每个推荐都保留与其仓库、审计和安装路径的明确关联。
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
A container platform that needs no Kubernetes learning, Build, deploy, assemble, and manage apps on Kubernetes, no K8s expertise needed, all in a graphical platform.
Small python-gtk application, which helps the user to merge or split PDF documents and rotate, crop and rearrange their pages using an interactive and intuitive graphical interface.
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Graphical Java application for managing BibTeX and BibLaTeX (.bib) databases
A graphical processor simulator and assembly editor for the RISC-V ISA
Static analyzer for C/C++ based on the theory of Abstract Interpretation.
A cross-agent research paper toolkit that transforms papers into learning environments with summaries, code demos, and a local web viewer for Claude Code, Codex, OpenCode, and DeepSeek Harness.
An agent skill that transforms AI assistants into expert economics paper writers by synthesizing best practices from over 50 authoritative guides.
KubeView is a Kubernetes cluster visualization tool that provides a graphical representation of your cluster's resources and their relationships
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.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
A container platform that needs no Kubernetes learning, Build, deploy, assemble, and manage apps on Kubernetes, no K8s expertise needed, all in a graphical platform.
Small python-gtk application, which helps the user to merge or split PDF documents and rotate, crop and rearrange their pages using an interactive and intuitive graphical interface.
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Graphical Java application for managing BibTeX and BibLaTeX (.bib) databases
A graphical processor simulator and assembly editor for the RISC-V ISA
Static analyzer for C/C++ based on the theory of Abstract Interpretation.
A cross-agent research paper toolkit that transforms papers into learning environments with summaries, code demos, and a local web viewer for Claude Code, Codex, OpenCode, and DeepSeek Harness.
An agent skill that transforms AI assistants into expert economics paper writers by synthesizing best practices from over 50 authoritative guides.
KubeView is a Kubernetes cluster visualization tool that provides a graphical representation of your cluster's resources and their relationships
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.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
A container platform that needs no Kubernetes learning, Build, deploy, assemble, and manage apps on Kubernetes, no K8s expertise needed, all in a graphical platform.
Small python-gtk application, which helps the user to merge or split PDF documents and rotate, crop and rearrange their pages using an interactive and intuitive graphical interface.
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Graphical Java application for managing BibTeX and BibLaTeX (.bib) databases
A graphical processor simulator and assembly editor for the RISC-V ISA
Static analyzer for C/C++ based on the theory of Abstract Interpretation.
A cross-agent research paper toolkit that transforms papers into learning environments with summaries, code demos, and a local web viewer for Claude Code, Codex, OpenCode, and DeepSeek Harness.
An agent skill that transforms AI assistants into expert economics paper writers by synthesizing best practices from over 50 authoritative guides.
KubeView is a Kubernetes cluster visualization tool that provides a graphical representation of your cluster's resources and their relationships
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.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.
每个推荐都保留与其仓库、审计和安装路径的明确关联。
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
A container platform that needs no Kubernetes learning, Build, deploy, assemble, and manage apps on Kubernetes, no K8s expertise needed, all in a graphical platform.
Small python-gtk application, which helps the user to merge or split PDF documents and rotate, crop and rearrange their pages using an interactive and intuitive graphical interface.
AI skill for OpenClaw & Claude Code — recommend from 10000+ Nano Banana Pro (Gemini) image prompts. Smart search by use case, content remix, sample images.
Graphical Java application for managing BibTeX and BibLaTeX (.bib) databases
A graphical processor simulator and assembly editor for the RISC-V ISA
Static analyzer for C/C++ based on the theory of Abstract Interpretation.
A cross-agent research paper toolkit that transforms papers into learning environments with summaries, code demos, and a local web viewer for Claude Code, Codex, OpenCode, and DeepSeek Harness.
An agent skill that transforms AI assistants into expert economics paper writers by synthesizing best practices from over 50 authoritative guides.
KubeView is a Kubernetes cluster visualization tool that provides a graphical representation of your cluster's resources and their relationships
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.
Generate highly simplified personified IP mascot logos with Flat-first geometry, rounded heavy forms, two IP colors plus one background color by default, and extremely subtle neo-skeuomorphic shading. Use when creating an animal, creature, robot, ghost, plant, object, or other character as a minimal square logo or app-icon artwork, including when the agent should infer three distinct IP directions from product-repository context.