LLM Mart Basic

Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distribute

Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or

Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, genera

Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larg

Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or m

Bayesian modeling and probabilistic programming with PyMC 6 and ArviZ 1.x — hierarchical models, MCMC (NUTS via PyMC, nutpie, NumPyro, or BlackJAX), variational inference, PSIS-LOO model comparison, and prior/posterior predictive checks. Use when fitting Bayesian or hierarchical

Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling enginee

Scalable deep-learning training with PyTorch Lightning — organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, build data pipelines and callbacks, log to W&B or TensorBoard, and run distributed training (DDP, FSDP, DeepSpeed). Use when structuring PyT

Classical machine learning in Python with scikit-learn — algorithms, preprocessing, pipelines, and best-practice reference documentation. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model ev

Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index or Brier score, handli

Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fair

Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or log

Trains single-agent reinforcement learning agents with Stable-Baselines3 — PPO, SAC, DQN, TD3, DDPG, and A2C behind a scikit-learn-like API. Use for standard single-agent RL experiments, quick prototyping, well-documented algorithm implementations on Gymnasium environments, or ad

Guided statistical analysis with hypothesis-test selection, assumption checking, effect sizes, power analysis, and APA-formatted reporting using scipy.stats, statsmodels, and pingouin (Bayesian alternatives with PyMC). Use when choosing and running the appropriate statistical tes

Statistical modeling in Python with statsmodels — OLS/WLS/GLS, GLM, discrete-choice and count models, mixed models, ARIMA/SARIMAX/VAR, with diagnostics, robust standard errors, and coefficient-level inference. Use when fitting specific model classes for econometrics, time series,

Forecasts time series zero-shot with Google's TimesFM foundation models — TimesFM 2.5 (200M, Apache-2.0 weights; ForecastConfig API, XReg covariates) and TimesFM 3.0 (~330M, multivariate with native past/future covariates; non-commercial weights) — producing point forecasts and q

Graph Neural Networks with PyTorch Geometric (PyG) — node and graph classification, link prediction, GCN, GAT, and GraphSAGE layers, heterogeneous graphs, and molecular property prediction. Use when building or training GNNs for geometric deep learning on graph-structured data. P

Loads, runs, and fine-tunes pretrained models with Hugging Face Transformers v5 (PyTorch-only) — pipeline() inference for chat-model text generation, text classification, NER, zero-shot, speech recognition, image classification, object detection, and image-text-to-text VLMs; Auto

Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a

Out-of-core tabular analytics with Vaex — memory-mapped HDF5/Arrow/Parquet via vaex.open, lazy virtual columns, delayed single-pass aggregations on billion-row tables, binned histograms/heatmaps, and vaex.ml transformers on one machine. Vaex is in minimal-maintenance mode (vaex-c

Fourteen posts of being wrong in production, compressed to checkboxes

Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds

Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.

Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.

A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.

A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.

None of the safety came from the model. It came from six boring habits.

Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.

Six hours in, one step left, everything green, and the incident that didn't happen

CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.

Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it

An API request routing a model's tool call through an approval gate to a remote MCP server

31 config keys, two audits, and why the first one was wrong in both directions

The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.

Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.

Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.

"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks

stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.

The most important rule wasn't about what I could change. It was about what I was allowed to display.

Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.

"Tiered plugin quality review: branch (quick gates),

Check GitHub Discussions for highly voted learnings and promote them to Issues.

Evaluate Claude Code rules in .claude/rules/ directories for quality

Audit skill quality, frontmatter compliance, token efficiency, and activation reliability. Recommends improvements.

Test Claude Code skills using RED/GREEN/REFACTOR TDD phases in fresh subagents to prevent priming bias.

Validate hooks for security, performance, and SDK compliance

Validate plugin structure, schema, and naming. Use for plugin creation, debugging, or verification.

Initialize projects with architecture-aware templates using paradigm research and selection guidance for the target domain.

Generate an implementation plan with system architecture design and dependency-ordered task breakdown from a specification.

Guide project ideation through Socratic questioning to generate briefs with validated approaches and decision rationale.

Execute implementation plan systematically with progress tracking and checkpoint validation

Run full attune lifecycle as a mission with state detection and phase routing

Initialize a new project with git setup, CI/CD workflows, pre-commit hooks, Makefiles, and language-specific tooling.

Build a project skill library under .claude/skills/ as a resumable mission: discover, author in parallel, review adversarially.

Create specs from project briefs with acceptance criteria and testable requirements

Update existing project configurations to current best practices with selective component upgrades

Validate project structure and configurations against best practices with detailed issue reporting

Convene a multi-LLM expert panel to pressure-test strategic decisions with adversarial review and reversibility assessment.

Generate visual diagrams of codebase structure using Mermaid Chart MCP rendering.

Audit codebase for AI-generated code quality issues (vibe coding, Tab bloat, slop)

Your Personal AI Assistant; easy to install, deploy on your own machine or on the cloud; supports multiple chat apps with easily extensible capabilities.

Agent OS: the agent gets smarter on its own. We just hold the line: the grading command and expected result never make it into the success contract we hand it.…

Curated systems, benchmarks, and papers etc. on memory for LLMs/MLLMs --- long-term context, retrieval, and reasoning.

:memo: Vimlike Modal Text Editor in Rust

CI-native security testing for MCP servers. Attack simulation, schema drift detection, and health scoring before agents depend on them.

Hermes Agent memory plugin/provider for scope-aware recall, SQLite truth, LanceDB semantic search, and hybrid retrieval.

For You Agent——AI 时代的个人随身数字人格。把你的模型、AI 账号、技能、提示词和工作方式，带到每一个 AI 工具里。

A coding agent: give it a prompt and it reads, writes, runs commands, and searches code in a loop until the work is done, using native tool-calling across OpenA…

A secure persistent personal agent server in Rust. One binary, sandboxed execution, multi-provider LLMs, voice, memory, Telegram, WhatsApp, Discord, Teams, and…

Self-evolving agent: grows skill tree from 3.3K-line seed, achieving full system control with 6x less token consumption

DeepSeek Harness Desktop (dsh-desktop). EAC: Embracing All Creation (揽尽万象). Bundled Node.js runtime with full dsh-CLI kernel, one-click startup, 10 built-in UI…

See your agent think. Zero-config observability & governance for 26 AI agent runtimes: Claude Code, Cursor, OpenAI Codex, GitHub Copilot, Gemini CLI, Cline, Ope…

Save 94% on AI coding tokens. Index your codebase, agents search instead of reading files. Works with Claude Code, Codex, Copilot, Cursor, Gemini CLI. Local MCP…

Yet another coding agent harness, lightweight and written in go.

a coding Agent from pi. ∞ providers, sub-agents, hashline edits, and a permission gate

Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting…

🧠 Leon is your open-source personal assistant.

The Station, an open-world multi-agent environment that models a miniature scientific ecosystem.

The Frontend Stack for Agents & Generative UI. React, Angular, Mobile, Slack, and more. Makers of the AG-UI Protocol

VelaTerm = iTerm2 + Codex, The Best Terminal for AI Coding