399 件 ・ 関連度順
概要と使いどころ
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
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。
Rory Sutherland's 'psycho-logic' approach to marketing, product, and business problems — solve the human perception, not just the engineering or economic metric. Use when the obvious 'logical' fix is expensive, slow, or already done by every competitor; when the user asks 'how do we make people want X', 'the product/price/funnel is fine but nobody converts', 'how do we stand out', 'is there a cheaper way to fix this', or mentions behavioral economics, nudges, irrational behavior, perception, or 'it doesn't make sense but it works'. This is the ROUTER skill: it diagnoses the problem in human terms, then dispatches to reframing, costly-signaling, counterintuitive-tests, or experience-design. Inspired by Rory Sutherland's book *Alchemy*. 日本語の相談（「商品はいいのに売れない」「値下げしかない？」「競合と差別化できない」）にも使う。中文咨询（“产品很好却卖不动”“只能降价吗？”“无法差异化”）也适用。For a catalogue of individual biases, use marketing-psychology (separate plugin) instead.
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' 'consumer behavior,' 'anchoring,' 'social proof,' 'scarcity,' 'loss aversion,' 'framing,' or 'nudge.' Use this whenever someone wants to understand or leverage how people think and make decisions in a marketing 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。
When the user wants to apply psychological principles, mental models, or behavioral science to marketing. Also use when the user mentions 'psychology,' 'mental models,' 'cognitive bias,' 'persuasion,' 'behavioral science,' 'why people buy,' 'decision-making,' or 'consumer behavior.' This skill provides 70+ mental models organized for marketing application.
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。
86 peer-reviewed behavioral frameworks applied to every Maxim output. The flagship moat.
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 AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
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。
Detect sandbox evasion techniques in malware samples by analyzing timing checks, VM artifact queries, user interaction detection, and sleep inflation patterns from Cuckoo/AnyRun behavioral reports
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。
Hunt shadow / zombie / undocumented API surface (OWASP API9 Improper Inventory Management) — enumerate the full API version history (v1/v2/beta/legacy paths, header- and subdomain-based versioning), pull and diff every reachable OpenAPI/Swagger spec (including ones only findable via the Wayback Machine), and behaviorally diff old vs. current versions for auth/rate-limit/validation regressions rather than just response-shape differences. Distinct from hunt-api-misconfig, which owns exploitation once you have a spec or endpoint (mass assignment, JWT, OData, Swagger-chain attacks); distinct from hunt-subdomain, which owns host-level discovery. This skill owns the version-inventory and behavioral-diff workflow itself. Use when the target has versioned API paths, multiple specs, a changelog referencing deprecated endpoints, or a mobile app whose hardcoded backend calls look older than the current web app's.
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エージェントがスキルやルールを実際に守るか、指示の強さが異なる３段階のシナリオで検証し、遵守率とツール呼び出しの時系列をレポートにまとめます。
OpenClawのエージェントに、背景や性格、行動の底線、名前を設定します。好みやランダム抽選から、設定ファイルと头像用の画像生成プロンプトをまとめるスキル。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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。
Migrate prompts and code from Claude Sonnet 4.0, Sonnet 4.5, or Opus 4.1 to Opus 4.5. Use when the user wants to update their codebase, prompts, or API calls to use Opus 4.5. Handles model string updates and prompt adjustments for known Opus 4.5 behavioral differences. Does NOT migrate Haiku 4.5.
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。
Apply psychological principles and behavioral science to copy and design. Useful for tightening hooks, framing, and pricing presentation.
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。
Analyzes meeting transcripts and recordings to uncover behavioral patterns, communication insights, and actionable feedback. Identifies when you avoid conflict, use filler words, dominate conversations, or miss opportunities to listen. Perfect for professionals seeking to improve their communication and leadership 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。
Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.
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。
Organizes, queries, validates, and converts Brain Imaging Data Structure (BIDS) datasets. Supports organizing neuroscience and biomedical data (MRI, EEG, MEG, iEEG, PET, microscopy, NIRS, motion capture, EMG, MR spectroscopy, behavioral), querying BIDS layouts, validating compliance, converting DICOM to BIDS, writing metadata sidecars, or creating BIDS derivatives.
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。
Mandatory skills, conventions, and behavioral rules for Avalonia UI development using the Zafiro toolkit.
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。
Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.
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 analyzing suspected malware through static, dynamic, and behavioral techniques, including IOC extraction, YARA or Sigma rules, sandboxing, and anti-analysis behavior.
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 working on complex multi-step tasks, when a session is getting long (40+ tool calls), when the agent starts ignoring rules it followed earlier, when conventions drift, when output quality seems to degrade, or after any context compaction event. Prevents long-session corruption AND context compaction amnesia through behavioral self-enforcement.
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 bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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。
Detects and analyzes malicious behavior in mobile applications through behavioral analysis, permission abuse detection, network traffic monitoring, and dynamic instrumentation. Use when analyzing suspicious mobile applications for data exfiltration, command-and-control communication, credential stealing, SMS interception, or other malware indicators. Activates for requests involving mobile malware analysis, app behavior monitoring, trojan detection, or suspicious app investigation.
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。
Detects and analyzes process injection techniques used by malware including classic DLL injection, process hollowing, APC injection, thread hijacking, and reflective loading. Uses memory forensics, API monitoring, and behavioral analysis to identify injection artifacts. Activates for requests involving process injection detection, code injection analysis, hollowed process investigation, or in-memory threat 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。
Detects anomalous authentication patterns using UEBA analytics, statistical baselines, and machine learning models to identify impossible travel, credential stuffing, brute force, password spraying, and compromised account behaviors across authentication logs. Activates for requests involving authentication anomaly detection, login behavior analysis, UEBA implementation, or suspicious sign-in investigation.
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。