「from scratch」の検索結果

Workflow for creating new applications from scratch. Covers requirements gathering, tech stack selection, scaffolding, implementation, and delivery of a functional prototype.

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Design, optimize, and communicate SaaS pricing — tier structure, value metrics, pricing pages, and price increase strategy. Use when building a pricing model from scratch, redesigning existing pricing, planning a price increase, or improving a pricing page. Trigger keywords: pricing tiers, pricing page, price increase, packaging, value metric, per seat pricing, usage-based pricing, freemium, good-better-best, pricing strategy, monetization, pricing page conversion, Van Westendorp. NOT for broader product strategy — use product-strategist for that. NOT for customer success or renewals — use customer-success-manager for expansion revenue.

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Set up, audit, and debug analytics tracking implementation — GA4, Google Tag Manager, event taxonomy, conversion tracking, and data quality. Use when building a tracking plan from scratch, auditing existing analytics for gaps or errors, debugging missing events, or setting up GTM. Trigger keywords: GA4 setup, Google Tag Manager, GTM, event tracking, analytics implementation, conversion tracking, tracking plan, event taxonomy, custom dimensions, UTM tracking, analytics audit, missing events, tracking broken. NOT for analyzing marketing campaign data — use campaign-analytics for that. NOT for BI dashboards — use product-analytics for in-product event analysis.

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Create a go-to-market strategy covering marketing channels, messaging, success metrics, and launch timeline. Use when planning a product launch, creating a GTM plan from scratch, or defining a launch strategy for a new market.

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Onboard a user to Wren Engine end-to-end. Walks through environment checks, project scaffolding, connection configuration via .env, and first query. Use when: user wants to install Wren Engine, set up a new data source connection, or bootstrap a new project from scratch. Triggers: '/wren-onboarding', 'install wren', 'set up wren engine', 'wren onboarding', 'connect new database to wren'.

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Generate a long-form Chinese interview-prep cheat sheet on a specific ML/LLM topic — formulas with derivations, from-scratch PyTorch code, comparison tables, and 25 高频面试题 (L1 必会 / L2 进阶 / L3 顶级 lab). Use when the user says '写面试 cheat sheet', '写一份 X 教程', '帮我准备 Y 面试题', '出一份 X 速查', or wants a 600-1000 line Chinese tutorial on a specific ML topic.

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Build an animation from scratch, making the decisions in the order that determines whether it feels right — should it animate at all, what purpose, which tool, which properties, which curve and duration, how it interrupts, how it exits. Writes the implementation. Use when asked to animate something, add motion, make a component feel alive, or build a transition. For critiquing existing motion use review-animations; for auditing a whole codebase use improve-animations.

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Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.

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Analyzes and generates llms.txt files -- the emerging standard for helping AI systems understand website structure and content. Can validate existing llms.txt files or generate new ones from scratch by crawling the site.

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Use when auditing existing Public Equity Investing models or spreadsheets. Do not use to build a new model from scratch.

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Map every stakeholder before engaging anyone. Use when launching an initiative, scoping discovery, or building an engagement plan from scratch.

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Expert guide for designing and building high-quality skills from scratch through structured conversation. Use when someone wants to create a new skill, build a skill, design a skill, or asks for help making Agents do something consistently. Also use when someone says "turn this into a skill", "I want to automate this workflow", "how do I teach my Agent to do X", or mentions creating SKILL.md files. Covers standalone skills and MCP-enhanced workflows. Do NOT use for creating subagents (use subagent-creator) or technical design documents (use create-technical-design-doc).

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Audit Claude Code and Codex hooks for validity, safety, and correctness across settings.json, hooks.json, config.toml, plugin, and skill or agent frontmatter hooks. Resolves each command, checks event names, handler types, matchers, exit-code and JSON decision strategy, and timeouts against each host's documented rules, then reviews hooks one by one. Use when setting up hooks, debugging a hook that never fires, never blocks, or hangs the agent, or doing a periodic hooks hygiene pass. Not for writing a new hook from scratch.

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Runtime-neutral Reddit account warm-up and brand promotion skill for AI agents. Systematically grow newly registered or existing Reddit accounts into credible identities, then organically plant brand content in target subreddits to eventually promote products or tools. Use for: register or take over a Reddit account and start the full warm-up cycle, build a credible Reddit account from scratch, run automated daily tasks on a schedule, camp on a post after publishing to reply, run multiple accounts sequentially. Triggers: reddit warmup / 30-day warmup / start warmup / build reddit karma / reddit brand promotion / reddit automated posting / today's task - reddit warmup <user> / Day N warmup / enter brand phase / refresh subreddit pool / camp <user> / stop camping / pause warmup / resume warmup / delete account / promote on reddit. EXECUTION RULE: before taking any action, load the reference files listed in the Phase Router section of this skill. Do not call browser-act, write any state file, or execute any step until the required reference files have been loaded.

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Plan complex Blazor UI features by decomposing them into focused components. USE FOR: building a complex Blazor page with multiple sections, planning component decomposition, designing a multi-section dashboard or layout, breaking down a large UI feature into composable components, pages with sidebars and content panels, any page with 3+ distinct visual sections or multiple interacting sub-features, identifying parent-child relationships and data flow. DO NOT USE FOR: creating new Blazor projects or apps from scratch (use create-blazor-project), implementing a single individual component (use author-component), writing component code with parameters and EventCallback (use author-component), or simple single-component pages.

