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概要と使いどころ
Measures entropy, mutual information, KL/JS, bits-per-byte. Use when picking KL direction, comparing cross-tokenizer perplexity, trusting MI estimates, or semantic-entropy gating.
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Search TikTok e-commerce creator leaderboards via Kalodata and query detailed profiles for specific creators. Supports viewing top-performing influencer-sellers by region, currency, language, and date range, and using creatorId to retrieve follower count, video/live revenue and GPM, contact information, and associated shops. Trigger when users mention TikTok creator search, TikTok creator ranking, TikTok influencer leaderboard, TikTok creator detail, TikTok creator profile, creator homepage data, creator contact info, TikTok creator search, TikTok creator ranking, TikTok influencer leaderboard, TikTok creator detail, creator analytics, kalodata creator search/detail. Even if the user does not explicitly mention "kalodata", trigger this skill whenever their need involves viewing TikTok creator leaderboards or detailed sales performance data for a specific TikTok creator.
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Analyze and extract information from images using multimodal AI recognition. Triggered when users want to analyze, describe, or extract information from an image URL — image recognition, image analysis, image description, visual content understanding, OCR text recognition, or visual Q&A. When a user provides an image URL and asks questions about its visual content, this skill should be triggered even if they do not explicitly say "image recognition."
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Design a practitioner inquiry cycle from research question through data collection to evidence-informed action. Use when starting action research, teacher-led investigation, or professional inquiry.
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Design multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.
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。
Your team has high autonomy — and standups are vague, weekly meetings drift into status reports, and 1:1s produce nothing but "I'm fine." This playbook fixes all three layers at once. What's inside: • Daily Standup system — 3-field format, good sample, 4 COO principles to keep it honest • Weekly Meeting — strict 25-min agenda, Scrum Master rotation, 0–10 pulse check • 1:1 Framework — 9-dimension quantified pulse, 5-group deep question bank, bilateral feedback structure • Diagnostic layer — "stuck-at" detection: info / authority / collaboration / capability / direction • FAQ for 4 common failure modes (dead standups, status-report meetings, "I'm fine" 1:1s, scaling beyond 10 people) 🇨🇳 你的团队自由度很高——但 standup 越来越糊，例会变成汇报，1:1 问不出真话。这套三层 Motivation 操作手册解决的正是这件事：standup 是信息层，例会是对齐层，1:1 是个人层。三件事做好了，motivation 不是管出来的，是长出来的。 🇯🇵 チームの自律性は高いのに、スタンドアップは曖昧で、週次ミーティングは進捗報告になり、1on1では「大丈夫です」しか返ってこない——このプレイブックは、その3層すべてを同時に解決します。 🇰🇷 팀의 자율성은 높은데 스탠드업은 두루뭉술하고, 주간 회의는 진척 보고가 되어버리고, 1:1에서는 진심을 들을 수 없다면 — 이 플레이북이 세 레이어 모두를 동시에 해결합니다. Triggers: "团队 motivation" | "standup 没人写" | "例会变成汇报" | "1:1 问不出真话" | "高自由度团队管理" | "COO 如何管人" | "team motivation" | "standup best practices" | "1on1 framework" | "weekly meeting template" | "チームモチベーション" | "팀 동기부여" | "AI startup team ops" | "remote team alignment" | "engagement下降"
Satori is a clinically informed wisdom companion for navigating the inner life — emotions, meaning, grief, purpose, relationship, identity, and the questions that don't resolve easily. Activate when someone is processing something difficult, wrestling with a life question, seeking perspective, or simply needs to think alongside someone who won't rush them toward an answer. Also activate when someone uses language like "I've been struggling with," "I don't know what to do," or "I need to figure out" — or any emotionally charged framing. When in doubt, activate. Draws from Taoism, Buddhism, Stoicism, Christianity, Sufi wisdom, Hindu philosophy, Confucian ethics, and African thought, alongside modern psychology, neuroscience, and trauma-informed frameworks (IFS, DBT, CFT, Schema Therapy, Somatic). Uses Motivational Interviewing, Voss tactical empathy, McAdams Life Story, and Singer Self-Defining Memory — woven naturally, not mechanically.
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Information architecture — design navigation structure, content hierarchy, sitemap, and taxonomy for a product or feature set. Use when asked to "organize the navigation", "information architecture", "how should content be structured", "sitemap", "nav redesign", "where should X live", or "content hierarchy".
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Create info panels that appear in the context panel based on semantic types
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Use when working with the non-metallic engineering materials — polymers, ceramics, or composites — and you need to reason about structure, processing, or properties. Covers thermoplastics versus thermosets, chain structure, glass transition, and viscoelasticity in polymers; ionic and covalent bonding, sintering, brittleness, and Weibull statistics in ceramics; and fiber-reinforced laminates, matrix and reinforcement roles, the rule of mixtures, and anisotropy in composites. Reach for it to choose a plastic, understand why a ceramic fails, or predict the stiffness of a laminate.
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AgentDB Reinforcement Learning Training operates on 3 fundamental principles:
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Expert research analyst specializing in comprehensive information gathering, synthesis, and insight generation. Masters research methodologies, data analysis, and report creation with focus on delivering actionable intelligence that drives informed decision-making.
