「ak sk」の検索結果

Generates code that transforms datasets between ML schemas for model training or evaluation. Use when the user says "transform", "convert", "reformat", "change the format", or when a dataset's schema needs to change to match the target format — always use this skill for format changes rather than writing inline transformation code. Supports OpenAI chat, SageMaker SFT/DPO/RLVR/RLAIF, HuggingFace preference, Bedrock Nova, VERL, and custom JSONL formats from local files or S3.

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Selects a base model for the user's use case by querying SageMaker Hub. Use when the user asks which model to use, wants to select or change their base model, mentions a model name or family (e.g., "Llama", "Mistral", "Nova"), or wants to evaluate a base model — always activate even for known model names because the exact Hub model ID must be resolved. Queries available models, presents benchmarks and licenses, and confirms selection.

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Analyze a software codebase for algorithmic complexity and performance hotspots, then propose or implement safe optimizations without breaking behavior. Use when Codex is asked to scan many files, find inefficient loops, nested iteration, repeated scans, costly rendering/recomputation, N+1 queries, avoidable O(n^2) or O(n) operations, or reduce complexity such as O(n^2) to O(n log n) / O(n), while preserving tests, APIs, outputs, and maintainability.

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Maps every competitor and the gap they leave. Finds the direct competitors, indirect ones and the substitutes a buyer would use instead, from public sources only, and records what each sells, at what price, how they position themselves and what their customers complain about in public reviews, every fact with a link. Ends with a positioning map and the gap the new business can own. Use when the user says "who are my competitors", "is this market crowded", "what else is out there", "competitor analysis", "find the gap", or "who am I up against".

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Your launch lead. Builds a dated, step-by-step launch plan: a cheap real-world test first (a waitlist, a pre-sale or a pop-up with a real price, so real buyers confirm what the simulated panel said), then the countdown to opening day with owners and deadlines, the launch-day run sheet, and the first 30 days of what to measure and what to change. Use when the user says "how do I launch", "launch plan", "when can I open", "launch checklist", "go live", or "soft open".

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Your brand designer. Names the business (with the trademark, domain and handle checks to run before falling in love with a name), writes the voice, a one-line promise and a tagline, and briefs the look: colours, type, logo and the first things a customer sees, so they match the positioning the marketing director chose. Use when the user says "name my business", "brand this", "logo", "brand identity", "what should it look like", "tagline", or "brand voice".

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The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".

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Finds the price buyers will actually pay. Reads the consumer panel's four price answers per buyer into a Van Westendorp price-sensitivity range (too cheap, a bargain, getting expensive, too expensive), sets it against what competitors really charge and against the CFO's margin, and recommends a price, a price ladder (good / better / best) and an opening offer to test. Use when the user says "how much should I charge", "price this", "is $X too expensive", "pricing strategy", "what price", or after the consumer panel.

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Your marketing director. Turns the buyer panel and the competitor map into positioning, the channels the target customer actually uses, a 30-day launch campaign with dated posts and ads, the first ten hooks, a budget split and the numbers to watch. Every message answers an objection real or simulated buyers raised. Use when the user says "how do I market this", "marketing plan", "launch campaign", "how do I get customers", "where do I advertise", "write my ads", or "positioning".

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Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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。

Benchmark LLM reference recommendation capabilities by verifying every cited paper against Crossref, PubMed, arXiv, and DBLP. Measures hallucination rate, per-field accuracy (title/author/year/DOI), discipline breakdown, and year constraint compliance. Supports tool-augmented (ReAct + web search) mode. Use when the user asks to evaluate, benchmark, or compare models on academic reference hallucination, literature recommendation quality, or citation accuracy.

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 a restorative practice protocol for addressing harm, rebuilding trust, and restoring relationships. Use when managing conflict, behavioural incidents, or relationship breakdown between students.

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 a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.

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Track performance across sessions and reduce scaffolding as competence grows. Makes fading visible — the learner knows when scaffolds are removed and why. Use for sustained learning engagement where independence is the goal.

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 an error analysis protocol to diagnose the root cause of student mistakes and misconceptions. Use when error patterns appear in student work and targeted feedback is needed.

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。

Structure a decision or design challenge through multiple perspectives before committing to action. Use as a synthesis step after scoping, mapping, and dilemma navigation when a group needs a wiser next step.

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Redesign a lesson to centre students' cultural backgrounds, community knowledge, and lived experience. Use when making curriculum relevant and inclusive for diverse student populations.

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。

Generate retrieval practice questions at varied difficulty levels for a topic or concept. Use when creating quiz starters, revision activities, or low-stakes testing materials.

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 a pedagogical documentation protocol for making children's learning processes visible and shareable. Use when documenting learning, communicating with families, or planning next steps.

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Map curriculum connections across multiple subjects for a real-world problem or authentic context. Use when planning cross-curricular projects or connecting content to real 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。

Takes a curriculum framework and a statutory or accreditation requirement list; produces a coverage table, gap summary, and CSV showing which framework content covers each requirement and where gaps exist.

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Drafts a customized Customer Agreement starting from the Y Combinator standard form SaaS template. Tailors the agreement through structured intake questions covering fee structure, data handling, ML rights, implementation services, and more. Applies 18 always-on defaults that transform the raw YC form into a professional starting point (renamed to "Customer Agreement", data privacy section added, warranty restructured, consolidated SLA/support exhibit, etc.). Produces a clean .docx and a lawyer-facing memo explaining every change from the YC standard. Use when user says "draft a SaaS agreement", "YC SaaS", "startup SaaS contract", "customer agreement", "SaaS subscription agreement", or "I need a SaaS agreement starting from the YC form". Also trigger when the user is a startup founder discussing SaaS contracting, even if they don't mention YC specifically.

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Screen a client, counterparty, payer or corporate against the sanctions lists published by the designating authorities themselves — the UK Sanctions List (FCDO), OFSI, the UN Security Council, the EU consolidated list and OFAC's SDN and non-SDN lists. Use when onboarding, before taking a payment, before completing a transaction, or whenever a name must be checked against sanctions. Produces candidates and provenance, never a clearance: the record names every list searched with its publication date and count, states what was NOT searched, and ends at a blank decision block. Fails closed — a cache that is missing, damaged, truncated or stale is reported NOT SEARCHED and the process exits non-zero, so nothing can read an incomplete run as clean. Matches across transliteration, diacritics, homoglyphs and initials, because nobody spells a name the way the publisher does. Standard library only; screening opens no network connection.

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Generates standalone interactive HTML "deal cards" that translate complex regulations into negotiation-ready reference tools, systematically distinguishing mandatory obligations from negotiable implementation choices. Use when the user needs an interactive regulatory guide for (1) contract negotiation support, (2) client education or internal training, (3) regulatory briefings for commercial stakeholders, or (4) structured comparison between required and flexible compliance paths. Primary focus on EU digital regulation (Data Act, AI Act, CRA, DORA, NIS2, GDPR) but the structural pattern transfers to any regulation where separating hard obligations from implementation choice is the point. Supports bilingual output where the jurisdiction calls for it.

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。