LLM Mart Basic

Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 datase

Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 3 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstruct

Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inve

Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially express

Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple

Read and write genomic alignment and variant files in Python with pysam (htslib bindings) — SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, plus region extraction and per-base coverage/pileup. Use when scripting NGS data-processing pipelines that parse, filt

Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux q

Generate de-novo protein backbones with RFdiffusion (Watson 2023) — a diffusion model for unconditional monomer generation, motif scaffolding, binder design against a target, and symmetric oligomers. Use when generating a new protein backbone from scratch, scaffolding a functiona

Quantifies bulk RNA-seq transcript abundance with salmon 2.x (the Rust rewrite; selective alignment or --sketch) and kallisto (v0.52.0, kb-python workflow), builds a decoy-aware gentrome index, runs quant with --gcBias -l A, then imports estimates via tximport/tximeta with a tx2g

Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data

Apply the scGPT single-cell foundation model (Cui 2024) to annotate and embed cells — zero-shot and fine-tuned cell-type annotation, gene/cell embeddings, batch integration, and gene-regulatory / perturbation inference from AnnData. Use when annotating cell types with a pretraine

Run RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to trajectories or studying differe

Train deep generative models for single-cell omics with scvi-tools — probabilistic batch correction and integration (scVI), reference-mapping transfer learning (scArches), differential expression with uncertainty, and multimodal models (totalVI for CITE-seq, MultiVI for multiome)

Analyzes spatial transcriptomics with squidpy (1.8.x) on AnnData and SpatialData objects, routing platforms correctly: Visium spots use spatial_neighbors(coord_type='grid') and pair with deconvolution, while Xenium/MERFISH single-cell data use coord_type='generic'/Delaunay neighb

Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets that are too large for

Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel

Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use when running end-to-end

Predicts protein-ligand binding poses with DiffDock diffusion-based molecular docking from PDB structures and SMILES, producing pose confidence scores for virtual screening and structure-based drug design. Use when docking ligands into a protein, generating binding poses, or scre

Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or lib

Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PA

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.

SonarQube 정적분석 실행 및 결과 조회. 코드 품질·보안 핫스팟·커버리지 확인 시 사용.

GitHub 이슈 #$ARGUMENTS 를 처리한다(이슈 우선 워크플로):

이슈/기획서 기준 SDLC 한 사이클(이슈→개발→테스트→검증→PR)을 사람 개입 없이 자동 실행.

GitLab 이슈 #$ARGUMENTS 를 처리한다(이슈 우선 워크플로):

이슈/기획서 기준 SDLC 한 사이클(이슈→개발→테스트→검증→MR)을 사람 개입 없이 자동 실행.

Invoke a repeated task with `/name`. File name = command name (`fix-issue.md` → `/fix-issue`).

Handle issue #$ARGUMENTS (issue-first workflow):

Renders the connection structure of the AGENTS.md ecosystem

Automatically runs one SDLC cycle (issue → development → testing → verification → PR/MR) based on an issue or spec, without human intervention.

Run SonarQube static analysis and check the results. Use to check code quality, security hotspots, or coverage.

Handle GitHub issue #$ARGUMENTS (issue-first workflow):

Automatically runs one SDLC cycle (issue → development → testing → verification → PR) based on an issue or spec, without human intervention.

Handle GitLab issue #$ARGUMENTS (issue-first workflow):

Automatically runs one SDLC cycle (issue → development → testing → verification → MR) based on an issue or spec, without human intervention.

Show Hermes planning-with-files status for the current project.

Start Hermes planning-with-files workflow in the current project.

بدء تخطيط الملفات بنمط Manus. إنشاء task_plan.md و findings.md و progress.md للمهام المعقدة.

Lock the current task_plan.md content with a SHA-256 attestation. Hooks then refuse to inject plan content if the file diverges from the attested hash, blocking silent tampering. Use --show to print the stored hash, --clear to remove the attestation. Available since v2.37.0.

Starte Manus-artige Dateiplanung. Erstelle task_plan.md, findings.md, progress.md für komplexe Aufgaben.

Self-check for the planning-with-files mechanisms that fail silently: plan resolution, hook injection, canonicalizer path shape, attestation state, install surfaces, and per-fire hook latency. Run it whenever hooks seem quiet or after installing on a new machine. Available since v3.6.0.

Paper trading simulator for Polymarket — built for AI agents. MCP server, live order books, strategy backtesting. Install: npx clawhub install polymarket-paper-…

An SSH tool dedicated to addressing all pain points · (macOS/Windows/Linux/Android/iOS)

The first AI agent harness native to the browser. A browser extension that runs a full agent loop where you already work: it drives your tabs, spins up sandboxe…

SmartLabel AI 2026: Auto-Annotate Any Object via LLM-Powered Prompt Parsing

Local-first visual generation runtime and studio for people and coding agents, with reproducible image and video workflows across multiple providers.

Scholar All-In-One: A research infrastructure for AI agents

Give your AI agent a real browser — with a human in the loop. Open-source MCP-native browser agent.

同花顺免费开源AI插件FQGate-agent（原插件名 tonghuasun-agent）：为 Codex、Claude Code、DeepSeek 等 AI 工具提供本机 A 股实时行情、K 线、Level-2、资讯、账户查询与可选交易能力。

A non-linear AI agent workbench — session is a tree: branch anytime, and context follows the branch

Production-grade AutoCAD MCP server for AI agents — 122 tools, dual COM (live AutoCAD) + headless ezdxf engines, ISO GD&T and dimension-tolerance validation for…

A DeepSeek coding agent harness: persistent shell, Ultra subagents, auto approval, Chrome MCP and session telemetry

Waku Waku! Waku Agent is a local-first AI agent harness you actually own, including loop, memory, eval, all in code built to stay legible as it grows.

Remote control for Claude Code, Codex, Grok, and Cursor on Mac or PC. View sessions, send tasks, and drive them remotely from your phone, PIN, or Ring. OpenClaw…

Next-gen AI-native server ops panel with an in-workspace SRE agent. Crash self-healing, safe rollbacks, Docker/game servers, and local Ollama support.

Professional cloud architecture diagrams with official AWS, Azure and GCP icons, from Terraform code or a plain JSON graph. MCP server + agent skill.

A ready-to-use self-growing desktop AI assistant for long-running tasks, memory, agents, tool reviews, MCP, and high-concurrency workspace automation. / 开箱即用的自我…

Collection of templates using the Alchemyst AI Platform for your next big AI app.

Fast, exact LLM decoding on Apple Silicon (MLX) behind an OpenAI-compatible endpoint

The open source AI research agent.

Open-source agent-native visual production workspace where humans and coding agents edit the same live canvas — local-first, BYOK image/video models.