LLM Mart Basic

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

Access the AlphaFold DB of 240M+ AI-PREDICTED protein structures (v6, plus precomputed homodimer/heterodimer complexes) — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computat

Search and retrieve preprints from arXiv via the Atom API by keywords, authors, arXiv IDs, date ranges, or subject categories. Use when finding or fetching papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical enginee

Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a p

Search the bioRxiv preprint server and retrieve paper metadata or download PDFs via its API. Use when finding life sciences preprints by keywords, authors, DOI, date ranges, or categories, or when conducting a biology literature review of not-yet-peer-reviewed work. Part of the A

Access the BRENDA enzyme database via its SOAP API to retrieve kinetic parameters (Km, kcat, Ki), reaction equations, organism data, and substrate-specific enzyme information indexed by EC number. Use when looking up enzyme kinetics, turnover numbers, or substrate specificity for

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.

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Get the Very Attacked People (VAP) report showing the most targeted users

Run the full billing-drift sweep for a billing period, formatted as a month-end reconciliation report

List upcoming contract and subscription renewals within a window, sorted by date

Run the license true-up reconciliation for one client or the whole portfolio

Search Freshdesk tickets with the Freshdesk query language — filter by status, priority, agent, group, type, tag, and date — and return a ranked, readable result list

Summarize a single Freshdesk ticket and its full conversation thread — the request, what has happened, current SLA state, and the recommended next action

Add an action (note, update, or response) to an existing HaloPSA ticket

Check contract status, service entitlements, and billing information for a client

Create a new service ticket in HaloPSA

Search the HaloPSA knowledge base for articles and solutions

Search for configuration items/assets by name, serial number, type, or client

Search for HaloPSA clients by name, domain, or other attributes

Search for tickets in HaloPSA by various criteria

Display comprehensive ticket information including history, actions, and related entities

View SLA status across tickets, including approaching breaches and at-risk tickets

Update fields on an existing HaloPSA ticket including status, priority, and assignment

Create a new deal in HubSpot with company association

Log a note or create a task on a HubSpot contact, company, or deal

Find a HubSpot company by name or domain and show associated contacts and deals

Summarize the HubSpot deal pipeline - deals per stage, total value, and expected close dates

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

Ultimate Multi-Agent OS for Autonomous AI NPCs 2026

Personal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

Slash API Batch: Cut AI Costs by 50% in 2026

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…