Decompose a session into typed handoff units and recall them later. Use when ending a session or resuming work whose prior state must be recovered.
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。
1.3万 件 ・ 関連度順
概要と使いどころ
Decompose a session into typed handoff units and recall them later. Use when ending a session or resuming work whose prior state must be recovered.
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。
Converts a Claude Code session JSONL file into an animated GIF terminal replay. Use when creating demos or visual evidence from past sessions.
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。
Manages Claude Code sessions with naming, checkpointing, and resume strategies. Use when organizing long-running work or resuming across sessions.
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。
Converts a Claude Code session into a blog post, case study, or Reddit post. Use when publishing dev blog content or community posts from real sessions.
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。
Guides the post-Schrems II Transfer Impact Assessment process following EDPB Recommendations 01/2020 six-step methodology. Covers assessment of third country legal frameworks, supplementary measures evaluation, and TIA scoring. Activate for international data transfers, SCCs, third-country adequacy, or Chapter V compliance. Keywords: TIA, Schrems II, transfer assessment, EDPB, supplementary measures, SCCs, international transfer.
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。
Guides privacy program maturity assessment using the AICPA/CIPT Privacy Maturity Model with five levels: Ad Hoc, Repeating, Defined, Managed, and Optimized. Covers assessment methodology across ten privacy domains, scoring criteria, gap analysis, maturity roadmap generation, and benchmarking against industry peers. Keywords: privacy maturity, AICPA, maturity model, assessment, roadmap, benchmarking.
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。
Conducts retention impact assessments for new processing activities to determine appropriate data retention periods. Covers regulatory requirements scanning, proportionality review, purpose-based retention determination, and retention period documentation aligned with GDPR Article 5(1)(e) and Article 25 data protection by design. Activate for retention assessment, new processing retention, retention period determination queries.
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。
Conducts Privacy Impact Assessment for vendor and third-party data processing arrangements. Covers processor due diligence, Data Processing Agreement (DPA) requirements under GDPR Article 28, sub-processor management, cross-border vendor transfers, cloud service provider assessments, and ongoing vendor monitoring. Keywords: vendor PIA, processor assessment, DPA, Article 28, sub-processor, cloud privacy, third-party risk.
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。
Guides systematic assessment of current state versus GDPR requirements across all chapters with prioritised remediation matrix. Activate when starting compliance programmes or conducting periodic reassessment. Keywords: gap analysis, compliance assessment, remediation matrix, GDPR readiness.
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。
Guides the creation and review of data processing agreements under GDPR Article 28(3), covering all eight mandatory clauses. References the 2021 Standard Contractual Clauses and provides a compliance checklist for processor contracts. Activate when onboarding processors, reviewing DPAs, or auditing processor compliance. Keywords: DPA, data processing agreement, Article 28, processor, mandatory clauses, standard contractual clauses.
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。
Guides the end-to-end GDPR Data Protection Impact Assessment process under Article 35, including mandatory trigger identification per Art. 35(3), DPIA content requirements per Art. 35(7), and EDPB WP248rev.01 methodology. Activate for systematic profiling, large-scale special category processing, or large-scale public monitoring. Keywords: DPIA, Article 35, impact assessment, WP248, data protection, risk assessment.
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。
Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training 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。
Fix silent data loss when mutating Nostr replaceable events (Kind 0 profile, Kind 3 contact/follow list, Kind 10002 relay list, etc.) in client apps. Use when: (1) Following someone wipes the user's entire follow list, (2) Updating profile metadata loses existing fields, (3) Fresh browser session or mobile login causes data loss on first action, (4) Replaceable event mutation uses stale or null cached state. Root cause: Nostr replaceable events are full-replace (no partial update), so publishing based on stale/unloaded cache overwrites the canonical version. Applies to any Nostr client using React, Flutter, or similar reactive frameworks where query state may not be loaded when a mutation fires.
