Results for “loom”

Guided first-run that produces a real verified win in under five minutes using the skill library on a seeded offline fixture. Load when a new user asks how to start, run the demo, try agent-loom, or get a quick win. Also triggers on "quickstart", "first run", "demo agent-loom", "try the skills", or onboarding to the library. Zero external credentials required. Idempotent — safe to run multiple times.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Manages a long-term Markdown knowledge base for software projects, capturing architecture, decisions, and conventions in `.lore/` with commands to init, sync, query, audit, compress, and mirror to platform files.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Loki is a horizontally scalable log aggregation system built by Grafana Labs. Unlike traditional log platforms that index the full text of every log line, Loki indexes only metadata labels, making it significantly cheaper to operate. It integrates natively with Grafana for querying and visualization, and uses LogQL — a query language inspired by PromQL — to filter, parse, and aggregate log streams.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.

Route active project/repo memory requests into one honest packet: memory-layer choice, load-context, search-context, store-conclusions, setup-integration, or repo-packer route-out. Use when agents need searchable decisions, manifests, stable links, handoff notes, and small “read this first” packets across sessions. Route long-lived markdown knowledge bases to `llm-wiki`, structural graph memory to `graphify`, human-authored vault organization to note/vault skills, and one-shot repo packing to tools like Repomix, Gitingest, or Code2Prompt.

Master Java 21+ with modern features like virtual threads, pattern matching, and Spring Boot 3.x. Expert in the latest Java ecosystem including GraalVM, Project Loom, and cloud-native patterns.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

Render long lists (1k+ rows) efficiently in Salesforce LWC using lightning-datatable infinite loading or an IntersectionObserver sentinel, with keyset pagination and accessibility guidance.

Turns a rough request into working software via a governed build loop: align first, plan, then iterate one task at a time with deterministic backpressure and holdout-scenario judging.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

LoRA: Low-Rank Adaptation of Large Language Models

Use when planning a COLM submission campaign across the calendar — working backward from the late-March abstract and paper deadlines through the May-June rebuttal, July decisions, August camera-ready, and October conference, coordinating co-authors, compute, and reciprocal-reviewing duties, and slotting COLM into a multi-venue LM-research pipeline.

Creates and maintains persistent markdown planning files that survive session resets by hooking into Claude Code's lifecycle.

Project Lombok for reducing Java boilerplate. Covers annotations for getters, setters, constructors, builders, logging, and more. Based on production patterns from castellino and gestionale-presenze projects. USE WHEN: user mentions "lombok", "@Data", "@Builder", "@Slf4j", asks about "boilerplate reduction", "getters/setters", "@RequiredArgsConstructor", "@Value" DO NOT USE FOR: Java language features - use `java` skill instead DO NOT USE FOR: MapStruct integration - use `mapstruct` skill DO NOT USE FOR: Spring annotations - use `backend-spring-boot` skill

Governs unattended automation workflows with mandatory budgets, dry-run defaults, and a separate verification gate before any side effect.

Discover, audit, repair, adapt, and design bounded, verifiable AI-agent loops with explicit triggers, actions, stopping conditions, and guardrails.

Executes Python code iteratively via an MCP bridge to produce verified results for calculations, data analysis, and task decomposition.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.

LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.