198 件 ・ 関連度順
概要と使いどころ
Deep bug analysis to break the fix-forget-repeat cycle. Analyzes root cause category, why fixes failed, prevention mechanisms, and captures knowledge into specs. Use after fixing a bug to prevent the same class of bugs.
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。
CCPA and CPRA California privacy compliance. Use for CCPA/CPRA readiness assessments, personal information data mapping, consumer rights handling, privacy policy review, opt-out mechanisms, and California privacy audits.
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。
Audits legal AI skills, prompts, workflows, MCP/tool instructions, and agent packages for prompt injection, malicious instructions, unsafe scripts, suspicious metadata, credential exposure, exfiltration paths, persistence mechanisms, and supply-chain risk. Use before installing, importing, publishing, promoting, modifying, or trusting third-party or generated AI skills and legal workflows. Do not use as a generic code review unless skill trust, workflow safety, prompt injection, or supply-chain hygiene is in scope.
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。
California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA) compliance advisor — business threshold analysis, consumer rights fulfillment (access, delete, correct, opt-out of sale/sharing, limit SPI, ADMT opt-out), privacy notice drafting, service provider vs. contractor vs. third-party classification, sensitive personal information (SPI) handling, data minimization, opt-out mechanisms (including GPC), cybersecurity audits and risk assessments (live since Jan 1, 2026), ADMT obligations (effective 2026, deadline Jan 1, 2027), CPPA enforcement (Disney $2.75M, PlayOn $1.1M, Ford $375K), penalty exposure, GDPR comparison, and gap assessments for businesses operating in or targeting California residents.
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。
Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory
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 R package BioNAR, developed to step by step analysis of PPI network. The aim is to quantify and rank each protein’s simultaneous impact into multiple complexes based on network topology and clustering. Package also enables estimating of co-occurrence of diseases across the network and specific clusters pointing towards shared/common mechanisms.
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。
React error boundary hierarchy, fallback UI patterns, offline-first fallback, retry mechanisms, and graceful degradation.
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。
Read, write, and edit ChemDraw CDX/CDXML files with RDKit's rdkit.Chem.rdChemDraw plus direct XML editing, always paired with a rendered PNG. Parse molecules and reactions from .cdxml/.cdx, write structures with good 2D depiction, and hand-build or modify the parts RDKit cannot write: reaction arrows, plus signs, schemes/steps, and text/labels. Use for reaction schemes, synthesis routes, mechanisms, retrosynthesis, or SI figures. Critical: RDKit writes structures only — round-tripping a reaction through a Mol silently drops arrows and text; this skill shows the XML layer that preserves them. For pure molecular analysis (descriptors, fingerprints, SMARTS) use rdkit-cheminformatics; for multi-format 3D conversion use openbabel.
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 DDInter drug-drug interactions via REST API (1.7M+ interactions, 2,400+ drugs). Search by drug name/ID for severity (major/moderate/minor), mechanisms, and clinical recommendations. No auth. For FDA labeling use dailymed-database; for pharmacogenomics use clinpgx-database.
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 Open Targets GraphQL API for target-disease associations, evidence, drug links, safety. Search targets by gene, diseases by EFO ID; scores from 20+ sources, drug mechanisms, tractability. For ChEMBL use chembl-database-bioactivity; for trials use clinicaltrials-database-search.
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。
Structured hypothesis formulation: turn observations into testable hypotheses with predictions, propose mechanisms, design experiments. Follows the scientific method. Use scientific-brainstorming for open ideation; hypogenic for automated LLM hypothesis testing on datasets.
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。
Classifies and enforces constraints via soft vows, hard vows, and Nen Court layers. Use when designing or auditing enforcement mechanisms for project rules.
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。
Deeply research technical architecture, source code, mechanisms, SDKs, frameworks, project comparisons, and system-design options across repositories, history, official docs, issues, discussions, and high-quality community signals. Use when the user asks for deep research, source-level understanding, architecture comparison, mechanism satisfaction, or evidence for designing a system. Always map the question and obtain confirmation before beginning substantive research; skip for simple factual lookups or straightforward implementation tasks.
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。
Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration
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。
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.
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 compliance with Thailand's Personal Data Protection Act B.E. 2562 (2019). Covers consent framework, DPO requirements, PDPC enforcement, lawful bases for processing, cross-border transfer mechanisms, and data subject rights under the PDPA. Keywords: Thailand PDPA, PDPC, consent, DPO, cross-border transfers, data subject rights.
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 Article 28(2) sub-processor approval workflow management. Covers prior specific and general authorization mechanisms, change notification procedures, objection windows, flow-down obligation enforcement, and sub-processor chain risk monitoring.
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。
Handles GDPR Article 18 right to restriction of processing requests, covering the four grounds for restriction (accuracy contest, unlawful processing, erasure opposition, legitimate interest pending), technical flagging mechanisms, and lifting procedures. Activate for restriction request, Art. 18, processing freeze 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。
Implement the NIST Privacy Framework COMMUNICATE function covering CM.AW awareness raising and CM.PO communication policies. Provides transparency mechanisms, stakeholder engagement frameworks, privacy notice templates, and communication workflow guidance.
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。
Guide for managing consent for children's personal data under GDPR Article 8 and COPPA. Covers parental consent mechanisms, age verification methods, country-specific age thresholds (ranging from 13 to 16), parental authorization workflows, and age-appropriate design per the UK ICO Children's Code.
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。
Nigeria Data Protection Regulation (NDPR) and Nigeria Data Protection Act (NDPA) 2023 compliance. Covers lawful basis for processing, data subject rights, cross-border transfer mechanisms, Data Protection Compliance Organisation (DPCO) registration, mandatory DPIA filing, and breach notification. Keywords: NDPR, NDPA, Nigeria, NITDA, DPCO, Africa data protection, cross-border 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。
Conducts a Data Protection Impact Assessment for automated decision-making and profiling systems under GDPR Article 35(3)(a), covering algorithmic transparency, meaningful human oversight, contestation mechanisms, and Art. 22 safeguards. Activate for DPIA automated decision, profiling DPIA, algorithmic impact assessment, Art. 35(3)(a), ADM risk assessment 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。
Executes GDPR Article 20 data portability requests, covering machine-readable format requirements (JSON, CSV, XML), direct controller-to-controller transfer mechanisms, and scope limitations to data provided by the subject on consent or contract basis. Activate for portability, data export, Art. 20, data transfer 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。
Deploy differential privacy in production systems including epsilon selection strategies, noise calibration with Laplace and Gaussian mechanisms, privacy budget tracking, composition theorems, and Python implementation patterns. Covers both central and local differential privacy models.
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。