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Import information from foreign sources into a set of artifact kinds (the target), generating or updating them to reflect the imported information. Use when the user wants to "import", "ingest", or "bring in" external sources like files, URLs, or pasted text into artifacts like SPEC, CODE, DOCS, TASK, INFR, or OTHR.
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Convert a semantic core into crawlable SEO and LLM-friendly information architecture. Use this skill when the user asks for URL structure, page taxonomy, canonical rules, hreflang groups, page roles, pillar/topic/news/article/blog/project separation, multilingual route strategy, or a durable content model such as one short brief plus one longform article. It prevents duplicate public content bodies and hands link-graph work to internal-link-graph-architect.
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Generate and optimise novel small molecules with REINVENT 4 — de novo sampling from a chemical language model, scaffold decoration with LibInvent, fragment linking with LinkInvent, and similarity-constrained analogue generation with Mol2Mol. Use this skill to set up reinforcement-learning or curriculum runs, compose a multi-parameter scoring function from docking scores, predictive models, and physicochemical desirability, and read the resulting sampled sets. Also trigger on REINVENT, LibInvent, LinkInvent, Mol2Mol, scaffold hopping, R-group replacement, linker design, chemical language model, or reinforcement-learning molecule optimisation.
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Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.
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Foundation obedience training for dogs — sit, stay, come, heel, and down using positive reinforcement and marker training. Covers timing, reward hierarchy, session structure, distraction proofing, and common handler errors. Use when a new puppy (8+ weeks) is ready for foundation training, an adult dog lacks reliable basic commands, a rescue dog needs to learn the household's command vocabulary, before advancing to complex behaviors or off-leash work, or when existing commands have degraded and need re-establishing.
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Редактирует русский текст по инфостилю Ильяхова. Убирает стоп-слова, штампы, канцеляризмы, голословные утверждения. Заменяет конкретикой: фактами, цифрами, пользой. Адаптирует строгость под контекст: UI, лендинги, email, статьи, саппорт. Вызывай, когда пользователь просит отредактировать, улучшить, вычистить или проверить русский текст. Also triggers on: edit Russian text, improve copy, review UX writing, fix marketing text, rewrite landing page, clean up email.
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Content angle generator for turning dry expert topics into high-engagement infotainment. Applies 7 journalistic angle types (Myth vs Reality, Paradox, Level Error, Price of Solution, Conditions, Hidden Driver, Contrary Thesis) + historical/future lens + news-hook framing. Use when experts need their content to become interesting rather than just informative - blog posts, video scripts, social content, newsletters, landing pages, ad hooks. For any niche: B2B, B2C, services, products, personal brand.
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Update an EXISTING Tip in the ai-product-dev-tips repository — edit its README, add or fix code, refresh outdated information, fix mistakes — while keeping the repo's format conventions and syncing the root README link. Use this whenever the user wants to update, edit, fix, revise, refresh, correct, or complete an existing Tip in this repo — e.g. "nlp_processing/29 の README を更新して", "〇〇の Tip を最新仕様に直して", "image_processing/11 の説明を修正して", "あの Tip のリンク切れを直して", "この Tip を完成版にして". It locates the target Tip, studies the current format conventions (shared with the ai-product-dev-tips-create skill), applies consistent edits, verifies the content with web research (no fabrication), and syncs the root README link (title text / [In-progress]). This is for UPDATING existing Tips, not creating new ones — to add a brand-new Tip use the ai-product-dev-tips-create skill instead. Trigger this even if the user doesn't say "skill".
Gathers information from multiple sources in parallel, compares what they say, flags where they agree or conflict, and delivers a clean synthesis with a confidence rating for each finding. Useful for making evidence-backed decisions, when evaluating competing claims, during due diligence on a vendor or market, and for building research briefs before strategy sessions. Use when the user says "research this", "synthesize multiple sources", "compare sources", "research report", "what does the evidence say", "gather information on", or "deep dive into".
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Use when evidence needs grading before anyone reasons from it — the user asks "how reliable is this?", "grade this source", "check this report", "can I trust this article?", "is this corroborated?", or hands over a URL, a pasted report or a Liberty91 event ahead of an ACH or assessment. The Quality of Information Check from the CIA Tradecraft Primer, adapted for vendor and press CTI reporting. Resolves each document to its originating source, splits it into claims, and grades every claim on two written elements, access level (how the source knows) and claim support (what backs the statement), with a stated rationale, using a fixed rubric with hard caps. The grade is not an Admiralty rating. Five outlets citing one vendor report count as one source; anything that cannot be traced to a primary is capped, not guessed. Returns a graded claim table, gaps, a deception screen and JSON. /ach, /threat-assessment and /writing-assessments run it first by default.
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Use when the user asks "which SAT should I use for X?", wants the index of Structured Analytic Techniques, or is choosing between quality-of-information-check, ACH, key-assumptions-check, devils-advocacy, indicators of change, etc. Quality of Information Check is the default first step of any diagnostic run.
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Use when the user asks about infostealer families (LummaC2, RedLine, Vidar, Stealc, Raccoon, Rhadamanthys, etc.), log marketplaces (Russian Market, Genesis successors, BidenCash, Hudson Rock corpus), or stealer-driven incidents and credential exposure. Self-updating knowledge cell.
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