「bootstrap」の検索結果

Use when you need to create a new Maven-based Spring Boot 4.0.x project using SDKMAN-managed Java and Spring Boot CLI tooling. This should trigger for requests such as Create a Spring Boot Maven project; Bootstrap Spring Boot project with SDKMAN; Generate a new Spring Boot service; Create Spring Boot 4 Maven project; Scaffold Spring Boot service with Java 25. Part of Plinth Toolkit

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Use when building or reviewing Micronaut applications — Micronaut.run bootstrap, @Singleton/@Prototype, @Factory beans, @ConfigurationProperties, environments, @Requires, @Controller vs services, @Scheduled, graceful shutdown, @ExecuteOn for blocking work, and Jakarta-consistent APIs. This should trigger for requests such as Review Java code for Micronaut application structure and beans; Apply best practices for Micronaut configuration, @Requires, and factories; Improve scheduling, shutdown, or threading in Micronaut services. ; Review Micronaut dependency injection scopes and factories; Configure Micronaut @Requires conditions for environments. Part of Plinth Toolkit

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Use when you need to create a new Maven-based Quarkus 3.x project using SDKMAN-managed Java and Quarkus CLI tooling. This should trigger for requests such as Create a Quarkus Maven project; Bootstrap Quarkus project with SDKMAN; Generate a new Quarkus service; Create Quarkus 3 Maven project; Scaffold Quarkus service with Java 25. Part of Plinth Toolkit

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Operating SOP for DSPy (Stanford NLP) — the declarative framework for "programming, not prompting" language models. Activate when the user says any of: "use DSPy", "compile a prompt", "optimize prompts/programs", "MIPRO/MIPROv2", "BootstrapFewShot", "GEPA", "Signatures + Modules", "teleprompter", "auto-tune prompts for a different LM", or whenever a brittle hand-crafted prompt pipeline needs to be turned into a *compiled*, measurable, swappable program. Do NOT activate for one-shot prompt tweaks, no-metric exploratory work, or pipelines where prompts must remain human-authored verbatim — use raw prompting or LangChain templates instead.

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The compile-readiness gate for prompt auto-optimization. Decide whether you have earned the right to run an optimizer (DSPy MIPROv2 / GEPA / BootstrapFewShot) before spending compute. Two preconditions only — a real metric, and enough examples for the optimizer you picked. Garbage metric in, garbage prompt out. Pick the optimizer by data scale; GEPA inverts the scale assumption (~10 examples + textual feedback).

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Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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Operating-system distillation of LlamaIndex — the leading RAG / document-agent framework. Activate when the calling agent must build, debug, harden, or evaluate a Retrieval-Augmented Generation pipeline over unstructured/private data, decide between RAG primitives (Index types, retrievers, query engines, routers, agents), or pick LlamaIndex vs LangChain / Haystack / raw vector store for a coding task. Encodes the 5-layer mental model (Documents → Nodes → Indices → Retrievers → Query Engines / Response Synthesizers), the canonical RAG bootstrap SOP from baseline `VectorStoreIndex` through hybrid + reranker + eval-loop hardening, the official 13-failure-mode checklist, and 5 dilemma cases distilled from docs, GitHub issues, and 2025 production post-mortems.

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Install dependencies and build tools for WooCommerce iOS

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Creates and scaffolds a new Spring Boot project (3.x or 4.x) by downloading from Spring Initializr, generating package structure (DDD or Layered architecture), configuring JPA, SpringDoc OpenAPI, and Docker Compose services (PostgreSQL, Redis, MongoDB). Use when creating a new Java Spring Boot project from scratch, bootstrapping a microservice, or initializing a backend application.

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AI Agent Harness 设计模式 — 记忆、权限、上下文工程、委派、技能、 Hook、Bootstrap。中文版。

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Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.

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Owns project and build-level work for Compose and Compose Multiplatform apps: bootstrapping a new project, adopting the kit in an existing project, adding or extracting a module, convention plugins in build-logic, the version catalog, and CI plus agent hooks. Use when starting a Compose Multiplatform app, adding a module, or editing settings.gradle.kts, build.gradle.kts, build-logic, libs.versions.toml, or packaging. Do NOT use for routing and architecture choices (compose-architecture), feature code (compose-feature), repositories and persistence (compose-data), composables or resources (compose-ui), or commonMain vs platform code (compose-platform).

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Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.

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Use when starting or picking up a non-trivial effort (feature, investigation, perf probe, refactor, migration) in a shared repo where teammates or future agents must coordinate or hand off; when a repo has no durable in-tree record of decisions and dead-ends; when you're about to drop a well-measured negative result; or when bootstrapping a journal from a repo's commit history.

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Bootstrap a Full GRACE 4 project by creating the canonical .grace context, graph, verification, and changes skeleton.

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Onboarding conversation to generate a user profile.

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Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.

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Use whenever you need to understand how this theme works — adding pages, managing content, configuring the site, using partials/components/modules, project architecture, scripts, or styling/theming (Tailwind v4, Bootstrap 5, or a hybrid — verify which). Use for ANY question about the structure, usage, or customization of the template or theme.

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Indie maker playbook for bootstrapped apps inspired by Pieter Levels' MAKE: The Indie Maker Blueprint. Use when a user is starting or sizing an app idea, choosing what to build next, building a minimal product, preparing a launch, diagnosing stalled growth, deciding pricing or monetization, automating operations, maintaining a per-app MAKE.md tracker, or evaluating whether to sell a product.

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Run an honest market-fit and viability audit of any project or idea and produce a decision doc, not code. Use when someone asks "is this viable as a business", "audit the market fit", "make the case and compare to competitors", "should I apply to YC or bootstrap", or wants to validate a project, product, or market before investing more time. Gathers verifiable traction data first, researches the competitive field, gets an independent cold read, writes a doc with pre-committed pass/middle/kill criteria and a concrete week-1 assignment, then hardens it with an adversarial review loop.

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Scaffold and amend the L1 Investment Constitution — [ALL_CAPS] placeholder bootstrap, SemVer bump rules, Sync Impact Report, MINOR/MAJOR re-examination dispatch after the gate-5 budget confirm. Thesis creation stays refused until ratification (Q83).

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Think and work like an expert Mathematical Statistician. Use when a task calls for Mathematical Statistician judgment. Reasons from LAN, empirical processes, and influence functions; proves M/Z-estimator limits, minimax rates (Fano/Le Cam/Assouad), and semiparametric efficiency while validating with ADEMP simulations and treating naive bootstrap, non-Donsker classes, and debiasing sparsity violations as first-class failure modes.

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Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, generating bootstrap consensus, or needing quick exploratory phylogenies before running full ML analysis.

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Internal — for Boundless team members only. Help developers deploy Boundless AWS infrastructure to the dev environment. Use when the user wants to deploy a service to dev, bootstrap their dev environment, troubleshoot infrastructure issues, or understand what env vars a service needs. Also handles teardown and status checks.

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