「core」の検索結果

Runs a product quality review on a journey, feature, landing page, onboarding flow, or AI/agent surface and returns a friction log, a scored critical-journey review, and a taste bar your team can calibrate against. Use when someone asks is this good enough to ship, how polished should it be, why do users bounce, what is our quality bar, how do we build taste, or to review a design, prototype, onboarding, or checkout. Draws on 264 insights from 100 Lenny's Podcast guests incl. Katie Dill, Dylan Field and Stewart Butterfield.

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。

Rewrite an exec update, memo, strategy doc, deck outline, Slack post or talk so it lands with senior people, and get a graded score plus the top 3 fixes. Use when you say 'rewrite this for my VP', 'exec update', 'how do I present to the CEO', 'my memo is too long', 'prep me for this review', 'all-hands talk' or 'I get nervous presenting'. Draws on 296 insights from 90 Lenny's Podcast guests incl. Wes Kao, Nancy Duarte and Matt Abrahams.

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。

Turns a pile of ideas, requests and goals into a confidence-scored roadmap plus a goals/OKR set the team can actually follow. Use when someone says 'prioritize', 'what should we build next', 'roadmap', 'now next later', 'ICE or RICE', 'write our OKRs', 'quarterly planning', 'too many priorities' or 'sales keeps changing the plan'. Draws on 133 Lenny's Podcast guests including Itamar Gilad, Christina Wodtke and Janna Bastow.

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。

Systematically gather, score, and format client testimonials. Use when someone needs social proof, wants to collect feedback, needs to turn happy clients into public advocates, or asks for help requesting or drafting a testimonial.

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。

Audit and rewrite your LinkedIn profile to attract the right people. Scores each section, rewrites headline and about copy, and includes an AI visibility checklist so you show up in ChatGPT, Perplexity, and Claude search. Use when someone says "optimize my LinkedIn," "LinkedIn profile help," "rewrite my about section," or "how do I show up in AI 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。

SEO analysis and optimization specialist. Use when conducting technical SEO audits, optimizing meta tags, analyzing Core Web Vitals, or improving search engine rankings. Focuses on actionable recommendations.

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。

Comprehensive performance specialist covering analysis, optimization, load testing, and framework-specific performance. Use when identifying bottlenecks, optimizing code, conducting load tests, analyzing Core Web Vitals, fixing memory leaks, or improving application performance across all layers (application, database, frontend). Includes React-specific optimization patterns.

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。

Core planning principles for Portfolio Buddy 2 development. Use when: planning any feature implementation, modification, or refactoring. Ensures code preservation, mobile/desktop optimization, and thorough requirement gathering.

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。

Iterative worker-reviewer cycle that spawns a critic subagent to score work 1-10 and provide actionable feedback, then revises until a quality gate is met. Use when implementing features, writing specs, reviewing existing code, or completing any task where quality matters more than speed. Trigger phrases: "use review-loop", "polish this", "iterate on this", "/review-loop", "review with feedback loop".

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。

Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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。

Decomposed, multi-criteria metric design for LLM pipelines. The metric IS the model — change the metric and the optimizer changes behavior. Decompose by default; bool during compile, float during eval; calibrate against human; mitigate judge bias. Search keywords: LLM-as-judge, llm as judge, eval metric, evaluation score, scoring function, rubric, RAGAS, G-Eval, judge bias, verbosity bias, how to evaluate LLM output.

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。

Neutral, framework-agnostic decision tree for project kickoff: "which agent / RAG / LLM framework should I reach for?" Synthesizes the ecosystem sections of 7 landmark-project SOPs (LangGraph, LlamaIndex, DSPy, CrewAI, vLLM, Aider, Dify) into one layered rubric. Core stance: frameworks are LAYERS, not competitors — a real project usually combines DSPy (compile) + LlamaIndex (retrieve) + LangGraph (orchestrate) + vLLM (serve), and you choose ONE per layer, not one to rule all. Use when starting any LLM/agent/RAG project, or whenever the "which framework?" question is asked. Deliberately neutral — unlike vendor docs and the LangChain-biased `framework-selection` on skill.sh, this skill has no horse in the race.

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。

Review the model establishment, numerical solution, result analysis, model validation, and sensitivity or robustness sections of a mathematical-modeling paper against the complete problem. Use for core-model-section diagnosis, not automatic full-paper scoring or unsupported recomputation.

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。

Review mathematical modeling competition papers from a complete problem and paper, with an optional strict bzd-paper-format-checker audit, itemized problem-specific scoring, a bottom-up low-score safeguard, and CUMCM award calibration by problem type, division, region, school and advisor. Generate a Chinese HTML judge report.

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。

Solana token data, PnL, risk scores, 1-second OHLCV, wallet analytics, and self-hosted swap execution via Raptor

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。

Mean-reversion strategy tools including Hurst exponent, half-life estimation, z-score signals, ADF testing, and Ornstein-Uhlenbeck modeling

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。

Assist with core product management activities including writing PRDs, analyzing features, synthesizing user research, planning roadmaps, and communicating product decisions. Use when you need help with PM documentation, analysis, or planning workflows that integrate with your codebase.

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 managing multiple initiatives across time horizons (now/next/later, H1/H2/H3), balancing risk vs return across portfolio, sizing and sequencing bets with dependencies, setting exit/scale criteria for experiments, allocating resources across innovation types (core/adjacent/transformational), or when user mentions portfolio planning, roadmap horizons, betting framework, initiative prioritization, innovation portfolio, or resource allocation across horizons.

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-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with truth-score verification.

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。

Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.

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。

Retrieval-augmented generation (RAG) skill for the D&D 5e System Reference Document (SRD). Use when answering questions about D&D 5e core rules, spells, combat, equipment, conditions, monsters, and other SRD content. This skill provides agentic search-based access to the SRD split into page-range markdown files.

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。

专业的比特币钱包连接技能，支持btc-connect core、react、vue包在React、Vue、Next.js、Nuxt 3项目中的完整集成，包含UniSat和OKX钱包适配、网络切换功能、SSR环境配置、统一Hook API和v0.5.0最新特性

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。

Expert guide for Backend.AI distributed computing platform. Automatically activates when users ask about: - Backend.AI architecture, components (Manager, Agent, Storage Proxy, Webserver, App Proxy) - Features (session scheduling, Sokovan orchestrator, multi-tenancy, resource allocation) - APIs (REST, GraphQL), authentication, RBAC authorization - Container runtime (kernels, jail sandbox, hook library, virtual folders) - Accelerator support (CUDA, ROCm, TPU, NPU, Graphcore IPU) - Client SDKs (Python, Java, JavaScript, PHP) - Setup, requirements (Python 3.13+, Docker, PostgreSQL, Redis, etcd) - How WebUI connects to/interacts with Backend.AI backend - Plugin interfaces, development setup, infrastructure Use when user mentions "Backend.AI", "backend.ai", "Sokovan", component names, or asks about the backend platform this WebUI connects to. allowed-tools: WebFetch, Read

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。

Battle-tested Playwright patterns for writing and debugging reliable E2E, API, component, visual, accessibility, and security tests. Use when you need locator strategy, assertions, fixtures, network mocking, auth flows, trace debugging, or framework recipes for React, Next.js, Vue, and Angular. TypeScript and JavaScript.

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。