DataFrame or LazyFrame pipelines installed with pandera[polars]. Trigger on pandera.polars DataFrameSchema, DataFrameModel, Column, Field, Check, PolarsData, decorators, coercion, strictness, lazy error collection, and validation-depth decisions. Do not use for pandas-backed Pandera, Pydantic object models, Polars transformations without Pandera, static dataframe typing alone, or generic data-quality platforms.
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。
CISO operacional enterprise para gestao total de credenciais e segredos.
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。
Persistent compounding memory for AI agents. 5 default MCP tools: session_start, session_end, remember, recall, check. Full surface (18 tools) available with --full flag. Two-verb model: inhale (session_start) and exhale (session_end). Correction-first memory with decision trail tracking, watch_for warnings, palace rooms with salience scoring, cross-project insight matching, same-day journal merging, ambient recall hooks. Local markdown only. Zero cloud, zero telemetry, Obsidian-compatible. Optional Supabase backend: when configured via `ar setup supabase`, recall() uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword search — same API, semantic understanding. Gracefully degrades to local search if not configured.
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。
Self-hosted semantic memory for AI agents via MCP. Save worklogs, decisions, and notes, then recall them across sessions by meaning, not keyword. Postgres + pgvector with auto-tagging.
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。
Evaluate architecture and module boundaries for volatility-based decomposition using Juval Lowy's framework (from "Righting Software", building on Parnas 1972). Use when reviewing module splits, service boundaries, new abstractions, or any decomposition decision. Trigger on phrases like "where should this boundary be", "how to split this", "module boundaries", "encapsulate change", "volatility", or references to Lowy, Parnas, or "Righting Software". Complements /hickey (interleaved concerns) with a different lens (change encapsulation).
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 an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.
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。
This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.
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。
Creates detailed technical specifications for software projects covering requirements, architecture, APIs, and testing strategies. Use when planning features, documenting system design, or creating architecture decision records.
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。
Consult external AIs (Gemini 2.5 Pro, OpenAI Codex, Claude) for second opinions. Use for debugging failures, architectural decisions, security validation, or need fresh perspective with synthesis.
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。
Automate 7-phase feature development with specialized agents (code-explorer, code-architect, code-reviewer). Use for multi-file features, architectural decisions, or encountering ambiguous requirements, integration patterns, design approach errors.
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 guidance for writing secure, reliable, and performant Claude Code hooks - validates design decisions, enforces best practices, and prevents common pitfalls. Use when creating, reviewing, or debugging Claude Code hooks.
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。
Use when needing strategic project analysis from multiple independent expert perspectives. Triggers on business decisions, growth strategy, product direction, competitive analysis, or any situation where diverse C-level opinions reduce blind spots
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。