「categories」の検索結果

Browse hookify rule catalog. Use when installing pre-built rules or browsing categories. Do not use when writing custom rules; use hookify:writing-rules.

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Analyzes and improves prompts using 31 frameworks across 7 intent categories. Use when a user wants to improve, rewrite, structure, or engineer a prompt — including requests like "help me write a better prompt", "improve this prompt", "what framework should I use", "make this prompt more effective", or any prompt engineering task. Recommends the right framework based on intent (create, transform, reason, critique, recover, clarify, agentic), asks targeted questions, and delivers a structured, high-quality result.

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Refresh the user's competitive and market context. Re-scan named rivals and the category for what changed since the brief was written (rebrands, repositioning, new entrants, pricing shifts, renamed categories) and update docs/gtm-cofounder/founder-brief.md and the roadmap with anything that moves positioning. Use on a cadence, or whenever a competitor rebrands, a new tool appears, or the user senses the market has shifted under them.

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把已验证的传播机制转成适合目标账号的内容选题，并完成生成、迁移、评分和筛选，让选题自然连接人设、产品或门店。用户要从低粉爆款、垂直样本、邻近受众或跨赛道内容中寻找新选题时使用。 English: Turn validated content mechanisms into topics that fit the target account, then generate, adapt, score, and filter them while connecting naturally to the persona, product, or store. Use to find ideas from low-follower breakouts, niche examples, adjacent audiences, or other categories.

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拆解已筛选爆款内容为什么能传播，分析受众情绪、分享动机、有效立场、首批传播者和可迁移元素。用户问一条内容为什么爆、打中了谁，或哪些机制能跨赛道借鉴时使用。 English: Explain why a selected breakout post spreads by analyzing audience emotions, sharing motives, effective positioning, early sharers, and transferable elements. Use when the user asks why content went viral, whom it resonated with, or what can be adapted across categories.

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Audit and rewrite content to remove 21 categories of AI writing patterns with a 43-entry replacement table

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Triage failed Agent QA runs with MCP evidence, artifacts, logs, fixed failure categories, confidence, and actionable next steps.

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Designs and implements data retention schedules compliant with GDPR Article 5(1)(e) storage limitation principle. Maps data categories to retention periods with legal basis justification, regulatory minimum holding periods, and automated review triggers for schedule maintenance. Activate for retention policy, storage limitation, data lifecycle, retention period queries.

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Implement the NIST Privacy Framework IDENTIFY function including ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Provides control mapping, gap analysis templates, and implementation workflows for privacy risk identification.

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Creates GDPR Article 30(2) Records of Processing Activities for data processors with all four mandatory fields: processor and controller names and contact details, categories of processing, third country transfers, and security measures description. Activate for processor RoPA, Art. 30(2), processor records, sub-processor documentation.

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Guides implementation of the NIST Privacy Framework IDENTIFY function covering ID.BE business environment, ID.DA data actions, ID.IM improvement, and ID.RA risk assessment subcategories. Maps NIST PF controls to GDPR requirements for dual-framework compliance. Keywords: NIST Privacy Framework, IDENTIFY function, ID.BE, ID.DA, ID.IM, ID.RA, privacy risk assessment, data actions.

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Guides the periodic DPIA review lifecycle including trigger identification for regulatory changes, new data categories, technology changes, and breach incidents. Covers version control, stakeholder sign-off procedures, and DPIA register management per Art. 35(11). Keywords: DPIA review, PIA update, review cadence, version control, Art. 35(11), periodic review, trigger events, stakeholder sign-off.

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Conduct LINDDUN privacy threat modeling across all seven categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat trees, privacy-specific mitigation strategies, and integration with STRIDE security threat modeling.

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GDPR-compliant Data Processing Agreement drafting per Article 28(3). Covers all 8 mandatory provisions including subject matter, duration, nature and purpose, data types, categories of data subjects, controller and processor obligations, and sub-processor cascade requirements.

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Applying the ePrivacy Directive Article 5(3) strictly necessary exemption to classify cookies that do not require consent. Covers exemption criteria, functionality cookies, load balancing, session state, and non-exempt categories with regulatory guidance from EDPB and national DPAs.

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Implements financial records retention requirements across EU directives (5-7 years), SOX Section 802 (7 years), MiFID II (5-7 years), tax records, payment data, and AML obligations under AMLD. Maps financial data categories to statutory retention periods with cross-jurisdictional reconciliation. Activate for financial retention, SOX records, MiFID retention, AML retention, tax record keeping queries.

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Complete guide to LINDDUN privacy threat modeling methodology covering seven threat categories: Linking, Identifying, Non-repudiation, Detecting, Data Disclosure, Unawareness, and Non-compliance. Includes DFD-based analysis, threat tree catalogs, mitigation mapping to privacy design patterns, and step-by-step process.

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CPRA §1798.121 sensitive personal information restrictions and compliance. Covers all 9 sensitive PI categories including SSN, precise geolocation, racial/ethnic origin, biometric, genetic, health, and sex life data. Right to limit use/disclosure, permitted purposes, and implementation.

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Harmonises data classification across jurisdictions mapping GDPR special categories vs CCPA sensitive PI (1798.140(ae)) vs HIPAA PHI (160.103) vs LGPD sensitive data (Art. 5-II). Provides cross-regulation mapping matrix for multinational compliance. Keywords: cross-jurisdiction, GDPR, CCPA, HIPAA, LGPD, sensitive data, multinational, classification mapping.

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Guides privacy audit evidence collection processes including evidence planning, sampling strategies, documentation standards, chain of custody, interview techniques, system walkthrough procedures, and evidence evaluation. Covers ISO 19011 evidence categories (records, statements of fact, observations) and ISACA audit evidence requirements for privacy compliance assessments. Keywords: audit evidence, evidence collection, sampling, chain of custody, audit documentation, interview techniques.

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Classifies sensitive data in AI/ML training datasets including bias detection for Art. 9 categories, data card documentation, provenance tracking, and consent verification for model training. Keywords: AI training data, ML dataset, bias detection, data card, model training, Art 9, consent, GDPR AI.

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Create professional, consultant-grade PowerPoint presentations from scratch using MckEngine (python-pptx wrapper) with McKinsey-style design. Use when user asks to create slides, pitch decks, business presentations, strategy decks, quarterly reviews, board meeting slides, or any professional PPTX. AI calls eng.cover(), eng.donut(), eng.timeline() etc — 67 high-level methods across 12 categories (structure, data, framework, comparison, narrative, timeline, team, charts, images, advanced viz, dashboards, visual storytelling), consistent typography, zero file-corruption issues, BLOCK_ARC native shapes for circular charts (donut, pie, gauge), production-hardened guard rails for spacing, overflow, legend consistency, title style uniformity, dynamic sizing for variable-count layouts, horizontal item overflow protection, chart rendering, and AI-generated cover images via Tencent Hunyuan 2.0 with professional cutout, cool grey-blue tint, and McKinsey-style Bézier ribbon decoration.

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Audit, design, categorize, distribute, and measure agent skills using lessons from Anthropic's Lessons from building Claude Code: How we use skills. Use when reviewing an existing skill, deciding whether a workflow deserves a skill, planning a skill library, turning team knowledge into skills, choosing skill categories, writing trigger descriptions, designing progressive disclosure, or planning skill marketplace and usage measurement.

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。

Pre-publish assistant for new blog posts. Use when the user wants to classify a new post with categories and tags, generate SEO metadata (title, description, focus keyphrase), or get intelligent suggestions with rationale. Works with draft content (file path, URL, or text) and suggests from existing taxonomy to maintain balanced distribution.

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。