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Deploying Hello World to Fly.io

flyio-hello-world

jeremylongshore/claude-code-plugins-plus-skills

This guide demonstrates how to deploy a minimal "Hello World" application to the Fly.io platform. It covers two primary deployment methods: using the simple `flyctl` Command Line Interface (CLI) for quick setup, and utilizing the advanced Machines API for programmatic deployment. This skill is ideal for beginners starting a new Fly.io project, or for implementing robust, globally distributed edge compute infrastructure.

GitHub Issue Triage Workflow Management

mattpocock/skills

Manages the complete lifecycle of GitHub issues using a structured, label-based state machine. This skill assists maintainers in triaging incoming reports, determining if an issue is a bug or enhancement, and moving it through defined states (e.g., needs info, ready for agent, wontfix). It ensures systematic workflow management for development projects.

Securing SalesLoft API Integrations

salesloft-security-basics

jeremylongshore/claude-code-plugins-plus-skills

This comprehensive guide outlines best practices for securing SalesLoft API access and integrations. It details secure token lifecycle management (OAuth refresh), implementing robust webhook signature verification using HMAC-SHA256 to prevent replay attacks, and enforcing scope minimization for least-privilege access. Use this when designing production-grade, secure enterprise integrations.

Optimizing AI Context Window FinOps

geminiignore-finops

sickn33/antigravity-awesome-skills

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

Editorial Minimalist Web Prototype

web-proto-editorial

nexu-io/html-anything

This template provides an editorial-minimalist web layout, inspired by high-end magazines. It features a warm monochrome palette, classic serif displays, and clean grotesque body text, emphasizing macro-whitespace and subtle ambient micro-animations. It is perfect for portfolio sites, long-form articles, or any content-heavy design needing a sophisticated, literary feel.

Packaging Scientific Evidence for AI/Stats

aistats-artifact-evaluation

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing how to package all necessary supplementary evidence for academic submissions in AI, statistics, and machine learning domains. It covers best practices for ensuring reproducibility, including managing proofs, simulation scripts, benchmark data, random seeds, and minimizing metadata risks for reviewers.

Assessing Manuscript Fit For Ecology Letters

ecology-letters

brycewang-stanford/Awesome-Journal-Skills

This skill guides authors on determining the suitability of their ecology manuscript for *Ecology Letters*. It assesses conceptual ambition, methodological rigor, scope fit (e.g., macroecology, theory-data integration), and adherence to the journal's unique short-form structure and high standards. Use it before submission to ensure the paper meets the journal's requirement for a single, clear, general principle.

European ML Conference Submission Guide

european-conference-on-machine-learning-and-principles-and-practice-of-knowledge-discovery

brycewang-stanford/Awesome-Journal-Skills

This skill provides a strategic guide for authors evaluating if their AI/data mining manuscript is a suitable fit for the ECML PKDD conference. It covers scope definition, evidence requirements, academic framing, and strategic positioning relative to major global venues (e.g., ICML, NeurIPS). Use this to refine your submission narrative and maximize your chances of acceptance.

Guiding ML Theory Venue Selection

colt-topic-selection

brycewang-stanford/Awesome-Journal-Skills

This guide assists researchers in determining the optimal venue for their theoretical machine learning contributions. It provides a framework for evaluating whether a result is best suited for COLT, or if it belongs in other top-tier conferences (e.g., NeurIPS, ICML) or journals (e.g., JMLR). It emphasizes differentiating between rigorous mathematical proofs (bounds, separations) and purely empirical findings.

Guidelines for Machine Learning Artifact Submission

icml-artifact-evaluation

brycewang-stanford/Awesome-Journal-Skills

Provides comprehensive guidelines for packaging supplementary materials, including code, data, model weights, and benchmarks, for major machine learning conferences like ICML. It details the requirements for both the double-blind review stage (focusing on anonymization and minimal replication commands) and the final public release package (ensuring permanent public links and clear licensing).

Assessing Manuscript Fit and Line Positioning

jfr-fit-positioning

brycewang-stanford/Awesome-Journal-Skills

This guide helps researchers evaluate the core mechanism of their academic paper, determining whether the focus is fundamentally on financial mechanisms. It provides criteria for positioning the work along the macro-finance or micro-corporate finance line, and offers advice on suggesting alternative journals if the topic drifts into general industrial, accounting, or management fields.

Assessing International Financial Paper Scope

jimf-topic-selection

brycewang-stanford/Awesome-Journal-Skills

A guide for researchers on determining if their macro or finance paper has a load-bearing international or open-economy dimension. It provides specific tests and reframing techniques to ensure the core contribution is cross-border, preventing misrouted submissions to top academic journals.

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