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 structured context summaries for continuing work across AI sessions. Use when ending a session to enable seamless continuation in a new session without losing 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。
Automatically identify potential boundary and exception cases from requirements, specifications, or existing code, and generate comprehensive test cases targeting boundary conditions, edge cases, and uncommon scenarios. Use this skill when analyzing programs, code repositories, functions, or APIs to discover and test corner cases, null handling, overflow conditions, empty inputs, concurrent access patterns, and other exceptional scenarios that are often missed in standard testing.
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。
Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted issues.
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。
Deep behavioral audit of a Lattice skill — proposes 3 review personas relevant to the skill, runs independent scenario analysis from each persona's perspective, then merges only the high-confidence, practical findings into a severity-ordered gap report with proposed fixes. Structural validation (conventions, cross-references) is skill-validate's job — this skill finds gaps that would realistically surface when someone actually uses the skill: missing scenario handling, ambiguous instructions, silent failure cases, and behavioral inconsistencies. Filters out theoretical edge cases, low-likelihood speculation, and findings owned by other skills. Use after writing or significantly changing any skill, or when the user says 'review this skill', 'deep review', 'does this skill work', 'find gaps in this skill', 'stress test this skill', 'review from different angles', or 'skill review'. Standalone — does not call other skills.
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 a learner or instructor asks for a report, summary, reflection, progress note, performance recap, activity metrics, instructor-shareable record of a MATLAB AI tutoring session, aggregate report across multiple MATLAB tutoring session reports, or instructor dashboard artifact with metric drilldowns. Supports optional start and end datetime arguments for multi-session report and dashboard ranges.
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。
Organize, categorize, and summarize business expenses for reimbursement and tax preparation. Use when a user asks to create an expense report, organize receipts, categorize expenses, summarize spending, prepare expenses for reimbursement, or compile business expenses for tax filing. Handles receipts, CSV data, and manual entries.
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。
Add persistent memory to Claude Code that survives across sessions. Use when: maintaining continuity across Claude Code sessions, building agents with persistent project memory, avoiding repeated context setup. Covers claude-mem (AI-compressed session logs) and Claude Subconscious (Letta-based background agent).
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。
Browserbase runs managed headless Chromium sessions in the cloud, so browser automation, AI agents and scraping jobs need no browser infrastructure of their own. Use when someone asks to "run Playwright in the cloud", "Browserbase session", "headless browsers at scale", "scrape with proxies and CAPTCHA solving", "persist a login across browser sessions", or "use Stagehand with Browserbase". Covers sessions, proxies, contexts, parallel runs and replays.
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 in live learning experiences - cohort-based courses, workshops, webinars, coaching sessions, and real-time facilitation. Covers designing for synchronous learning, building peer accountability, and creating transformative live experiences that self-paced courses can't match. Use when "cohort course, live course, workshop, webinar, group coaching, live session, cohort-based, " mentioned.
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。
Interactive codebase orientation for a HUMAN who wants to learn the code. Runs the same tree-sitting + featuring pipeline as exploring-codebases but synthesizes it into guided exercises and an HTML teaching artifact rather than an analysis document. Use for "orient me to this repo", "teach me this codebase", "walk me through this code", "learning orientation", or when someone wants genuine comprehension rather than a task completed. The audience is the test: if nobody is being taught and the goal is to get work done, use exploring-codebases instead.
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。
First-encounter orientation on a repository nobody here has worked in yet. Runs a fixed five-step workflow — venv setup, tarball fetch, tree-sitting structural scan, featuring synthesis, then reasoning over the two — and yields an account of what the repo contains and how it is arranged, optionally written out as _FEATURES.md. Use for "I just cloned this", "what is this repo", "what does this do", "explore this repo", "give me an orientation", "what are the main features", "review what's new in this repo", or before starting work in a codebase you have not seen. This is the divergent what's-here skill. Route elsewhere for: a named symbol, a file's structure or a line range (tree-sitting); all callers of a Python symbol (searching-codebases); teaching a human the codebase through exercises (orienting-codebases); fetching or cloning a repo without analysing it (accessing-github-repos, cloning-project).
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。