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Add, configure, or review Ignite UI for Blazor Lite component support in Blazor applications. USE FOR: installing IgniteUI.Blazor.Lite or IgniteUI.Blazor.GridLite, registering AddIgniteUIBlazor() in Blazor Server, WASM, Hybrid, or split Blazor Web App projects, adding @using IgniteUI.Blazor.Controls, wiring the theme stylesheet, picking the right host page, locating the GridLite stylesheet path, explaining single-project vs split Server/Client Web App setup differences, and checking where an interactive render mode is needed for Ignite UI components to work. DO NOT USE FOR: general Blazor component authoring without Ignite UI, choosing app architecture or render mode from scratch (see create-blazor-project), JavaScript interop (see use-js-interop), authentication (see configure-auth), prerendering (see support-prerendering), or layout/component design questions that need no Ignite UI setup.

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Draft a new LinkedIn post from scratch using one of 20 2026 hook formulas (anaphora, R.I.P., time-anchor, curiosity-gap, contrarian, controlled A/B, false-binary, and more) plus a founders-edition angle library, picked by engagement goal (comments, reposts, likes, saves). Runs the humanizer pass and schedules via Publora on approval. Use to write a post, find a hook or proven format, or get founder-specific angles. Not for reviewing existing drafts (use linkedin-humanizer --mode audit).

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Repurpose existing content into a native LinkedIn post. Take a tweet, thread, YouTube video, blog, or newsletter and rebuild it for LinkedIn: re-hook before the fold, expand to the 900 to 1300 char sweet spot, add whitespace and a CTA, move links to the first comment, run the humanizer, publish via Publora on approval. Not for writing from scratch (use linkedin-post-writer), not for auditing a draft (use linkedin-humanizer --mode audit).

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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Generate a complete academic literature survey from scratch using multi-agent orchestration. Searches academic databases (OpenAlex, CrossRef, Unpaywall), downloads PDFs, builds BibTeX, drafts a LaTeX review, compiles it, reviews for quality, and revises based on feedback — all automated. Use this skill whenever the user asks to "generate a literature review", "write a survey paper", "review recent papers on [topic]", "create a survey of [field]", "综述", "文献综述", "survey papers in top journals", or wants a multi-agent pipeline to produce a publishable-quality literature review. Also trigger when the user wants to search multiple journals for papers on a topic and synthesize findings into a structured document. Works for any academic field but optimized for economics journals.

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Activate when the user is drafting any academic economics content from scratch (e.g., outlines, abstracts, introductions, data/methods/identification sections, results narratives, conclusions, referee responses, table/figure captions) and needs economics-specific structure, phrasing options, and quality checks to produce publication-ready prose.

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Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and event-study + coefficient + trend figures. Covers the full 8-step pipeline an applied economist or quantitative social scientist runs on every paper — (1) data cleaning, (2) variable construction & transformation, (3) descriptive statistics & Table 1, (4) statistical diagnostic tests, (5) baseline empirical modeling, (6) robustness battery, (7) further analysis (mechanism, heterogeneity, mediation, moderation), (8) publication-ready tables & figures. **Also covers two parallel domain modes that share the same 8-step scaffolding** — **Mode A — Epidemiology / public health** (target-trial emulation via `zepid` / hand-rolled `pandas`, IPTW + g-formula + TMLE doubly-robust triplet via `zepid` / `econml` / `lifelines`, Mendelian randomization via `pymr` / `mrtool` (or `rpy2` → `MendelianRandomization`/`TwoSampleMR`), KM / AFT / Cox survival via `lifelines`, E-value sensitivity, principal stratification — STROBE / TRIPOD reporting), and **Mode B — ML causal inference** (DML via `econml.dml` / `doubleml`, S/T/X/R/DR meta-learners via `econml.metalearners` / `causalml`, causal forest via `econml.grf` / `causalml`, Dragonnet / TARNet / CEVAE neural causal via `causalml`, BCF via `pymc-bart` / `bcf-py`, matrix completion, CATE distribution + policy tree via `econml.policy` / `policytree-py`, off-policy evaluation, conformal causal via `mapie`, fairness audit via `fairlearn`, DAG learning via `causal-learn` / `cdt` / LLM-assisted). Prescribes which library to reach for at each step, shows the canonical code, and links to deeper `references/` files for variant-specific patterns. Use when the user asks for a **complete empirical analysis** in Python, wants to replicate an applied-economics paper from scratch, needs a reproducible workflow that is NOT opinionated on any single vertical package (contrast with StatsPAI), wants explicit control over every estimator and diagnostic, or asks "how do I write a full empirical pipeline in Python?". Also triggers when the user names a specific classical step in isolation — "winsorize at 1/99%", "run Breusch-Pagan", "build a Table 1 balance table", "do a placebo test", "event study plot", "mediation analysis" — and wants it wired into the broader pipeline. Mode A triggers on "target trial emulation", "IPTW", "TMLE", "Mendelian randomization", "STROBE", "公共健康", "流行病学". Mode B triggers on "DML", "double machine learning", "causal forest", "meta-learner", "Dragonnet", "BCF", "policy tree", "conformal causal", "fairness audit", "因果机器学习".

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Use when starting any task the organization may already have an approved skill, prompt, MCP server, or Agent for: reviewing code, a commit, a diff, or a pull request; writing tests or documentation; querying a database, API, or service; automating a browser or web page; running untrusted code; connecting to a SaaS tool; drafting, researching, or any other substantive work. The user will not mention Observal: the task type is the trigger. Check what is already installed, then run observal discover search as the first action, before reading the repository or working from scratch. Also use when the user wants to log in, configure Observal, inspect local harness setup, share or open a repository Agent share, manage a teamspace or invitation, process inbox items, check installed registry items, or call an endpoint without a dedicated command.

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Scaffold, build, and package SwiftPM-based macOS apps without an Xcode project. Use when you need a from-scratch macOS app layout, SwiftPM targets/resources, a custom .app bundle assembly script, or signing/notarization/appcast steps outside Xcode.

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。