Directorio de skills
Cada recomendación conserva un vínculo claro con su repositorio, auditoría y ruta de instalación.
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. Looking for p5.js 2.0? http://beta.p5js.org
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
28 eval-informed mental models and critical-thinking skills for Claude Code, GitHub Copilot, Codex, Cursor, and other Agent Skills-compatible tools
Build consistent, themeable React apps based on constraint-based design principles
job-ops: DevOps principles applied to job hunting. A self-hosted pipeline to track, analyze, and assist your application process
A reusable skill kit for AI agents to generate structurally precise and aesthetically standardized draw.io diagrams across major cloud platforms and BPMN, with declarative layout, stencils, and validation.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Parseable is an observability datalake built from first principles.
A principles-first workflow for AI coding agents providing reusable phases and end-to-end workflows for software development tasks.
A meta-skill that creates, evaluates, and improves other AI agent skills with multiple modes and evidence-based validation.
A collection of agent skills that inject team-specific context into coding agents at session start, improving collaboration and adherence to conventions.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Directorio de skills
Cada recomendación conserva un vínculo claro con su repositorio, auditoría y ruta de instalación.
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. Looking for p5.js 2.0? http://beta.p5js.org
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
28 eval-informed mental models and critical-thinking skills for Claude Code, GitHub Copilot, Codex, Cursor, and other Agent Skills-compatible tools
Build consistent, themeable React apps based on constraint-based design principles
job-ops: DevOps principles applied to job hunting. A self-hosted pipeline to track, analyze, and assist your application process
A reusable skill kit for AI agents to generate structurally precise and aesthetically standardized draw.io diagrams across major cloud platforms and BPMN, with declarative layout, stencils, and validation.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Parseable is an observability datalake built from first principles.
A principles-first workflow for AI coding agents providing reusable phases and end-to-end workflows for software development tasks.
A meta-skill that creates, evaluates, and improves other AI agent skills with multiple modes and evidence-based validation.
A collection of agent skills that inject team-specific context into coding agents at session start, improving collaboration and adherence to conventions.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Directorio de skills
Cada recomendación conserva un vínculo claro con su repositorio, auditoría y ruta de instalación.
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. Looking for p5.js 2.0? http://beta.p5js.org
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
28 eval-informed mental models and critical-thinking skills for Claude Code, GitHub Copilot, Codex, Cursor, and other Agent Skills-compatible tools
Build consistent, themeable React apps based on constraint-based design principles
job-ops: DevOps principles applied to job hunting. A self-hosted pipeline to track, analyze, and assist your application process
A reusable skill kit for AI agents to generate structurally precise and aesthetically standardized draw.io diagrams across major cloud platforms and BPMN, with declarative layout, stencils, and validation.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Parseable is an observability datalake built from first principles.
A principles-first workflow for AI coding agents providing reusable phases and end-to-end workflows for software development tasks.
A meta-skill that creates, evaluates, and improves other AI agent skills with multiple modes and evidence-based validation.
A collection of agent skills that inject team-specific context into coding agents at session start, improving collaboration and adherence to conventions.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
Directorio de skills
Cada recomendación conserva un vínculo claro con su repositorio, auditoría y ruta de instalación.
p5.js is a client-side JS platform that empowers artists, designers, students, and anyone to learn to code and express themselves creatively on the web. It is based on the core principles of Processing. Looking for p5.js 2.0? http://beta.p5js.org
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
28 eval-informed mental models and critical-thinking skills for Claude Code, GitHub Copilot, Codex, Cursor, and other Agent Skills-compatible tools
Build consistent, themeable React apps based on constraint-based design principles
job-ops: DevOps principles applied to job hunting. A self-hosted pipeline to track, analyze, and assist your application process
A reusable skill kit for AI agents to generate structurally precise and aesthetically standardized draw.io diagrams across major cloud platforms and BPMN, with declarative layout, stencils, and validation.
What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?
Parseable is an observability datalake built from first principles.
A principles-first workflow for AI coding agents providing reusable phases and end-to-end workflows for software development tasks.
A meta-skill that creates, evaluates, and improves other AI agent skills with multiple modes and evidence-based validation.
A collection of agent skills that inject team-specific context into coding agents at session start, improving collaboration and adherence to conventions.
Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.